Maintworld Magazine 4/2021

4/2021 www.maintworld.com 

maintenance & asset management 

What led to 

this condition? p 22 

CAN WE BUILD SANDCASTLES WITH LOOSE SAND? PG 20 AI FACTORY FOR RAILWAY OPERATION & MAINTENANCE PG 32 MAINTENANCE PEOPLE, GET CERTIFIED! PG 44

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EDITORIAL 

To Repair or 

not to Repair... 

WITHOUT any doubt you have already tried to 

repair a household appliance and/or an electronic 

device. And let me guess, it proved not 

to be an easy task. Sometimes spare parts and 

technical information are not available. Or you 

simply do not succeed to open up the appliance 

without causing damage. Throwing it away and 

replacing it is then the only solution. 

UNACCEPTABLE 

In current times of climate change and environmental awareness, people no 

longer accept that things cannot be repaired. Many voices call for a shift from a 

throw-away society to a more sustainable model. The possibility to repair things 

that break plays a major role in this shift. Legislators around the world seem to 

have understood the importance of repair. 

THE RIGHT TO REPAIR 

In July 2021 new legislation has come into force in the UK requiring manufacturers 

of white goods and TVs to make repair information and spare parts available 

for ten years. In the US, Congressman Joe Morelle introduced a bill in June that 

goes even further. In the ‘Fair Repair Act’, both consumers and businesses are given 

the fundamental right to carry out repairs on their own equipment. Equipment 

manufacturers will be obliged to make available all diagnostic and repair information, 

parts, and tools in a timely manner, and on fair and reasonable terms. 

In Europe, manufacturers already have to comply with ecodesign directives, 

and are obliged to provide spare parts for 10 years. The European Parliament is 

also currently considering the introduction of a mandatory repair score. Italy and 

France will soon be introducing legislation banning the artificial ageing of consumer 

products to extend product lifetime. 

REPAIRABILITY BECOMES VISIBLE 

France was already pioneering by introducing the ‘repairability index’ for consumer 

electronics at the beginning of this year. The easier products are to disassemble, 

and the more readily available spare parts and technical information are, 

the higher the repair score. Also, the price of the spare parts is considered, next 

to some other product-specific criteria, for instance, the number of disassembly 

steps. Researchers are currently investigating the impact of the repairability index 

both on consumer behaviour, and on product design allowing better repairability. 

GAME CHANGER 

One thing is clear, the maintenance and asset management community should 

embrace the current uprise in awareness of the importance of repair. We will not 

only benefit from legislation that is also applicable on industrial assets, but having 

consumers thinking about repair might inspire young people to also take up a job 

in maintenance in repair. Let’s surf the waves of the right to repair movement! 

Wim Vancauwenberghe 

Maintenance Evangelist 

Member of EFNMS ESHEC (European Health Safety and Environment Committee) 

4 maintworld 4/2021 

6 

The 

SPS trade show aims 

at “bringing automation to 

life” and “covers the entire 

spectrum of smart and digital 

automation” technology topics.

IN THIS ISSUE 4/2021 

36 

Corporate 

leadership should 

support building the Process 

Guide by including plant 

leadership experts. 

= 

38 

Maintenance 

cost is an 

important indicator for 

a plant’s performance. 

6 

10 

14 

16 

ICONICS, World Leading Automation 

Software Provider & Trusted Partner 

in Digital Transformation, to Exhibit 

and Present at SPS 

Digitalisation of production systems: 

The right interfaces for late adopters 

What is the big deal about accredited 

certification? 

Avoiding Unplanned Downtime: 

Online Monitor of Critical Bearings 

20 

22 

24 

26 

32 

Can we build sandcastles 

with loose sand? 

What led to this condition? 

OPC UA, MQTT, and Information 

Interoperability 

Risk Based Inspection 

AI Factory for Operation 

& Maintenance 

36 

38 

Strategic Success for Shutdowns – 

The Importance of C-Suite Support 

Benchmarking maintenance cost 

in a vacuum 

42 

3 Ways in Which Manufacturers Can 

Manage Their MRO Inventory 

44 

48 

Maintenance people, get certified! 

Integrating information for 

evidence-based decision making 

Issued by Promaint (Finnish Maintenance Society), Messuaukio 1, 00520 Helsinki, Finland tel. +358 29 007 4570 Publisher Omnipress Oy, 

Väritehtaankatu 8, 4. kerros, 01300 Vantaa, tel. +358 20 6100, www.omnipress.fi Editor-in-chief Nina Garlo-Melkas tel. +358 50 36 46 491, 

nina.garlo@media.fi, Advertisements Kai Portman, Sales Director, tel. +358 358 44 763 2573, ads@maintworld.com Layout Menu Meedia, 

www.menuk.ee Subscriptions and Change of Address members toimisto@kunnossapito.fi, non-members tilaajapalvelu@media.fi 

Printed by Reusner, www.reusner.ee Frequency 4 issues per year, ISSN L 1798-7024, ISSN 1798-7024 (print), ISSN 1799-8670 (online). 

4/2021 maintworld 5

PARTNER ARTICLE 

Text and images: ICONICS, SHUTTERSTOCK 

ICONICS, World Leading Automation 

Software Provider & Trusted Partner in Digital 

Transformation, to Exhibit and Present at SPS 

The Smart Production Solutions (SPS) Trade Show will happen in Nuremberg, Germany 

on November 23 – 25. And as a world leading automation software provider 

and trusted partner in digital transformation, ICONICS will exhibit and present at SPS. 

This will be our first major international in-person trade show since COVID, and we 

are thrilled at the opportunity to see our customers face-to-face again and to help 

them solve their operational challenges and meet their corporate sustainability goals. 

THE SPS trade show aims at “bringing automation to life” and 

“covers the entire spectrum of smart and digital automation” 

technology topics. It is also an excellent chance to exchange and 

discuss ideas on how to address operational and energy challenges 

your company may be facing and spark the imagination for 

connected factories, connected infrastructures, and a connected 

front on sustainability initiatives. 

ICONICS develops software to automate the monitoring and 

management of manufacturing and industrial processes. We do 

this to help companies reduce their operating costs, be more 

efficient, detect faults, improve overall equipment effectiveness, 

reduce response times, and so on. The common thread across all 

these applications is to focus on specific, measurable factors, and 

then connect to the devices, equipment, and environments to 

collect the telemetry data. Once all of the data sources are connected, 

the trick is to display it in ways that are meaningful to 

operators, managers, engineers, and executives. It doesn’t matter 

what industry you are in or what type of community you belong to; 

we have the tools to display the data to give you real-time information 

and real-time wisdom. With this information, you can then 

diagnose what’s wrong, what’s happened, what’s inefficient, and 

then what corrective measures to take. 

The software at work in each of those cases is known 

as the ICONICS Suite, and it is a combination of four 

elements: GENESIS64, Hyper Historian, AnalytiX®, and 

IoTWorX. There is a unique and singular platform service 

connecting all of those elements together; not just for the 

industry today in its current applications, but also for the 

industry as it looks forward to future-proofing the elements 

of its systems. And ICONICS Suite is a unified platform 

that is competitive, flexible, scalable, and modular, as well 

as customizable and extensible. In essence, ICONICS is the 

“Switzerland of Automation” with the ability to connect to 

virtually any equipment or device. 

A key to realizing success in these automation projects 

is to start with one specific challenge and then scale up and 

out. Start economically and then use that as a baseline to 

solve whatever problem you encounter next. And as you 

probably know, there is always something else to uncover 

once you get started. 

Fortunately for the attendees, besides exhibiting at SPS, 

ICONICS will also present. Business Development Manager 

Sebastian Hohenhoff from our German team will talk on 

Tuesday, 23 November from 14:40 to 15:00 about “How 

6 maintworld 4/2021


Shopfloor Self-Service Dashboards Improve the Overall 

Visibility of Your Production Lines”. Here is a brief glimpse 

into what Sebastian will cover in his presentation. 

Many manufacturers face the challenge of gathering all relevant 

information from different data sources from the shopfloor. 

Different vendors provide different systems; some include local 

analytics functions while others provide just raw data, if even. 

This scattering of information often makes it difficult to provide a 

cohesive view of a system and, even in cases where all the data can 

be visualized together, it might not be shaped logically for the end 

user. Correlating these different datasets with each other is tricky, 

and trying to query them using a common set of filters or parameters 

can be quite difficult. 

To get an overall visibility into the manufacturing process, 

it is essential to have a solution which combines all relevant data 

from all the various sources into one visualization and analytics 

system, no matter who the machine vendor is or what PLC is used. 

With ICONICS, the real-time data from the production floor can 

be collected via open protocols like OPC UA, Modbus, CC-Link, 

and more. Even the use of native and proprietary protocols to 

connect to nearly all PLC vendors is possible by utilizing the 

Takebishi DeviceXPlorer, which integrates perfectly with 

ICONICS’ GENESIS64. 

Sebastian will also explain how in order to make visualization 

meaningful, just showing real-time data is not enough. 

It’s important to have a business intelligence (BI) data model 

and information flow engine like ICONICS AnalytiX-BI in the 

background. This engine can do calculations with real-time 

values, contextualize, and augment these with a variety of meta 

data. That meta data can come from nearly any IT source like 

relational databases, Kafka, MQTT, web services and more, or 

directly from ERP, MES, CRM, or other relevant 3rd party 

systems already in place. ICONICS bridges the existing data 

silos, gathers and normalizes all that data with user-defined 

data models, represents collections of datasets that are logically 

related to each other, irrespective of their physical origin, 

and allows multi-step transformations of the ingested data for 

better shaping and filtering. Striking the right balance between 

all of those requirements may seem daunting at first, but by 

working with the right solution provider, it is readily achievable 

and worthwhile, as it makes the end result even more 

powerful. 

The outcome of the AnalytiX-BI data model can be visualized 

on any smart device, depending on customer preference. 

A thick-client installation on a desktop computer can be used or 

the flexibility of the HTML5-compatible dashboards can be leveraged 

with every current web browser. The dashboards can show up 

on large TV screens, tiny smartwatch displays, mobile phones, tablets, 

or wearables, responsively adjusting in size to provide the best 

possible user experience. The latest ICONICS’ release 10.97.1 even 

supports advanced 3D graphics on any modern web browser without 

requiring any add-ons, extensions, or plug-ins to be installed. 

These more advanced dashboards and visualization features 

can be created with ICONICS’ very own GraphWorX64 tool. 

But what about the plant manager who is interested in a KPI dashboard 

with a focus on Overall Equipment Effectiveness (OEE) 

for their entire plant? To address this need, ICONICS has made 

self-service dashboards available with KPIWorX. Self-service 



means that anyone with access to the visualization system can 

easily create dashboards directly from within the browser via 

a drag-and-drop feature; no additional software is required. By 

using standardized symbol libraries and preconfigured components 

(gauges, process points, trends, alarms, grids and more), 

a fully functioning dashboard can be created within a matter of 

minutes to show the latest real-time and plant-related KPIs. 

Additionally, in today’s environment, security of IT and OT 

systems is essential. No unauthorized party should be able to read 

production data or, even worse, write back malicious or false 

data into the system. To prevent cybersecurity related breaches, 

GENESIS64 utilizes multiple layers of security. First, to set up, 

every installation requires a user with a password to avoid the 

danger of completely open systems. To make user administration 

easier, the system fully integrates with Microsoft’s Active Directory 

for on-premises installations and Azure Active Directory 

for cloud deployments. We also strongly encourage the use of 

multi-factor authentication, which is supported within ICONICS 

applications via OIDC / OAuth2. To secure the domains, SAML 2.0 

is supported. To protect the system itself, ICONICS uses binary 

signing along with a strong integrity check. VeriSign has signed the 

binaries to ensure these have not been tampered with or changed 

without authentication. Furthermore, the binaries are obfuscated, 

preventing reverse engineering. 

Finally, Sebastian will provide a demonstration of our 

ICONICS software, so attendees can see firsthand how their 

organizations can benefit from our system automation platform. 

If you can’t make the presentation, come by our booth 

to talk to our team. You can find the ICONICS booth in Hall 

5-159, in close proximity to our technology partners like 

Microsoft, the OPC Foundation, and Takebishi. 

Providing Industry Solutions for Today 

and for the Future 

Advances in technology, no matter the application, occur 

at an incredibly fast pace. To stay competitive, it’s imperative 

to stay up to date. But there is an upside to this – it’s 

fun! And going to trade shows like SPS is not only an amazing 

experience, it is educational at the same time. If you 

are planning on attending, stop by for a chat. We’ll get you 

up to speed on our latest automation software and digital 

transformation technology and get you and your company 

on the road to operational efficiency and sustainability. 

ICONICS provides industry solutions for today and for the 

future. It’s what we do. 

Want to know more about how the ICONICS Suite can 

help your company with its system automation, digital 

technology transformation, and sustainability initiatives? 

Watch at your convenience an array of informative, interesting, 

and relevant sessions from our ICONICS Connect 2021 

event at iconics.com/Connect2021 



Digitalisation of production systems: 

The right interfaces for late adopters 

The fact that digitalisation of production systems is progressing has become an 

established consensus among manufacturers, operators, and service providers 

in the mechanical engineering sector. At the same time however, statements 

such as the following from T-Systems often cause uncertainty among OEMs in 

the mechanical engineering sector: “Digitization is disrupting existing”. 

Dipl.-Ing. JAKOB DÜCK, Global Industry Segment Manager 

THE CORRELATION, as well as the resulting 

transformations of business models and 

the associated risks, must be viewed in a 

highly differentiated manner. The mechanical 

engineering sector in particular, 

featuring its typical structure of SMEs and 

"hidden champions", is in a very good position 

worldwide to perceive digitalisation 

not as a threat but as an opportunity to 

expand existing business models and, over 

the long term, to open up new markets by 

leveraging new technologies. In the final 

instance, it is clear to all business players 

that digitalisation will secure the longterm 

competitiveness of OEMs in the mechanical 

and plant engineering sector. 

Which arguments support such a view? 

1. Digitalisation can only be 

successfully mastered in many 

individual steps. For those involved, 

this cannot be about all-encompassing 

umbrella functionalities, as described 

under the terms "IIoT", "Industry 

4.0", "Digital Engineering" and the 

like. Far more, it is about concrete 

approaches that can be used to advance 

the efficiency and cost-effectiveness 

of machines along the entire "life 

cycle" with as little input and effort 

as possible. And because automation 

in mechanical engineering has been 

driven principally by digitalisation for 

decades, it is primarily those OEMs 

that can successfully implement 

these approaches based on their core 

competencies. Only OEMs are able 

to implement specific measures that 

combine existing functionalities and 

systems with the most promising 



new control and data transmission 

technologies! [2] 

2. Digitalisation in industry is often 

mentioned along with the keyword 

"Industry 4.0", a term which stands 

for the 4th industrial revolution: 

consequently, the disruptive potential 

of current technical developments 

is equated with the effects of the 

industrial use of steam engines, 

electricity and computers. Successful 

players such as Amazon, Microsoft and 

Google are often cited as prominent 

examples of the forces of change. In the 

case of the medium-sized mechanical 

and plant engineering industry, on 

the other hand, these developments 

appear at least partly as a threat. The 

protagonists of digitalisation are 

endeavouring to take the edge off 

this. Hans Beckhoff, the founder and 

CEO of Beckhoff Automation, very 

aptly explained during an IHK event 

in 2017 that these changes and shifts 

represent opportunities for industrial 

manufacturing and that the speed of 

the upheaval is slower than initially 

assumed: "From today's perspective, 

the introduction of the steam engine 

seems like a revolution. However, 

at the time it took more than half 

a century for its use in industry to 

result in substantial changes." In a 

similar manner, he stated, one should 

regard the impact of digitalisation for 

industrial production today, which is 

triggering an evolutionary development 

at all levels and in all processes. At the 

same time, Beckhoff also emphasises 

that this realisation by no means 

guarantees that one should sit back and 

do nothing! According to Beckhoff, it is 

precisely the courageous protagonists 

who will be rewarded if they creatively 

develop new business models for 

production systems. 

HARTING has analysed the 

implementation strategies of its 

customers and can decidedly confirm 

Beckhoff's theses. Accordingly, in 

order to achieve sustainable success 

with digitalisation projects, it is above 

all advisable not to want to achieve 

everything immediately [3]. 

Whether the development is revolutionary 

or evolutionary: All parties 

involved agree that data forms the basis of 

more rational processes - and indeed all 

types of data. The catch phrase "Data is the 

new oil!" originally referred to "Big Data" 

or the storage and availability of consumer 

data. But this characterisation can certainly 

also be applied to data in the industrial 

arena. However, to stay with the metaphor, 

this "new oil" still requires functioning 

"pipelines" and other structural elements. 

Consequently, "Data is the new oil" not 

least describes the current situation of 

many machine and plant manufacturers 

who are in the process of revising the generation, 

processing and transmission of 

data for their products. 

The OEM's "data view" of production 

systems today can be summarised as follows: 

- OEMs are experts for many existing 

technological, machine-related data, as 

well as for the use of this data in intrinsic 

machine functions, and for advanced automation 

functions 

- The increased use of the "internal 

intelligence" of automation components 

such as drives, smart sensors, actuators or 

HMI systems with all the associated data 

transitions is also part of an OEM's standard 

toolkit today 

- In addition, this comprises all possible 

data transfer layers on the level of interlinked 

machine or production lines that 

use known data origin, machine, user and 

process models, which are also considered 

proprietary know-how 

- However: In terms of digitalisation, 

not all the aforementioned data structures 

and transmission layers that are part of the 

control and automation systems should 

simply be "discarded" and replaced by new 

ones. This is due to the fact that almost the 

entire functionality of modern production 

systems is based on software and suitable 

specific interfaces - these functionalities 

have been developed with an enormous 

amount of material and engineering effort. 

Consequently, an initial conclusion is 

as follows: In order to drive digitalisation 

forward with as little effort and input as possible 

and to cope with the associated rising 

data volumes, machine and plant manufacturers 

must be able to continue to use existing 

data structures and interfaces! 

In the sense of ‘the data is the new oil’ 

analogy, proven and sufficiently functional 

"pipeline structures" must continue to be 

used and extended to include new "pipelines". 

In this way, companies will succeed 

in enhancing their competitiveness and 

gaining new market shares in their own 

business segment or in other fields of production 

technology. To put it in terms of 

control technology for industrial systems: 

An OEM active in mechanical engineering 

needs its proven fieldbuses and interfaces 



for evolutionary digitalisation. At the 

same time, suitable physical interfaces are 

advantageous for the expansion of new 

systems and services in the edge areas, as 

well as for the most seamless connection 

possible to the world of "Big Data". Players 

mastering both disciplines will be best 

equipped to meet the growing and, in some 

cases, still unknown future requirements 

of machine users. 

The trend-setting requirements for 

developments related to digitalisation 

outlined in the following section are based 

on the experience of the HARTING Technology 

Group. The company provides 

solutions for all types of data interfaces 

of modern drive, control, HMI and communication 

technology in mechanical engineering 

production systems. HARTING 

is also a pioneer in many ground-breaking 

developments for power and signal transmission 

in the industrial arena. In the 

field of Industrial Ethernet, HARTING is 

playing a key role in shaping and designing 

various standards on the physical layer: for 

example, the company is actively involved 

in solutions for the so-called SPE (Single 

Pair Ethernet) technology. 

Decades of experience in the field of interfaces 

for factory automation, combined 

with the expertise of a trendsetter in the 

latest data transmission technologies (including 

the "Big Data" world), make it possible 

from HARTING's viewpoint to always 

find optimal solutions on the physical layer 

for each and every specific interface design. 

With the help of the right interfaces, OEMs 

can decisively drive the migration to digitalisation 

forward that is so vital for them. 

In each solution here, the respective application 

with its mechanical, environmental 

and EMC conditions and other requirements 

must remain leading. 

"What is the simplest and most effective 

way to design data transmission interfaces 

in production systems - at all conceivable 

levels of the factory and all the way into 

the 'cloud'? " This question often causes 

headaches in the R&D and engineering departments 

of machine building companies 

that want to gradually shape and design 

individual actual digitalisation aspects in 

their projects. The requirements that need 

to be met are as follows: 

- All types of data interfaces should be 

implementable, both well-proven but also 

current innovations 

DIGITALISATION WILL SECURE THE LONG-TERM COMPETITIVENESS 

OF OEMS IN THE MECHANICAL AND PLANT ENGINEERING SECTOR. 

- The range of interfaces must be scalable, 

i.e., the same interface type can be designed 

in the required normative version, 

IP protection class or for the required environmental 

conditions (EMC, resistance 

to dirt, UV radiation, mechanical stresses 

such as shock & vibration or the hygiene 

requirements) 

- With regard to the transitions between 

sites or sections, it must be possible to use 

interfaces that function reliably and conform 

to standards 

- Product variants must be available 

that are designed for different manufacturing 

and assembly processes at the OEM, 

e.g., for tool-free assembly if flexibility is 

required, or for automatic assembly in the 

case that higher quantities are to be manufactured 

in connection with a high level of 

process reliability 

- Data interfaces must be combinable 

with each other and must be placeable with 

other signal and power interfaces in one 

enclosure or even together in one insulator 

in order to save space and costs and to simplify 

processes. 

The approach outlined above allows developers 

and project managers to concentrate 

on the central tasks for their respective 

application during the design phase 

- without having to spend time on the "less 

important criteria" of interfaces. At the 

same time, they can be certain that there 

is a suitable interface available for every 

expansion stage of a machine module or a 

data transmission link. The corresponding 

solutions are both cost- and functionoptimised 

and scalable. The cost-efficient, 

technically straight forward expansion of 

services and system extensions at all levels 

of factory automation and beyond can be 

implemented at any time, even retroactively, 

at the respective machine user. 

Figure 1 presents the approach in an 

exemplary way: It provides an overview 

of the best-known network systems for 

industrial data transmission and describes 

selected actual HARTING solutions, which 

are shown as product families. It is apparent 

exactly how great the freedom in the 

design of the data interfaces actually is: 

for practically every type of field bus or 

Industrial Ethernet there are several options 

available for designing the physical 

layer. This means that it is (almost) always 

possible to find a solution that is optimally 

suited to the application - even for requirements 

that are still unknown today and/ 

or for digitalisation requirements that are 

growing along with the application. 

SOURCES: 

[1] T-Systems: Accelerate Digitalisation (t-systems.com) 

[2] HMS: "The goal is to generate value from data."(industry-of-things.com) 

[3] HARTING: http://www.us- tech.com/RelId/2651702/ISvars/default/Weighing_the_Cost_and_Benefits_of_Digitalization_in_Manufacturing.htm 


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CERTIFICATION 

What is the big deal about 

accredited certification? 

Being certified is just 

about passing a test, 

right? If you can pass the 

test, surely that is proof 

that you are worthy of 

being certified, right? 

And as long as there are 

no obvious and easy 

ways to cheat, surely the 

examination process is 

adequate, right? 


JASON TRANTER, 

ARP 

Mobius Institute 

AS THE OWNER of two organizations that 

provide training and accredited certification, 

I have a different opinion that I 

would like to share. So yes, I am biased, 

but we have done things the hard way 

for very good reasons (and the ISO is on 

my side – or should I say, I am on their 

side). 

To train or not to train, that is 

the first question 

Not that it is specifically related to the 

accreditation process, but people often 

ask why training is required to be 

certified by Mobius Institute. 

First, the condition monitoring 

standards (ISO 18436) defined by the 

International Standards Organization 

(ISO) require that training is completed. 

They define the topics that must be 

covered and the duration of the train-

CERTIFICATION 

ing. But even the Asset Reliability Practitioner 

(ARP) certification requires 

training, and it is not part of the condition 

monitoring standards. 

The reason the ISO choose to require 

training, and why the author agrees with 

that decision, is because an exam can 

only cover a limited set of questions - it 

is hoped that a person who is certified 

would have much broader and deeper 

knowledge. Although there are typically 

100 questions on an exam, it is impossible 

to test on every single subject. And 

on most exams, you only have to get 70% 

of the questions correct. 

Therefore, we believe that a person 

who is certified should have been 

educated in a structured manner to a 

healthy level of depth on all of the relevant 

topics – not just know enough to 

get by. The exam "simply" ensures that 

they understand certain facts, concepts, 

and principles and can apply their 

knowledge. 

Should you need experience 

to be certified? 

Again, the ISO condition monitoring 

standards require that candidates meet 

certain experience requirements which 

must be independently verified. For 

example, an ISO 18436-2 Category II 

vibration analyst must have 18 months of 

experience before they can be certified. 

But again, if you can pass the exam 

why do you need to prove that you have 

experience? In the author’s opinion, 

the requirement to have experience not 

only ensures that certified personnel 

can deliver greater value, but it also adds 

weight to the meaning of being certified. 

If a person is smart enough, they can 

learn just enough to get through the 

exam potentially without ever having 

tested a machine or visited an industrial 

site. An employer or consulting client 

should have some confidence that a 

certified person has at least a minimum 

level of competence. Verified experience 

provides that confidence. 

Can’t the exam be written so 

that only competent people 

can pass? 

There are certainly those who believe that 

is true, but the author is not one of them. 

First, when most exams have a 

70% pass grade, any questions that do 

require experience do not even need 

to be answered correctly. But you may 

like to try to think of an exam question, 

that has four multiple choice options, 

that only a person with experience can 

answer correctly, without there being 

any requirement to work in any specific 

industry (i.e., you can’t ask a question 

about paper mill reliability, or a question 

specific to mining). And if you can come 

up with such a question, well done, now 

you only have to write 99 more before 

the exam is ready to go. 

So no, even though we ensure that 

our exams are as practical as possible 

(i.e., not just calculations, recognition of 

acronyms and definitions, etc.) I do not 

believe that only a person with genuine 

experience can pass. 

WHEN A NEW CERTIFICATION 

PROGRAM IS DEFINED IT 

MUST BE APPROVED BY 

THE ACCREDITATION BODY. 

What is the difference 

between accredited and 

non-accredited certification? 

In short, accredited certification has 

been audited by an independent, 

Government approved entity that 

ensures that what the certification 

body does is fair, independent, and 

that it meets all of the requirements. 

While there are standards like 

ISO 18436 that define how condition 

monitoring certification works, there 

are many certification programs 

that are not specifically 

defined by the ISO. ARP, 

CMRP, CRL, and CRE 

are all such programs. 

Rather than defining 

every last detail of how 

every single certification 

program should operate, 

a standard has been 

developed, ISO/IEC 17024, that defines 

the core elements that every personnel 

certification program must meet. 

Accreditation bodies such as ANSI, 

JAS-ANZ, and UKAS can then audit 

certification bodies such as the 

Mobius Institute Board of Certification 

(MIBoC) and SMRP to ensure that 

everything is clean and aboveboard. 

When a new certification program 

is defined it must be approved by the 

accreditation organization. It will then 

be audited every six months for the first 

two years minimum and then every 

12 months thereafter. 

They check everything. 

They randomly select a large number 

of certified candidates and ensure 

they were trained correctly, examine 

correctly, they meet the experience 

requirements, and much more. They 

also ensure that our personnel follow 

all procedures, that we have an effective 

quality control process in place, and 

that everything we do in relation to the 

examination process meets the highest 

standards. That is why we video record 

people taking exams, there must be 

invigilator’s, we have exam databases, 

and much more. 

I could easily make this an extremely 

long, detailed, (and boring) article by 

explaining all of the details behind our 

committees, examination development 

and statistical analysis, and much more - 

but I will save you from the detail. Let us 

just say that no stone is left unturned to 

ensure that everything is done correctly. 

As a business owner, there are 

numerous situations where decisions 

must be made in line with the certification 

and accreditation requirements 

versus decisions that would lead to 

greater revenue and profit. But that is 

the way it needs to be if certification 

is to be respected by those who are 

certified and those who wish to employ 

certified personnel. So yes, accreditation 

matters. 


CASE STUDY 

Avoiding Unplanned Downtime: 

Online Monitor of Critical Bearings 

ADRIAN MESSER, 

CMRP, UE Systems 

adrianm@uesystems.com 

Keeping a close eye on the condition of critical equipment 

is fundamental in any industrial facility. When 

critical bearings fail, it almost always leads to unplanned 

downtime and interrupted production process, 

costing companies thousands in production loses. 

IN THIS CASE STUDY we will look at how an online monitoring 

solution using ultrasonic sensors was able to detect an issue on 

a critical bearing before it turned into a big problem. 

Critical equipment: bleach decker in a pulp and 

paper plant 

Usually, in pulp and paper plants we will find a wash floor or 

wash area, where the paper comes through to be thoroughly 

cleaned / bleached. That job is done by a machine called a 

bleach decker, which is considered a critical and fundamental 

piece of equipment for production operations. 

In this particular plant, which has a predictive maintenance 

program in place, it was decided to invest in online monitoring 

for these machines. The maintenance team wants to be alerted 

as soon as anything unusual is happening with the equipment 

in order to prevent any failures that would lead to a stop in 

production. This machine has 4 bearings, of about 48 inch / 

120 cm diameter, rotating at 3 RPM. 

To enable online monitoring and early failure detection, 

ultrasonic sensors are being used on the machines’ bearings. 

These are UE Systems Remote Access Sensors, which are permanently 

installed on the bearings and constantly collect decibel 

readings and sound recordings. All this data is then sent to 

a central processing unit called the 4Cast. This unit is connected 

to the Internet and will alert the maintenance team (e-mail 

and SMS alerts) when certain decibel levels are reached. 


CASE STUDY 

Bleach decker with the ultrasonic sensor mounted on the bearing 

The sensors are connected to the 4Cast, a central processing unit 

with internet connection 

Why ultrasound? 

The preference for ultrasound technology to monitor these 

bearings has to do with its obvious advantages: since the 

ultrasonic sensors monitor the bearings’ friction levels, any 

increase in friction will be noticed. This allows for a very early 

warning of failure. Also, because the data from the sensors 

comes in the form of decibel readings, it is easy to interpret: 

the higher the friction, the higher the dB value. When this value 

reaches a certain limit above the baseline, an alarm is sent. 

And, even more relevant to these bearings, ultrasound is the 

most efficient technology to inspect slow speed bearings. The 

bearings on this machine are rotating at 3RPM. At such slow 

speeds, it is generally extremely challenging to notice any issues 

using technologies such as vibration analysis or thermography. 

But ultrasound shines when the subject is slow speed bearing 

monitoring, especially when you can record the sound from the 

bearing, analyse it in a sound spectrum software and check if 

the amplitude shows any peaks, which normally indicate a fault. 

Thus, ultrasound is the perfect technology when we want to 

online monitor slow speed critical bearings. 

Failure detection with online monitoring using 

ultrasonic sensors 

Everything seemed to be fine with the bleach decker at this 

pulp and paper facility, as the machine was working as expected. 

However, the 4Cast, an ultrasonic online monitoring 

system, received an unusual decibel reading from one of the 

ultrasonic sensors. The NDE (non-drive-end) bearing of this 

bleach decker was registering 17dB when, normally, a bearing 

rotating at such slow speeds like 3RPM should simply show a 

0dB reading. 

This, of course, triggered the system to immediately alert 

the maintenance team. The 4Cast was setup to consider any 

reading above 8dB on this bearing to be a high alarm, and 

therefore, the following alert was sent from DMS, the UE Systems 

software where all the data from the 4Cast is stored: 

We can clearly see why the alert was triggered: the 4Cast 

received a 17dB reading from a bearing where the threshold for 

a high alarm was setup at 8dB. The alert message also contains 

useful information regarding the machine (operating floor 

slow moving bearing; bleach decker) and, naturally, a time 

stamp of when the reading was taken. 

When an alarm level is reached, the 4Cast will also take a 

sound recording from the bearing for further analysis. This 

is especially useful in slow speed bearings, where the sound 

spectrum can tell us a lot about what’s going on with the asset. 

In this case, and even though the machine was apparently 

working as expected, the sound file spectrum showed a very 

different story. 


CASE STUDY 

The peaks shown in this sound sample clearly indicate a 

problem with the bearing. Also, when reproducing the sound 

file, we could very clearly hear the impact noises. The failure 

was even more obvious when the sound file was compared to 

a sound recording from one of the other bearings. 

We can clearly see the differences. In this case, the recording 

sounds smooth and looks uniform, and we don’t see 

amplitude peaks at all. So, this would be an example of how 

the sound spectrum of a good bearing should look like. 

The next step for the maintenance team was scheduling 

the replacement of this bearing, without disrupting production. 

When the bearing was dismantled, the damage was 

clearly visible. 

The signs of impact are obvious. Also, metal fragments 

were found in the shaft, plus spalling, with some pitting, and 

slight abrasion were present in the outer race. 

Conclusion 

By detecting the issue at an early stage, the maintenance team 

was able to replace the bearing during scheduled downtime 

and without disrupting the production process. We can imagine 

the consequences if the issue was not detected at this stage 

and the bearing was allowed to continue operating: the metal 

fragments would certainly affect the motor shaft, which would 

then also need to be replaced; and the facility would have to 

face unplanned downtime. In such a situation we could be 

looking at a loss of around 250K GBP. 

By using the proper technology, with the proper maintenance 

procedures in place, the team was able to identify and 

solve the issue before it became a major problem. This case 

study shows how powerful ultrasound technology can be, 

especially when used in sensors connected to the network to 

provide truly online and permanent monitoring solutions. 



Can we build sandcastles 

with loose sand? 

How apparently worthless data, can still be useful 

The Internet of things, big data and prescriptive 

maintenance are supposed to be the big promises 

from the 21st century. No more unplanned downtime 

because we can predict all failures. Many companies, 

however, struggle with these developments. They 

say there is hardly any data available and question 

the usability. It appears to be “loose sand” … until you 

start working with it. 

PETER DECAIGNY 

MAINNOVATION, 

Peter.Decaigny@ 

mainnovation.com 

THE FIELD of maintenance is developing. 

More and more, maintenance and 

asset management are mentioned 

in the same breath. Where maintenance 

focuses mainly on the present 

and nearby future – the assets need 

to function to be able to deliver the 

required production or service – asset 

management is more about long-term 

planning, life-cycle issues, and modernization. 

The goal is to monitor the 

lifespan of the assets and start investment 

projects at the right time to guarantee 

safety and reliability. 



Poor data quality? 

Executing asset management requires 

knowledge about the expected lifespan, 

use, degradation and, eventually, 

failure of assets. 

– Our experience is that companies that 

think they only have low-quality data, at 

least have these figures at their disposal, 

says Peter Decaigny of Mainnovation. 

– And they are more useful than 

you think. 

Companies that are ruled by the illusion 

of the day – they are operating like a fire 

brigade with a focus on corrective maintenance 

– struggle to see the big picture. 

– Everyone is busy, malfunctions 

regularly occur and most attention is 

paid to solving them as quickly as possible. 

The misconception is that registration of 

this downtime is just 'loose sand'. However, 

if you compare the downtime (in hours) 

with the frequency of failure - the number 

of failures - you gain insight into which assets 

often cause downtime for a long time. 

These are the assets you need to pay attention 

to, Decaigny says. 

– By looking for the cause or reason 

for the downtime, perhaps they can be resolved 

or removed. 

– We gain insight into the 'Mean Time 

Between Failure' and also the 'Mean Time 

to Repair' and this helps to draw the right 

conclusions. 

Incoherent? 

In another case there is a fair amount of 

data available, but there seems to be no 

connection. It seems like an incoherent 

story from which no conclusions can be 

drawn. But here too the message is 'just 

start with what you do know'. 

The first step is collecting and analysing 

data. If the analysis only raises questions 

and does not provide any insight, it is advisable 

to take a critical look at the representation 

of those figures. 

– We can, for example, plot the Time to 

Failure in chronological order. So how many 

weeks did it take for an asset to fail. This 

could just result in an arbitrary number of 

figures. From 120 weeks, to 40 weeks, to 16 

weeks and then suddenly 118 weeks again. 

It is difficult to draw conclusions from this. 

We see companies making the mistake of 

taking an average. In this example, one 

would replace an asset before the 74th week, 

but is that 'just in time' or is this capital 

destruction? Data can often initially lead to 

confusion instead of insight. 

– But don't give up, Decaigny says. 

– Start to combine data. Use other 

models that compare different values to 

the Time to Failure. Or focus on the peaks. 

What causes the outliers? 

Big data 

Mainnovation has clients within plants, 

fleet and infra. This means great versatility 

in assets. From power stations, 

tank storage companies and industrial 

installations, to transport and various 

infrastructure companies. 

– Every asset is unique. Even comparable 

assets have unique factors such 

as the method of use, the level of maintenance 

and the skills and tools of the 

operators and the technical service are 

of influence, explains Decaigny. 

THE INTERNET OF 

THINGS, BIG DATA AND 

PRESCRIPTIVEMAINTENANCE 

ARE SUPPOSED TO BE THE BIG 

PROMISES FROM THE 21ST 

CENTURY. 

– Furthermore, there are, for example, 

weather influences or the pressure 

or humidity that can have negative 

effects on the materials. We know the 

term 'a Monday morning product', but 

that should be adapted to ‘a Tuesday 

afternoon product’, because statistically 

that turns out to be a bad production 

moment. How come? Nobody knows. 

With this Decaigny wants to emphasize 

that we cannot just blindly rely on 

numbers. 

– It starts with data. And by working 

with it you can improve the data. Then 

it will become clear that the factor 'temperature' 

– as an example – must also 

be taken into account. And who knows, 

you might discover that the Thursday 

afternoon operator prefers to work with 

an open window. 

Value drivers 

A correct analysis of the available data is 

therefore of great importance. Not only 

when minimal data is available, but also 

when we use big data and take various 

external factors into account. Moreover, it 

becomes increasingly difficult to compare 

apples to apples and to draw conclusions. 

Another approach to get started with data 

is to first determine what the most important 

value driver is. In other words: what should 

we aim for in order to create value with maintenance 

and make a positive contribution to 

the operating result. 

– That can differ per company, even per 

factory, explains Decaigny. 

– While one wants to aim for maximum 

uptime, because the demand for the product 

is very high, the other may have to focus on 

cost reduction. There is also value in reducing 

(security) risks or perhaps it is better to invest 

in modernization now because this has economic 

added value in the long term. 

These are the four value drivers from 

Mainnovation's VDM XL methodology. 

– And whoever wants to steer in four directions, 

will eventually come to a standstill, so 

that's never a good idea. 

Compare 

In the first example we mentioned in this 

article – where it was all about minimizing 

downtime – the focus was on asset utilization 

and improving uptime. In this case we 

opt for the value driver cost control. If this is 

the mission, we should collect data to help 

make decisions about operational expenditure 

(OPEX). But how do you know whether 

these costs are too high and can possibly be 

reduced? 

Decaigny has a simple answer to this 

question: 

– Compare, for instance by benchmarking. 

Mainnovation has a benchmark database 

of more than 1.000 companies. Comparing 

data with companies in the same industry, 

provides a realistic picture of the improvement 

potential. By dividing the investment 

amount by the replacement value, large 

companies and small companies can still be 

compared. 

– For this big data is not a must. But it is 

important to choose the right data. By comparing 

apples to apples, you know where you 

stand and what the possibilities are to take 

steps forward to bring you closer to your 

business goal, states Decaigny. 

So, we have seen that it is good to take the 

plunge and just get started. Even if it seems 

like loose sand, it is possible to build sandcastles. 

A final tip that Decaigny also likes to 

give is: 

– Make it visible. Let the shopfloor participate. 

Show the data, show the improvements 

so that people also understand which 

buttons they have to turn – literally or figuratively 

– to get even more positive figures. 



What led to 

this condition? 

Everything is fine? That shouldn't make us invisible 

18 pumps under the responsibility of a Condition Monitoring team, 

demonstrating an almost identical behavior, with identical symptoms… and 

surely calling for full attention. A user (meaning a friend, a member of the SDT 

family) asked me to assist. I was happy to join the party. 

TEXT: HARIS TROBRADOVIC I IMAGES: SDT, SHUTTERSTOCK 



We are so dedicated to look for a root cause of each failure, 

to prevent it from re-occurring. Well, let’s look for a root 

cause of success with the same dedication and invested effort, 

to make sure it does re-occur. 

FIRSTLY, I looked at all the Ultrasound data one by one, and all 

of them looked quite similar to the one shown above. 

After detailed examination of the entire data set, I found 

ABSOLUTELY NOTHING WRONG. With no hesitation, I 

called some people much cleverer than myself, to review all 

the vibration data and they came back with the absolute same 

conclusion about the condition – they found ABSOLUTELY 

NOTHING WRONG. 

Although it seemed that the party was over, the best part 

was yet to come: some Root Cause Analysis (RCA), root causes 

of that condition and maybe some recommendations. “If it 

wasn’t in a newspaper, it never happened”. 

One might think that there was no reason to do an RCA, and 

that there was nothing to report, because everything was fine. 

Well, we thought that we had a perfectly good reason for RCA 

and a proper report. 

Because everything is fine 

Just a summary of the issued report: 

As you can see, there is a lot to report. That excellent 

condition did not happen all by itself. There were decisions, 

investments, training, people … and lots of knowledge and care 

involved to come to the point where we found no issues in the 

collected data. 

Let’s look at ALL heroes, 

not just some of them 

Usually, I read about finding a defect, a potential failure. That 

is, of course, good. It justifies the use of technology, it proves 

the competence of the expert using it and it proves that Condition 

Monitoring is a lifesaving approach, so to speak. 

But, finding a defect, even in the earliest stages, is never 

good news. 

It is surely better than waiting for an asset to start sending 

smoke signals and fail, but in its essence; it is not good news. 

Nobody celebrates when a medical diagnostician finds a 

problem, even in the early stages. It proves that he uses proper 

technology in a proper way, it proves that he is a good expert. 

But that is not good news. 

Look at how it developed over the years, moving from full 

reactive behavior to predictive. Years ago, companies were 

celebrating people coming in at 3 am to repair failed assets, 

purely reactive. Those people had a complete exclusivity on 

heroism. That was wrong, of course. 

Then, we learned a lesson, and started celebrating those 

who detect problems much earlier, Condition Monitoring. It 

didn’t go smoothly, there was a lot of effort invested in writing 

a report about success, because it is not an easy task writing 

about something that would cost X $ if not addressed in time. 

Practically, reporting an absence of a huge problem by showing 

a presence of small one. Showing an egg that would become a 

dragon. 

People easily notice the presence of a bad 

event, but fail to notice the absence of one 

Moving to a proactive mindset makes recognizing heroes even 

more tricky. How do you convince management about the danger 

coming from a dragon, when you don’t even have an egg to 

show? How do you report the absence of a big problem without 

having a small problem to show? How do you report the complete 

absence of problems? How do you connect that absence 

with your work? And, on top of that, how do you translate it to 

a language that fits the business targets? 

Tricky, isn’t it? 

Condition Monitoring is much more than just detecting 

anomalies. We should not forget that an important (and surely 

desirable) part of the job is to confirm good condition. And 

that should be the most satisfying part of the job; issuing a report 

saying that you can confirm all the assets are working fine. 

That doesn’t mean that your technology doesn’t work well. 

That doesn’t mean that you are not good at it. It just means 

that your work improved the Reliability to the level where 

you do not have so much detected problems to show. But you 

should show the absence of them. 

Make a success root cause analysis and report it. 

Then … share the glory with those who made it possible. 

Those whose job is to make sure you have nothing to detect. 

The lubrication community is one of them. 

Let’s start bragging with perfect signals coming from perfectly 

operating assets 

… and explaining why that is so. 



OPC UA, MQTT, and 

Information Interoperability 

In earlier times, OPC learned a hard lesson that tying a specification to a specific wire 

protocol leads to obsolescence as technology evolves. This is why OPC UA has layered 

architecture, which makes it possible to create mappings for any number of transports 

like JSON HTTP or UA TCP for Client/Server and MQTT or UA UDP for Pub/Sub. 

By STEFAN HOPPE, President OPC Foundation 

WHEN OPC releases a specification, 

they try to provide mappings for what 

the market has initially indicated they 

want, only to find that sometimes the 

uptake may be diminished (e.g., AMQP). 

The power of OPC UA is that these mappings 

can be quickly modified to implement 

new mappings that better match 

market needs (e.g., MQTT). When a 

future technology emerges, a such as 

QUIC/HTTP3, OPC UA is ready. 

The reason protocols can be added as 

needed is because the value of OPC UA 

comes from information interoperability, 

which exists no matter what protocol 

is used to communicate. OPC UA provides 

a standard framework for describing 

information that can be accessed by 

Client/Server or Pub/Sub. This enables 

a level of plug-and-play between applications 

from different vendors that 

cannot be achieved by simply standardizing 

the message format and topic tree. 

This is particularly true for cloud-based 

applications that need to integrate data 

from many sources. 

This is why Erich Barnstedt, Chief 

Architect, Standards & Consortia, Azure 

IoT at Microsoft, shared that, “One of 


the questions I get quite a lot is “should I 

use OPC UA or MQTT to send industrial 

data to the cloud?” My answer is always 

the same: Use both! OPC UA for the 

payload and MQTT for the transport. 

Let me explain:” “First of all, comparing 

the two technologies is an apples-to 

oranges comparison, as OPC UA is an 

application while MQTT is a protocol. It 

is like asking: “Should I use web pages or 

the Internet Protocol for my website?” I 

think you get my point...” The emphasis 

on the need for information interoperability 

was also why the OPC Foundation 

and CESMII joined forces to create the 

OPC UA Cloud Library, which enables 

the publishing and discovery of standardized 

OPC UA Information Models as 

a component of the Smart Manufacturing 

Innovation Platform and Profiles. In 

their July 2021 press release, CESMII 

stated, “The key to new levels of innovation 

and performance will only be 

achieved when information, and associated 

context, can flow freely in the enterprise, 

to users and applications that 

need that information.” Delivering reusable 

Information Models is a strategic 

component of the Cloud Library. 

The protocol independent architecture 

of OPC UA also allows for synergies between 

applications that would not necessarily have 

anything in common. For example, all of 

the major automation vendors are investing 

heavily in OPC Foundation’s Field Level 

Communication (FLC) initiative, which is 

based entirely on UA Pub/Sub using UDP. 

For these applications, MQTT simply cannot 

provide the capabilities that the controller-to-controller 

FLC applications require. 

On the other hand, the UA Pub/Sub infrastructure 

developed for FLC will enable 

connectivity to the cloud via UA Pub/Sub 

over MQTT because the overall architecture 

and configuration model is the same. This, 

in turn, will mean a lot of OPC UA commercial 

off-the-shelf (COTS) products will be 

available that can push data to the cloud via 

UA Pub/Sub over MQTT. In the long term 

this means a much greater selection of products 

will be available to factory owners that 

need to connect their factories to the cloud. 

This emphasis on information interoperability 

and protocol adaptability makes 

OPC UA the best long-term solution for any 

factory owner looking to leverage MQTT 

and a means to connect their factories to the 

cloud.

Online Condition 

Monitoring 

Be Vigilant over your critical assets 

• Ultrasound 

• Vibration 

• Temperature 

• Tachometer 

• Process 

A turn-key 

condition monitoring 

solution combining the versatility 

of ultrasound, the analytics of vibration, 

standard communication protocols and an 

embedded trending and analysis software.

RISK MANAGEMENT 

Risk Based Inspection 

TEXT: PDM 

Every now and then, the world is shocked by incidents, such as explosions, 

emissions of toxic gases, or large fires. For instance, you will recall the giant 

explosion of 2,750 tons of ammonium nitrate in the Beirut harbor on August 4, 

2020. Such incidents are nightmares that must be prevented at all times. 

THIS ARTICLE focuses on one aspect of risk 

reduction in the process industry, namely 

the risks of static asset failure. It presents 

a methodology that helps companies to 

focus on the most critical static assets. That 

focus is necessary because in an average 

plant there simply are too many static assets 

to always keep an eye on. 

Risk Based Inspection is an effective 

and efficient method because it contributes 

to the integrity and reliability of static assets 

in industrial facilities. It helps to properly 

allocate the inspection resources to the 

static assets with the highest risk profiles 

that need the most attention. 

Risk Based Inspection (RBI) is an 

effective and efficient inspection management 

& planning methodology. RBI contributes 

to the integrity and reliability of 

static assets in industrial facilities. It helps 

to properly allocate the inspection resources 

to the static assets with the highest risk 

profiles that need the most attention. 

Therefore, it is recommended to use RBI 

as a default method, especially during large 

maintenance projects, rather than waiting 

for static asset failures, with related unsafe 

situations and consequential damage. 

RBI is considered as part of the Risk 

Based Maintenance (RBM) approach and is 

also seen as a way of working towards Condition 

Based Maintenance (CBM). 

Static assets and risks 

STATIC AND MECHANICAL ASSETS 

There is distinction between static and 

mechanical assets. The simple fact that 

mechanical parts contain rotating or moving 

parts usually leads maintenance departments 

to pay more attention to these. 

Generally speaking, mechanically energized 

rotating or moving parts often break down 

relatively fast during operation. The movement 

of parts tends to create friction, heat 

and vibrations that cause wear. 

However, these are not a valid reason to 

neglect the static parts in industrial facili- 

RBI BEST PRACTICES – API 580 / 581 

This white paper is based on the API 580 “Risk Based Inspection” standard, which was 

issued by the American Petroleum Institute for application to static assets in refining, 

petrochemical, and chemical plants. This standard describes the minimum requirements 

by providing essential guidelines for the implementation of an effective and 

usable RBI program. 

The API 581 “Risk Based Inspection Technology” standard compliments these guidelines 

by detailing the appropriate methodologies and procedures to be followed. For 

example, it provides quantitative calculation methods to determine an inspection plan. 

The standards provide the basic RBI system design and is a great starting point for 

those wishing to begin with RBI initiation. 

There are other international standards that can be used in addition to or instead 

of API 580/581, for instance, the European standard EN 16991 “Risk-based inspection 

framework” and PCC-3 “Inspection Planning Using Risk-Based Inspection”, issued by 

ASME (American Society of Mechanical Engineers). 



ties, because these are subject to different 

deterioration mechanisms, for example 

corrosion under insulation (see textbox). 

Static assets are typically used for the 

construction of plants or for storage and 

transportation of fluids. These fluids are 

often dangerous, especially in chemical 

installations. 

The integrity and reliability of mechanical 

assets is the domain of Reliability Centered 

Maintenance (RCM), while static assets 

are the subject of Risk Based Inspection. 

Despite the similarities in approach 

of RCM compared to RBI, for example with 

regard to the risk matrix, RCM is beyond 

the scope of this document. 

Static assets 

Static assets are parts of industrial facilities 

that do not contain rotating or moving 

parts. An average plant can easily number 

more than a hundred such static assets, for 

example: 

• piping systems 

• storage tanks 

• (pressure) vessels 

• heat exchangers 

• asset housing or casing 

• load-bearing structures. 

Examples of mechanical assets are pumps, 

rotating shafts, conveyor belts, turbines, 

engines, and robot arms. 

FAILURES OF STATIC ASSETS 

The process industry faces some major issues 

that imply that Risk Based Inspection 

is part of the solution to a broader problem 

than the failure of static assets: 

• Corrective maintenance is often carried 

out too late (after the outage), 

preventive maintenance often too early 

(better safe than sorry) 

• Cost-inefficiencies and low uptime / 

availability levels due to unplanned stops 

• Inadequate degradation models, limited 

standardized guidelines, lack of specialized 

(technical) tools. 

Corrosion under insulation (CUI) 

Corrosion under insulation (CUI) is a degradation 

mechanism that occurs in insulated 

pipes and appliances and it is one of the major 

threats to the aging assets of our contemporary 

industry. It is a difficult phenomenon to 

control because the locations where it occurs 

are difficult to detect. The rate of degradation 

depends on many factors and is difficult to 

predict. Corrective action is on average necessary 

after 20-30 years of operation. Potentially, 

the degradation of steel pipes and other 

equipment by CUI can lead to major incidents 

due to loss of integrity. To prevent this, corrective 

measures worth billions of euros are 

being taken. 

In order to reduce the risks associated with 

failure mechanisms, these mechanisms must 

be well understood, while control measures 

must be defined to ensure the integrity and 

reliability of the installation. The control 

measures consist of inspections, monitoring, 

adjustments & repairs. 

Improved system integrity, fewer unplanned 

stops, lower maintenance costs, 

and higher production are examples of the 

benefits and positive results of Risk Based 

Inspection. 

DELIVERABLES OF RISK BASED 

INSPECTION 

RBI results in five related deliverables (see 

Figure 1): 

1. Prioritization of high-risk components: 

WHAT to inspect 

2. Determination of inspection intervals: 

WHEN to inspect 

3. Expected damage mechanisms: WHERE 

to inspect 

4. Selection of best inspection method: 

HOW to inspect 

5. Data requirements for continuous improvement: 

WHAT to report. 

To obtain these deliverables efficiently, RBI 

follows a structured process. 

THE RBI PROCESS 

The RBI process as shown in Figure 2 consists 

of a loop with six steps: 

1. Data and information collection 

2. Risk assessment 

3. Risk ranking 

4. Inspection plan 

5. Mitigation 

6. Reassessment. 



Step 1: Data and information collection 

The RBI process is initiated with a data and 

information collection phase. This is needed 

to understand the characteristics of the 

primary processes, especially the damaging 

effect of the process mediums (chemicals) 

in the static equipment. This step provides 

accurate and up-to-date information for the 

next steps in the RBI process. The primary 

goal is to convert all that data into a suitable 

risk-based inspection plan for continuous 

condition monitoring, periodic inspections, 

and larger turnarounds. 

To enhance the failure-forecasting capability, 

the RBI database must include the 

following up-to-date information: 

• Description of failure mechanisms 

• Corrosion studies, especially of corrosion 

under insulation (CUI), a notorious 

equipment killer 

• Degradation models per process. 

Data is typically collected through detailed 

process analysis in conjunction with longterm 

corrosion and degradation studies for 

each part of the static equipment involved. 

Collecting and processing this data can 

take a lot of effort, requiring a long-term 

perspective, especially when different information 

systems are involved. Many large 

companies have already built a library of 

degradation models. 

Data for RBI analysis 

Data normally required for an RBI analysis 

may include, but is not limited to: 

• Type of equipment (original parts, replacements, 

or modifications; remaining 

lifetime) 

• Materials of construction 

• Inspection, repair, and replacement 

records 

• Process fluid compositions 

• Inventory of fluids 

• Operating conditions 

• Safety & detection systems 

• Deterioration mechanisms, rates, and 

severity (e.g., corrosion, corrosion under 

insulation, metal fatigue, stress, or 

chemical attack) 

• Personnel densities 

• Coating, cladding, and insulation data 

• Business interruption costs 

• Equipment replacement costs 

• Environmental remediation costs. 

A library of degradation models is the cornerstone 

of all maintenance strategies. Such 

a library is a prerequisite to switch from 

rule-based or time-based inspection to riskbased 

inspection. 

Step 2: Risk assessment 

For each part of the static equipment 

the probability of failure and the 

consequences of failure are assessed, 

based on data from the RBI database. 

The probability of failure analysis 

should address all deterioration mechanisms 

to which the equipment being 

studied is susceptible. 

The following consequences of failure 

must be considered: 

• Financial aspects 

• Health aspects 

• Environmental aspects 

• Regulatory consequences. 

RBI addresses the concerns of many 

plant managers: 

• Declining integrity of installations 

• Failure of static assets and its 

consequences (massive repairs, 

unplanned stops) 

• Insufficient availability and reliability 

of installations 

• Consequences of failure for business 

(costs, HSE, etc.) 

• Safety of employees and local residents 

(getting injured, burned, or 

poisoned) 

• Clean environment (hazardous 


leaks, powerful explosions, emissions of toxic gases) 

• Requirements imposed by the authorities (high fines, 

plant closure). 

The probability is typically expressed in terms of frequency 

(categories ranging from 1-5), while the consequences 

are ranging from “A” (minor) to “E” (severe). 

Next, the risk of failure is calculated, which results in risk 

categories, “high”, “medium”, “low”. 

Risk of failure 

= 

probability of failure x 

consequences of failure 

RoF = PoF x CoF 

The assessment of overall plant risk is highly complex. 

That's why a multidisciplinary team with a broad expertise 

is needed. The RBI process involves multiple stakeholders 

and various engineering backgrounds. The judgment 

of old hands in the profession is to be considered 

particularly valuable, but even for them it remains quite 

difficult to estimate the risks in the first place. 

Step 3: Risk ranking 

To prioritize the risks and to communicate the results of 

the analysis a risk matrix can be used, like the example 

shown in Figure 3 (a 7x7 matrix is also common). The risk 

categories (RoF) are plotted in the matrix of probability 

categories (PoF) by consequence categories (CoF). 

The analysis, as depicted in the risk matrix and substantiated 

by quantitative data, is used to optimize priorities 

and intervals for the inspection planning. Equipment 

items residing towards the right upper corner of the matrix 

should take priority, because these items have the 

highest risk. These are the most probable failures with 

the most severe consequences. In contrast, items residing 

towards the left bottom corner of the matrix will tend to 

take lower priority, because these items have the lowest 

risk. 

Like for many other phenomena, the Pareto principle 

is applicable for RBI, as it turns out that a large percentage 

of the total unit risk will be concentrated in a relatively 

small percentage of the equipment items. So, from 

all equipment items that are competing for attention, the 

idea is to review the inspection plan focusing on those 

components with the highest risk. 

Step 4: Inspection plan 

After the risk ranking, engineers try in collaboration with 

corrosion engineers to design an inspection plan that gives 

priority to components with the highest total risk. Material 

degradation and failure mechanisms will continue to 

develop, no matter if the act of inspecting is carried out or 

not. Inspection serves to identify, monitor, and measure 

these mechanisms before becoming critical. The inspection 

serves as a continual risk-updating and reduction effort, 

merely in terms of knowing what is currently going on 

with the static assets that are being utilized.


Limited resources and manpower 

prevent thorough inspections of all 

static assets, especially when costly 

inspection methods must be used in a 

relatively short period of time. Because 

the inspection plan allocates the inspection 

resources to the static assets with 

the highest risk profiles, while avoiding 

unnecessary regular inspections 

on non-critical items, RBI is actually a 

cost-cutting maintenance strategy. The 

potential savings on inspection cost are 

20-40 percent by implementing RBI. 

Categories of inspection 

To identify, classify, analyse, and evaluate 

failure mechanisms, RBI uses three 

categories of inspection: 

1. Visual inspection: external inspection. 

2. Invasive inspection: opening-up assets 

to take samples and examining 

CUI. 

3. Non-destructive inspection: endoscope, 

Eddy-current, acoustic emission 

and vibration analysis. 

The risk ranking will not provide a 

straightforward indication of the type of 

inspection; this needs to be determined 

per item by choosing the inspection method 

that is sufficient for detecting the deterioration 

mechanisms and its severity. 

Typical situations where risk management 

through inspection may have 

little or no effect are: 

• Corrosion rates well established, 

and equipment is nearing end of life 

• Instantaneous failures related to 

operating conditions such as brittle 

fracture 

• Inspection technology that is not 

sufficient to detect or quantify deterioration 

adequately 

• Too short a time frame from the 

onset of the deterioration to final 

failure for periodic inspections to 

be effective (e.g., high-cycle fatigue 

cracking) 

• Event-driven failures (circumstances 

that cannot be predicted). 

Step 5: Mitigation 

If completed inspections have shown 

that the inherent overall risk of a static 

item is acceptable or relatively low 

when compared to other evaluated 

static items, no further mitigation 

measures may be necessary. However, 

since the whole idea revolves around 

tackling static items with the highest 

risk, more often than not some form 

of mitigation has to be considered. For 

effective risk reduction you can devise 

measures that limit the consequences 

and probability. To prevent future 

failures, it may be necessary to repair, 

modify, or renew parts of the installation, 

or to shorten the time interval 

between turnarounds or regular inspections. 

RBI can potentially be used as a steppingstone 

to condition-based monitoring 

as it provides an organization with 

the ability to gain valuable insight into 

their highest risk items of static equipment, 

which is routinely inspected. The 

initial investments in condition-based 

monitoring, including complex sensors 

and software packages, can be quite 

high, but can also be seen as a form of 

mitigation. 

Step 6: Reassessment 

The previous steps are all based on 

particular moments. As time goes by, 

changes that could affect the probability 

or consequences of failure are inevitable. 

Therefore, it’s important that the 

facility has an effective Management of 

Change process that identifies when a 

reassessment is necessary. The RBI reassessment 

concerns: 

• Inspections 

• Process and hardware characteristics 

• Maintenance (strategies / approaches). 

Many deterioration mechanisms are 

time dependent. Typically, the RBI 

assessment will project deterioration 

at a continuous rate. These rates 

may vary over time. Through inspection 

activities, the average rates of 

deterioration may be better defined. 

Some deterioration mechanisms 

are independent of time, e.g., they 

occur only when there are specific 

conditions present. These conditions 

may not have been predicted in the 

original assessment but may have 

subsequently occurred. Inspection 

activities will increase information 

on the condition of the equipment 

and the results should be reviewed to 

determine if a RBI reassessment is 

necessary. 

By creating and using predictive 

degradation models, combined with 

routine inspections and testing as efficiently 

and effectively as possible, 

RBI allows for long-term monitoring 

of static assets. In the context of 

continuous improvement being 

embedded in the RBI approach, the 

RBI process should be repeated after 

the cycle has taken place and the necessary 

investments in risk reduction 

have been made. Keep in mind that 

there will always be some degree of 

residual risk as all risks can never be 

completely eliminated. The aim of 

RBI is to reduce this residual risk to 

an acceptable level. 


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DIGITALISATION 

Industrial AI 

Part III: AI Factory for 

Operation & Maintenance 

RAMIN KARIM, DIEGO GALAR 

AND UDAY KUMAR, 

Division of Operation and 

Maintenance Engineering Luleå 

University of Technology 

All over the world, industries are boarding the 

digital train and exploiting the power of new 

and emerging technologies, such as Artificial 

Intelligence (AI), Deep Learning (DL), Machine 

Learning (ML), Big Data analytics, and Digital 

Twins, to enhance competitiveness and make 

operations competitive and economically viable. 

HOWEVER, transitioning to digitalisation 

to manage the complex engineering assets 

effectively and efficiently is a challenging 

task, as it requires the use and 

integration of various tools, technologies 

and models. 

The move to implementation of 

new and emerging technologies requires 

availability and accessibility to 

data and information, as well as domain-specific 

physics-based models 

to gain deeper insight into industrial 

processes, thus facilitating correct de- 



Figure 1. Generic concept of 'AI Factory' 

Figure 2. AI Factory defined Analytics Autonomy Level (AAL), (Karim et al, 2021) 

cision-making by considering all relevant 

and contextual information. In 

this transformation journey, data and 

models are considered digital assets 

(ISO55000) that reflect the current 

health of an asset; and processing such 

data permits a glimpse into the future 

trends of an asset’s health and its performance 

from a life cycle perspective. 

However, to establish effective and 

efficient asset management using a 

life cycle perspective and to exploit 

the power of new technologies, we 

need appropriate concepts, methodologies, 

tools, and technologies, 

especially given the requirements 

of different types of assets and the 

associated domain-specific challenges. 

More specifically, industries 

are facing challenges developing and 

implementing AI to increase the level 

of autonomy in maintenance, especially 

in the contexts of maintenance 

and repair needs assessment, task 

planning, resource allocation, task 

execution, performance assessment, 

and continuous improvements, whilst 

considering the relevant contextual 

information. 

The digital transformation of industry 

requires deeper insights into the 

operational domain of assets, prevalent 

business and governance models, 

and regulatory regimes to adequately 

address the challenges. From a digital 

asset management perspective, the 

main challenges are related to source 

integration, distributed computing, 

content processing, and cybersecurity. 

A technology platform could bridge 

research findings with innovative solutions 

that are scalable for the transformation 

of industries. These platforms 

could provide digital pipelines between 

data providers and data consumers, 

as illustrated in Figure 1. We label this 

concept the AI Factory (AIF). Each 

pipeline represents a set of orchestrated 

activities aimed to extract, transfer, 

load, and process data between the 

provider and the consumer; pipelines 

are configurable entities, which can 

utilise a palette of technologies for 

communication, storage, and processing 

to enable context-adaptability and 

meet the users’ requirements. The selection 

of appropriate technologies for 

each pipeline should be based on the 

context-specific requirements such as 

requirements for scalability, authentication, 

and authorisation. 

To facilitate the development of 

line-of-production and to select the 

appropriate technologies in the AI 

Factory platform, an Analytics Autonomy 

Level (AAL) needs to be developed; 

see Figure 2. The AAL aims to 

identify the maturity level of analytics 

in an organisation aim 



Figure 3. AI Factory’s model-driven and data-driven architecture 

ing for industrial AI. When the digital 

and AI maturity of an organisation are 

identified, a roadmap can be developed 

to support a data-driven approach based 

on the current needs and future opportunities. 

AI Factory (AIF:) Content and 

products 

The AI Factory is a virtual technology 

platform which integrates technologies 

and tools to facilitate engineering 

and business solutions by exploiting 

the power of data and data-driven 

technologies. Of course, organisations 

have been using data in their decisionmaking 

processes for centuries, but 

an AI factory platform also considers 

contextual information enhancing the 

quality of decision-making process. 

Furthermore, the AI Factory also integrates 

and uses models based on physic-of-failure 

to develop engineering and 

business solutions through a use-case 

approach. 

Digitalisation and the emerging 

technologies have made an enormous 

difference to the way data is collected 

DIGITALISATION AND THE 

EMERGING TECHNOLOGIES 

HAVE MADE AN ENORMOUS 

DIFFERENCE TO THE WAY DATA 

IS COLLECTED AND USED FOR 

DECISION-MAKING. 

and used for decision-making, and this 

provides the foundation for the structure 

of AI Factory and its architecture. 

First, digitalisation has enabled a 

set of digital infrastructures in society, 

industry, and transport that can be 

used in various contexts, such as sharing 

data and models. Second, computing 

technologies have given society, 

industry, and transport (among others) 

the ability to run on a digital infrastructure. 

Third, Internet of Things (IoT) 

and sensor technology have augmented 

human senses with the capability to 

sense and measure new phenomena, 

provided as data flowing throughout 

the digital infrastructures. Finally, AI 

has enabled us to discover knowledge 

from a vast amount of data, using a 

digital infrastructure and computing 

power. These four ingredients have 

changed the concept of the data-driven 

approach. 

Today, the data-driven approach is 

associated with complex fact-based 

decision-making processes using digital 

infrastructures, distributed computing 

(cloud/edge), sensor data, and 

augmented analytics empowered by 

AI. The fundamental idea of the datadriven 

approach is the same: making 

decisions on facts (data). 

In many areas, including the maintenance 

of transport systems, the datadriven 

approach promises better accuracy 

via fact-based decision-making, 

leading to operations excellence and 

system sustainability with respect to 

economy, technology, and the environment. 

However, the mere availability 

and accessibility of data provided by an 

appropriate information logistics system 

is not sufficient for good maintenance 

decision-making. Data needs to 



be processed, analysed, and interpreted 

using digitalisation and AI technologies. 

Context awareness is another important 

aspect which needs to be considered 

in maintenance decision-making. 

Context-awareness is the ability to 

sense the context in which a decision 

will be made, and adapt the analytics 

and information logistics to support 

the decision-making process. 

The following essential aspects must 

be considered in a data-driven approach: 

• Information logistics, representing 

the digital infrastructure 

• Analytics, representing knowledge 

discovery through a set of algorithms, 

fed by data from the information 

logistic infrastructure 

• Context-awareness, representing 

a set of situations for which decisions 

will be made, i.e., the purpose 

of analytics. 

To improve the accuracy of analytic 

services, the AI Factory implements 

an architecture that integrates modeldriven 

and data-driven approaches in 

a seamless manner, as illustrated in 

Figure 3 

AI Factory for Operation 

and Maintenance 

The ongoing digitalisation and implementation 

of AI-technologies in operations 

and maintenance depend on the 

availability and accessibility of data 

for geographically distributed systems. 

Lulea University of Technology has 

created AI Factory (AIF), a seamless 

platform built on loosely coupled storage 

and computing services for data 

sharing. AIF is a set of smart cloud/ 

edge-based data services that aim to 

accelerate digitalisation in industry 

and transport. AIF services provide capabilities 

such as acquisition, integration, 

transformation, and processing 

of asset-related data across all stakeholders, 

where services can be invoked 

on-premise or in multiple cloud-based 

environments. AIF is applicable to 

numerous industries. For example, 

AI Factory for Railway integrates technologies 

and business requirements 

and other historical and contextual 

information to gain deeper insight into 

the performance of railway assets. 

AIFs overall architecture rests on 

four main pillars; see Figure 1: 

• A technology platform 

• A digital governance platform 

(eGovernance) 

• A communication platform 

• A coordinating platform 

An important aspect to consider when 

developing decision-support solutions 

based on AI and digital technology is 

the users’ experience. The user experience 

(UX) design process aims to 

create relevance, context-awareness, 

and meaningfulness for end-users. In 

railway contexts, applying a humancentric 

model in the development of 

AI-based artefacts will enhance the 

usability of the solution, with a subsequent 

positive impact on decisionmaking 

processes. Therefore, the 

analytics services in AI Factory are 

combined with other technologies such 

as Virtual Reality (VR), Augmented 

Reality, Mixed Reality (MR), and so on, 

to improve user experience (UX) in AI 

implementation.

ASSET MANAGEMENT 

Strategic Success for Shutdowns – 

The Importance of C-Suite Support 

How do the C-Suite leaders ensure they have given the proper 

direction and support, so that plant(s) have a clear message and 

guidance on what STO’s mean to their company’s success? 

IF YOU ASK MOST C-SUITE leaders if 

they have long range planning, they will 

commonly respond with a resounding 

“YES”. This article’s intent is to look at 

the inclusion of Strategic Asset Integrity 

as part of the plan. Many times, it is left 

to the plants to figure out what should be 

done to “keep it running”. 

Challenges without 

C-Suite Support 

This can create problems from the very 

beginning of the process, due to overall 

company alignment: 

• Plants do what they think is right 

with little to no direction 

• They consequently do it differently 

from one plant to another 

• They do not measure success the 

same way 

• They have budgets and timing “edicts” 

handed down, with little to no input 

from plant subject matter experts, as 


ALAN WARMACK, 

DIRECTOR/PARTNER, 

MARSHALL INSTITUTE 

INC. 

to the work scope needed in the plants 

to ensure we have the appropriate 

time and budget to accomplish the 

right work on the right assets. 

• Start dates are moved backwards 

and forwards, with no thought of 

the impact to the STO plan that 

the plant personnel are trying to 

work to, nor how it affects procurement 

issues such as locking 

contract resources, materials, etc., 

which cost additional money and 

can have adverse effects on the 

performance of the STO’s objectives 

being achieved. 

Industries call these events by different 

names, typically based on which business 

sector you fall in. Shutdown is typically 

used in manufacturing and mining, Turnaround 

is typically used in the energy sector, 

and Outage is typically used on utilities going 

down (HVAC, Compressed Air, etc.)


Regardless of what you call it, an STO is 

essentially a predetermined period, when 

a defined set of planned activities are to be 

completed. The work to be accomplished 

should be activities that can only be performed 

while the plant is out of service. 

Personnel in the plant are the ones who 

struggle to make these events successful. 

The question we should ask is why do they 

struggle to create a plan that will successfully 

achieve the end results that are expected? 

Oftentimes, it can be because they 

don’t know what corporate leadership’s 

expectations are. 

It is very common to see the following 

types of issues among companies in a wide 

variety of industries. Below is an example 

from the Petro-Chemical and Refinery Association. 

* 

• 95 % of post-SD recommendations 

are not implemented 

• 90 % of SDs do not reach expected 

goals 

• 90 % of SDs have scope creep of 

between 10–50 % 

• 80 % of SDs exceed planned costs 

by 10% or more 

• 50 % of SDs overrun 

Importance of C-Suite 

Support 

It is critical that STOs are included in C- 

Suite discussions when determining long 

range plans. Most C-Suite leaders will 

ensure they have looked at future trending 

expectations, such as sales and marketing, 

new product development, major expenditures 

for plant expansions, brownfield 

plant closures, bringing greenfield plants 

online, etc. 

These long-range planning meetings 

should include plant management, which 

will provide updates related to long term 

asset conditions of the existing assets within 

their plants, which would assist with an 

overall understanding of the company’s 

strategies over the next 5 – 10 year’s plans. 

Which assets will need replacement? What 

regulatory items will dictate certain timing 

of outages at the site? 

This is a great opportunity for the 

company’s leadership to develop company 

goals and drivers, with input from 

the plant’s leaders, thus gaining an overall 

alignment as to why the company performs 

STO’s, what their importance to the health 

of the company is, and how they support 

the Corporate Vision and Mission. Only 

then can the plant leaders can return to 

their plants with a clear method of communication 

on what, why, when and how. 

Integrated Activity Plan 

As C-Suite begins this inclusion to their 

long-range planning efforts, more effective 

decisions will made in direct support of 

their company’s future goals and objectives 

in coming years. This is called Integrated 

Activity Planning. (IAP) 

As the Integrated Activity Plan is formulated, 

with input from all functions of 

the company, decisions can then be made 

in a more rational and productive manner 

for all functions. Examples of corporate 

leadership supporting the company’s longterm 

strategies include which plant(s) 

should be taken down at certain times of 

the year, what sequence should the plants 

go down, how to best support the market 

by transitioning product flow in advance 

of a plant STO event to minimize sales 

impacts, what major expenditures will 

LONG-RANGE PLANNING 

MEETINGS SHOULD INCLUDE 

PLANT MANAGEMENT, WHICH 

WILL PROVIDE UPDATES 

RELATED TO LONG TERM ASSET 

CONDITIONS OF THE EXISTING 

ASSETS WITHIN THEIR PLANTS. 

be made, based on the function of each 

facility, etc. These decisions should be influenced 

by the plants providing their predicted 

work scope, based on their plant’s 

individual long-range plans. This will assist 

in developing viable timing of events, as 

well as a clearer understanding of future 

budgeting projections. 

Once the Plant Managers have this 

corporate plan, they can be much more 

confident in making the proper decisions 

on how their STO events will be managed, 

once back at their sites. They will be able 

to develop their plant’s goals and drivers, 

which will directly support the long-range 

corporate plans. This will create significant 

synergies between the company and the 

plants. Corporate leaders will have greater 

confidence that the sites are now managing 

each site’s STO based on goals and drivers 

in support of the company’s vision and 

mission. 

Corporate Process Guide 

After development of the Integrated 

Activity Plan, it is critical that corporate 

leadership supports building a Corporate 

Process Guide that will be used as 

guidance to all plants on what a good 

STO should look like. This will ensure 

all plants have a clear, standardized 

path to follow. Of course, each plant will 

have to make minor changes based on 

their size and complexity, but the point 

is, while they may have to perform the 

functions differently, they should be doing 

these key functions. This provides a 

good means for corporate to better compare 

plant STO performance and results 

when plants are being reviewed. 

Corporate leadership should support 

building the Process Guide by including 

plant leadership / subject matter 

experts. This allows each plant to have 

input as to the content of the guide, 

increasing a sense of ownership, as opposed 

to an “edict” being passed down 

from Corporate. They are the experts 

on these events. They know what works 

and what does not work. Including 

them will provide a much better process 

which will create alignment between 

corporate and the plants, allowing a 

greater chance at proper implementation 

back at their site. 

In Summary 

These are considerations for the initial 

phase of STO development. This article 

touched on topics at a high level, and 

there are many other details which must 

be included, but it is meant as a sampling, 

which hopefully if implemented will 

assist in ensuring plant asset integrity 

is properly supported. This will create 

order for the plants, to support the company 

with increased asset integrity to 

support greater production, revenue, and 

decreased spending. 

When strategic plans are developed at 

the C-suite level, individual plants gain 

clarity on their ultimate targets. This clarity 

provides them the ability to set their 

plant-level STO strategies in support of 

their company’s vision. 

Not only does strategic clarity empower 

each site or plant to execute shutdowns 

successfully, it can and arguably should 

increase safety while optimizing duration 

and cost. 

*WORLD CLASS Shutdown MANAGE- 

MENT LESSONS BENCHMARKING, 

National Petrochemical and Refiners Association, 

David W. Fontenot 



Benchmarking 

maintenance 

cost in a vacuum 

It is very common that a company or a plant tries to benchmark 

its maintenance cost. The maintenance cost/ton or maintenance 

cost/unit is typically the benchmark of most interest. 

BUT THE EFFORT to benchmark the 

maintenance cost/unit produced is quite 

futile if it is the sole focus. Why? First, 

because the maintenance cost is extremely 

hard to compare between plants 

due to variability of the following: 

• Company definition of maintenance 

cost 

• Local tax laws 

• Currency exchange rate variations 

(If comparing internationally) 

• Company practices (and ethics) 

• The maintenance debt 

• Difference in production flow 

• Difference in equipment selection 

and engineering before plant 

start up 

• The age of the equipment 

Second, the maintenance cost by itself 

is not very interesting or even very relevant. 

Perhaps it can be compared with 

baseball pitches or ice hockey slap shots, 

which are roughly the same speed. 

A good pitch or slap shot is a tad over 

100 mph. Many are obsessed with measuring 

the speed of a pitch or slap shot, 

but is it relevant? A little bit, but neither 

baseball nor hockey is about fast pitches 

and hard shots, it’s about winning the 

game. Similarly, the name of the game for 

any company in the world is one thing, 

profit! That is why companies exist. 

A paper mill in Canada had one of the 

highest maintenance costs ($ Mtce cost/ 



ton) in the industry, but they were the 

most profitable ($ profit/ ton). The mill 

spent money on maintenance: 

• Precision repairs 

• The correct materials 

• The right maintenance tools for 

everyone 

• Trained their people 

• Organized the technical data such 

as Bill of materials, equipment 

registers, added work order history, 

etc. 

All these items cost money, mostly temporarily, 

and increased the maintenance 

cost. What was their return on investment? 

They achieved exceptional 

equipment reliability and could sell 

more product. Therefore, the revenues 

increased a lot more than the total cost 

even though their maintenance cost was 

relatively high. 

The point is that the maintenance 

cost by itself is not very interesting to 

benchmark, and in some cases, completely 

misleading for any relevant business 

decision. Sub-optimizing by performing 

a maintenance cost analysis by 

itself is a mistake that can lead to poor 

decisions by top management. 

The reality I see is that many maintenance 

organizations are completely 

focused on reducing maintenance cost 

and/or to keep within the maintenance 

budget. But it is important to understand 

that if the product manufactured 

can be sold with a profit margin, meaning 

that sales price/unit is larger than 

the cost/unit to produce it, reduced 

downtime (therefore increased production 

time) will be more important than 

cutting the maintenance cost. 

It should be mentioned that there are 

other factors in an organization's operation 

that are important beside profit and 

cost. Those factors are not explored in 

this article since the focus is on maintenance 

cost. Some examples of other 

important factors are: 

• Quality of the product. There are 

few things more expensive than 

poor quality. 

• Safety 

• Environmental compliance and 

awareness 

• Follow all regulations including 

over and above safety and environment 

• Leadership ability 

• Skills 

• Ethics 

• Culture (perhaps just an outcome 

of all above, but still) 

Is Maintenance Cost / Estimated 

Replacement Value (ERV) 

the right benchmark to use? 

A popular benchmark is the Maintenance 

cost / Estimated Replacement 

Value (MC/ ERV). Many consultants 

have promoted this as a great number 



to compare between plants. Some 

have even claimed that 2 percent is 

“best practice”, or “world-class”. The 

2 percent is often used regardless of 

what industry that is being benchmarked. 

Referring to the variance 

in the numerator (if a/b=c, then a is 

the numerator i.e., the maintenance 

cost) that we have extensively covered 

above, we know that the maintenance 

cost varies greatly. Trying 

to benchmark the MC/ERV between 

industries is preposterous and incompetent. 

Take a simple example of a conveyor 

belt that transports iron ore 

outside in a hot, humid environment 

to a similar belt that transports wood 

chips indoors in a northern paper 

mill. The wear of the belt that carries 

rock in the sun will be more than the 

one that carries wood chips indoors. 

Therefore, the maintenance cost will 

be higher. A pump that pumps room 

temperature water wears differently 

compared to one that pumps bitumen 

in the oil sands. 

Adding to the uncertainty of Maintenance 

Cost/ Estimated Replacement 

Value, is the ERV itself. Few 

plants have a correct number for the 

estimated replacement value since 

the actual depreciation of the assets 

hasn’t been kept up correctly. 

Is Maintenance Cost 

Useless to Benchmark? 

No, it is not useless to benchmark 

maintenance cost. Maintenance 

cost is an important indicator 

for a plant’s performance. 

But, the maintenance cost must 

be put in perspective with all factors 

described above. The age, 

past maintenance performed, 

the initial investment quality 

(Life Cycle Costing), and all other 

factors must be analyzed. It 

would be impossible to make an 

analysis that encompasses all the 

important factors that include 

maintenance cost. Therefore, the 

number shouldn’t be analyzed as 

a “stand-alone” number. 

What should, and can, be 

analyzed is the maintenance 

cost performance over time in a 

specific plant without comparing 

it to other plants. The cost 

should be analyzed together with 

a set of additional “balancing” 

KPIs such as Overall Production 

Efficiency (OPE), total cost, revenue, 

etc. 

WHAT SHOULD BE THE MAIN 

GOAL FOR A MAINTENANCE 

MANAGEMENT IF IT’S NOT REDUCING 

MAINTENANCE COST? 

Let’s look at the maintenance cost from 

one more angle. If reducing maintenance 

cost is the key goal for a maintenance 

department, it is a very easy goal to 

achieve. Simply stop doing any maintenance 

work and your cost will be zero, 

goal achieved! Some may say that the 

idea above is silly, no mine, plant or mill 

would do that. Of course not, but why 

wouldn’t they? 

If you stop doing maintenance work, 

the equipment and the plant stops running, 

and your revenue will go to nil. 

Plants should define what the outcome 

of the maintenance department should be. 

It is a critical discussion to have because 

it changes the whole approach to maintenance 

in an organization. The product of 

maintenance work should not be service, it 

is not repair, it is not cost. The outcome of 

maintenance work is equipment reliability. 

If the goal for maintenance is to deliver 

equipment reliability instead of reduction 

of maintenance cost, high reliability will 

reduce the cost over time, and you will get 

the best of both worlds. 


15–18 MARS 2022 · SVENSKA MÄSSAN, GÖTEBORG 

SMART MAINTENANCE 

MEANS KNOWING WHAT HAPPENS NEXT 

UNDERHALL.SE 

#UNDERHÅLLSMÄSSAN


Ways in Which 

Manufacturers 

Can Manage Their 

MRO Inventory 

Modern facilities are focused on streamlining production processes while lowering 

operational costs and eliminating equipment downtime. Managing inventory for 

different departments within an organization can be nerve-racking. Facilities must 

maintain sufficient stocks of replacement parts, tools, safety supplies and other 

consumables needed for Maintenance, Repair and Operations (MRO) activities. 

BRYAN CHRISTIANSEN, founder and CEO of Limble CMMS. 

MRO INVENTORY represents a sizable percentage 

of an organization’s annual budget. 

Companies are using different strategies 

in a bid to optimize MRO inventory. A lean 

MRO inventory ensures that companies 

eliminate obsolete stocks, retain essential 

supplies at safe levels, and reduce MROrelated 

expenses. 

Production facilities vary in size and 

complexity. As a result, the number of consumables 

required to keep each facility running 

varies. Below are ways for companies 

to manage MRO inventory to match the 

changes in production technology and complement 

proactive maintenance activities. 

1) Vendor-Managed Inventory 

(VMI) 

Through this initiative, the company enlists 

the services of a 3rd party partner. The 

service provider is granted full authority 

to manage inventories at the customer’s 

location. The vendor replenishes stocks 

of essential products, tools or consumables, 

retaining them at the desired level at 

any given time. In the VMI approach, the 

supplier is at liberty to alter resupply decisions 

rather than relying on fulfilment of 

customer-initiated orders. 

Vendor-Managed Inventory relies on 

framework agreements that give vendors 

clearer visibility of facility-level stock de- 



mands. By delegating stock-control activities 

to an independent service provider, inhouse 

employees find ample time to focus 

on their core responsibilities. 

By gaining full access to the facility’s 

inventory, the vendor can extract sufficient 

data to forecast demands. This data 

reduces reliance on manually prepared 

purchase orders, which are subject to repetitive 

corrections and reconciliations. 

Visibility in the downstream consumables 

eliminates the probability of stockouts 

while facilitating significant cost 

savings. Fewer people will be required 

for raising, validating and reconciling 

purchase orders, leading to savings in 

admin-related costs. In addition, companies 

incur reduced costs required for 

warehousing and related resources. With 

this model, companies are billed based 

on the consumed stock rather than the 

supplied stock. 

VMI initiative has its drawbacks. 

Companies tend to build trust around 

the approved vendors, making it difficult 

INNOVATIVE MRO INVENTORY 

MANAGEMENT STRATEGIES 

ENSURE THAT COMPANIES 

PAY FOR WHATEVER IS USED 

AND NOT EVERYTHING THAT IS 

SUPPLIED BY THE VENDOR. 

to source products from other vendors 

leading to a compromise in the quality 

and pricing. For sensitive production facilities, 

the VMI model is inapplicable as 

non-employees would have to get access 

to critical inventory data. 

2) CMMS and ERP solutions 

for managing inventory 

Maintenance operations within the production 

floors have been improved, thanks 

to the robust Computerized Maintenance 

Management Systems (CMMS). They can 

work in collaboration with Enterprise Resource 

Planning (ERP) solutions to ensure 

the availability of parts and essential supplies 

required to ensure smooth business 

operations. 

CMMS solutions that come with an 

inventory module provide tools for organizations 

to manage spare parts inventory. 

They help to allocate parts, merging their 

usage to work orders and updating future 

purchase orders. Mobile CMMS solutions 

often contain scanning utilities that enable 

technicians to account for every part and 

tool used in any MRO process. 

CMMS and ERP solutions can hold a 

massive amount of data enabling companies 

to maintain a detailed record of their 

MRO inventory. By serializing essential 

supplies, the technicians can search and 

identify the location of parts in a warehouse. 

They enable any technician to 

participate in inventory management by 

generating purchase orders whenever necessary. 

Maintenance planners can track incoming 

inventory and use tracking details 

to plan for upcoming maintenance tasks or 

reorder supplies. Based on periodic data, 

technicians can prioritize vendors based 

on the quality of their products, cost, and 

lead times. 

Using CMMS and ERP to manage 

MRO inventory gives in-house employees 

full control over stock levels. The system 

generates alerts whenever the levels fall 

below a prescribed level, providing enough 

insights to enhance long-term inventory 

planning. They are effective for large-scale 

manufacturing facilities with complex 

MRO supply requirements. 

3) On-site kiosks and vending 

machines 

Most organizations have centralized 

warehousing facilities to keep stock of 

essential supplies. Employees engaged in 

MRO visit these stores to receive tools, 

parts, protective equipment, and production 

supplies. Store personnel keep records 

of all outgoing products, triggering 

reorders from multiple suppliers when 

stocks decrease. The whole process is 

time-consuming and may impact normal 

production. Vending machines and onsite 

kiosks can help resolve challenges 

associated with centralized storage 

units. They can be run by companies or 

suppliers, who later bill the organization 

based on consumption. 

Vending machines are programmed 

to dispense required MRO items round 

the clock while retaining an accurate record 

of all transactions. The machine is 

configured for access by specific employees 

with stringent limits on quantity and 

frequency of use. The restrictions ensure 

that inventory is properly utilized, preventing 

unnecessary waste. The vending 

machines generate alerts when levels fall 

below pre-set limits. Data collected by 

the machines are utilized in forecasting 

future demand. 

Vending machines are critical for 

production facilities with geographically 

dispersed teams. They reduce commuting 

costs and have advanced control 

tools to prevent wastage. That being said, 

any restrictions have to be carefully set 

as one doesn’t want to limit access to 

tools and parts needed to perform unplanned 

emergency maintenance. 

Final remarks 

Modern manufacturers are focusing on 

sustainable production processes, proactive 

maintenance, and on reducing operational 

costs. Companies must prioritize 

the availability of essential supplies for 

all production floor teams. 

Innovative MRO inventory management 

strategies ensure that companies 

pay for whatever is used and not everything 

that is supplied by the vendor. 

Depending on the size of a facility, a company 

can choose one or a combination 

of the above methods to optimize MRO 

inventory. 


CERTIFICATION 

Maintenance people, get certified! 

The 6+1 European maintenance organizations work together as partners in Erasmus+ 

funded project “Qualification, Validation and Certification of Maintenance Personnel” 

to move maintenance qualifications, certifications and validations in Europe forward. 

EUROPEAN INDUSTRY needs to work 

smarter to increase added value, 

profitability, and competitiveness. 

Working smarter with a combination 

of automation, digitalization and 

maintenance allows higher availability 

of the production processes. New 

technologies like robots, autonomous 

vehicles, electromobility, automated 

warehouses, conveyor systems - 

combined with booming and developing 

industries like e-commerce, e-shops and 

online groceries with same day delivery, 

distribution and public transportation 

- constitute a number of challenges for 

maintenance and asset management, 

a fact we already knew. These trends 

accelerated with the COVID19 

pandemic, which brought havoc into 

labour markets in many countries. All 

this resulted in increasing demand 

for highly-qualified maintenance 

personnel. 

Well, new technologies - like augmented 

reality, 3D printing, IoT, AI and 

machine learning - also create interesting 

new opportunities in maintenance, 

transforming traditional maintenance 

work into a whole new experience for 

our personnel. This also contributes to 

much higher expertise expected from 

the people employed in maintenance 

processes today. 

These trends in maintenance, however, 

are not adequately emphasized in 

the vocational education as well as the 

academic world. This is one of the most 

important issues for the EFNMS. Higher 

education is mostly orientated on 

development of new technologies and 

products and therefore there is a need 

for theoretical and practical vocational 

training. 

The guidelines from the European 

Centre for the Development of Vocational 

Training, CEDEFOP, focuses on 

the validation of non-formal and informal 

learning. The total learning results 

are of the highest interest for development 

of an organization. 

Smart and cost-effective production 

including maintenance will make 

great effects on the competitiveness for 

European industries. Therefore, good 

examples are a prerequisite for competence 

development and new business 

opportunities. 

Erasmus+ project: Certification and 

validation meet new qualification needs 

One of the main aims of this Erasmus+ 

project was to refresh the structure of 

maintenance qualifications and update 

it to reflect current trends in technologies 

and techniques used in industry and 

maintenance. The basic objective was to 

form a staircase for qualification, validation, 

and certification for personnel 

within the maintenance area. The outcomes 

can be used for recruiting maintenance 

personnel for companies and more 

importantly, for competence development 

and to support lifelong learning. 

On top of that, the results of the 

work of the 6 international partners 

and EFNMS contribute to the European 

maintenance certification process 

by creating, fitting and finetuning a 

number of validation and certification 

questions and tasks. The EFNMS certification 

for Maintenance Managers, Engineers 

and Technicians will be in this way 


CERTIFICATION 

STRUCTURE OF THE 

VALIDATION TEST 

Figure 1. EQF levels compared with achieved education and maintenance personnel roles 

Table 1. Competence requirements for maintenance professionals of eqf levels 4, 5 and 6. 

boosted to reflect current qualification 

needs for maintenance personnel in 

Europe. 

With the above explained ideas in 

sight, the priorities of the project were 

set to develop: 

• A common platform for the 

qualification, validation, and 

certification of maintenance personnel 

with measurable learning 

outcomes. 

• Detailed qualification requirements 

for maintenance personnel 

• System for mapping individual 

competencies to the qualification 

needs. 

The project was divided into two consecutive 

phases: 

• Description of needed qualifications 

with measurable learning 

outcomes 

The European Federation of National 

Maintenance Societies (EFNMS) has built 

up a database that shape the validation 

system. It is based on English questions 

which are then translated into local language. 

This allows cross-border comparison 

of competences. A validation for certificates 

in Hungary can be compared to a 

certificate in Sweden. Everything is done 

under the auspices of the EFNMS. For 

EFNMS certificates, the Certification Committee 

is responsible for the requirements 

and the way to operate the database. 

Locally, each country's maintenance association 

manages the validation systems. 

The international database is divided into 

seven subject groups with 26 subgroups. 

Validation tests follow this structure. 

Every subject has a number of questions. 

The number is determined by the importance 

of the subject in the professional 

role. If you are going to measure a person's 

capability, at least 10 questions are 

required on the subject and then there 

should be at least 30 questions to choose 

from. 

Each test involves a specific number 

of questions from the database. The 

number of questions per subject is determined 

by the importance of the subject 

in the professional role. The system randomly 

chooses how the questions come 

in the test. This allows test takers to sit 

next to each other during the test. 

The questions have four to six 

answers, where one answer is right. Some 

questions contain pictures or diagrams, 

where the candidate is required to select 

the right answer. When the test is completed, 

the evaluation is quick and automated. 

The test taker receives a printed 

result. 

The same subject report and classification 

are available for all occupational 

levels. Here the EU approach follows 

the structure of the European Qualification 

Framework training system, where 

Maintenance Managers are at EQF level 

7, Maintenance Engineers EQF level 6, 

Maintenance Technicians EQF level 5 and 

Maintenance Mechanics, Maintenance 

Electricians and Automation Electricians 

at EQF level 4. 

For more information about the project 

and its outputs, please follow the 

project dedicated website http://www. 

cemaint.eu. The complete structure of 

qualifications for EQF levels 4, 5, 6 and 

7 is also available on the website. 


CERTIFICATION 

• Development of validation 

questions for EQF level 6 and 

7 based on the structure of 

required qualifications. 

• There are four categories of 

maintenance personnel addressed 

in the project: 

• EQF level 7: Maintenance 

managers, 

• EQF level 6: Maintenance supervisors 

and engineers, 


technician specialists, 


mechanics, electrical and automation 

electricians, 

EQF stands for European Qualification 

Framework. The EQF system 

concerns eight reference levels 

describing the learning outcomes – 

what a learner knows, understands 

and is able to do. Levels of national 

qualifications will be placed at one 

of the EQF reference levels. 

The EQF levels ranging from 

basic (Level 1) to advanced (Level 

8) can be roughly assigned to levels 

of education (academic, secondary, 

primary) or achieved degrees 

and diplomas (as shown in the 

diagram below). However, what 

really defines various EQF levels 

is the learning outcomes, not an 

education level achieved by an individual. 

For personnel in the field of 

physical asset management and 

maintenance, EQF levels between 

3 and 8 are generally expected. 

EQF Level 3 is sufficient for Mechanics, 

usually the professionals 

on the lowest level in the maintenance 

organization structure. 

Multiskilled Mechanics are professionals 

with significant experience 

and flexibility to be able to perform 

various advanced tasks in the field 

of maintenance. For Multiskilled 

Mechanics EQF level 5 is considered 

adequate. Maintenance Managers 

then typically recruit from 

professionals on EQF levels 6, 7 or 

8, with suitable academic training 

combined with sufficient experience 

with maintenance processes. 

Besides managerial functions, 

EQF level 6 also includes teachers 

at vocational schools educating 

maintenance technicians and mechanics. 

EUROPEAN COOPERATION OF SIX COUNTRIES AND ONE 

FEDERATION 

Six team members - national maintenance societies federated within the EFNMS 

- have been involved in this project working hard to introduce a complete system 

for qualification, validation and certification for maintenance personnel throughout 

the 23 member countries in the EFNMS. All project partners are members 

of the EFNMS, European Federation of National Maintenance Societies, and are 

responsible for maintenance developments in their own country. 

By mixing experts and technical specialists from different countries and backgrounds, 

a truly creative and forward-thinking team was formed. 

The leading project partner responsible for project management was the Swedish 

Maintenance Society, SvUH (Riksorganisationen Svenskt Underhåll). SvUH is 

a non-profit organization owned by the industry, energy sector, universities and 

maintenance suppliers. More than 140 companies, organisations, universities and 

schools are represented as members. 

The Slovenian Maintenance Society (DVS) and the University of Maribor are 

responsible for training in maintenance and the associated certifications. Thanks 

to the great experience of handling several Erasmus+ projects Slovenia was an 

important partner in this project. 

The Hungarian Maintenance Society, Magyar Ipari Karbantartók Szervezete, 

MIKSZ, was founded by professionals involved in industrial management, service 

and equipment distributors, and universities. 

The Czech Maintenance Society (CSPU, Česká společnost pro údržbu, z.s.) is a 

non-profit association for individual professionals and corporate members aiming 

to improve industrial and facility maintenance. 

The Finnish Maintenance Society, Promaint, is an organization of members 

from industrial production and maintenance departments. Promaint has approximately 

1 400 members, of which about 200 are companies or communities. 

Iceland is a new member in EFNMS and the Icelandic Maintenance Society, 

EVS, has a very active role in introducing new maintenance management solutions 

in the country. The members cover various industries and include aluminium 

smelters, power generating, engineering and IT companies. The project partner of 

the Erasmus+ project on behalf of EVS was DMM Lausnir. 

Last but not least, The European Federation of National Maintenance Societies, 

EFNMS, acted as an umbrella organization and a body of EU political and professional 

importance. 

The EFNMS is the initiative organization for developing qualification and competence 

for maintenance personnel in Europe. The idea for planned competence 

development came from the EFNMS Training and Certification Committees. 

The EFNMS has a leading role in validation and certification of European maintenance 

managers since the year 1993 and European maintenance technician specialists 

since the year 2005. Over the years more than 500 maintenance managers 

and more than 300 maintenance technician specialists have been certified in 

Europe. 

The EFNMS, as a leader for 23 national maintenance societies, also plays a 

key role in dissemination of this project´s results to establish maintenance as an 

important factor for European industry´s continued development and improvement. 

THE AUTHORS: 

TOMÁŠ HLADÍK, ČSPÚ, Czech Republic 

GUÐMUNDUR JÓN BJARNASON, DMM Lausnir, Iceland 

MARIA BRUS LUNDELL, SvUH, Sweden 

MIKAELA MALMRUD, SvUH, Sweden 

INGEMAR ANDREASON, SvUH, Sweden 

ILKKA PALSOLA, Promaint, FInland 

ISTVÁN PÁLL, MIKSZ, Hungary 

ZSOLT NYESTE, MIKSZ, Hungary 


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HOST


Integrating information 

for evidence-based 

decision-making 

In maintenance and asset management, decisions are sometimes based on a single 

key indicator or on a strong opinion. Evidence-based asset management encourages 

a broader consideration of different sources of information and knowledge and 

helps in making better decisions. Information often includes written text, and thus 

novel language technologies could extend the evidence base. 

JESSE TERVO, HELENA KORTELAINEN, PASI VALKOKARI, VTT Technical Research Centre of Finland Ltd. 

HELENA AHONEN-MYKA, Lingsoft Language Services Inc. 


EVIDENCE-BASED medicine follows the 

idea that the best and latest scientific 

evidence should be used in patient treatment 

decisions. The evidence-based 

decision-making logic has also been 

applied in engineering. Evidence-based 

Asset Management (EBAM) originates 

from the University of Toronto (https:// 

cmore.mie.utoronto.ca/). It could be said 

that EBAM considers devices as patients 

who are subjects of care – i.e., maintenance 

work and other asset-related 

tasks. These tasks always involve decision-making 

that requires a combination 

of information from different sources. In 

this way, the decision-maker will be able 

to justify their decision and back it up 

with several information sources. 

EBAM differs from data-based decision-making 

as it emphasizes practical 

expertise and seeks to take account of 

the underlying phenomena behind the 

numbers. In addition to hard statistical 

data, various soft data sources – such 

as tacit information – are considered 

in decision-making. Tacit knowledge 

is a form of information that is difficult 

to exploit, as it is gained through 

experience and can manifest itself in 

unconscious modes of action. However, 

the tacit knowledge of the field maintenance 

technicians can be brought out 

and put into words e.g., through various 

expert procedures such as risk analysis 

and reliability-centred maintenance 

(RCM). Such analyses are carried out, 

for example, in the context of planning 

and continuous improvement. 

Language technologies open 

up information in text format 

Valuable information can be obtained 

from written text, for instance from 

manufacturer manuals, factory diaries, 

and maintenance reports. The challenge 

with this kind of information, however, 

lies in the wide variety of possible expressions 

of language. As the information 

is often expressed in a different way 

than expected, matching of a decisionmaker's 

information needs with the 

content can be difficult. The content 

may include non-standard words, like 

incorrectly typed and incomplete words, 

or abbreviations. Moreover, words often 

have synonyms, or a document may 

not be available in the language of the 

decision-maker. 

All these challenges can be addressed 

with language technology tools. Linguistic 

analysis can reduce the variety 

of word forms by providing base forms 

and even correct errors automatically. 

Semantic analysis, based on machinereadable 

terminologies, can further 

enrich text with synonyms and other 

alternative terms, which can then be 

used in mapping content to information 

needs. Finally, language barriers can be 

overcome by machine translation. 

Information is scattered 

Especially for inexperienced workers, 

complex and seldom disturbances 

and faults, or new equipment and 

novel technology pose challenges. It 

would be useful to make use of several 

sources of information in fault diagnosis 

processes. Similarly, many asset 

management and resource management 

decisions, such as work planning 

and investment, should take into account 

not only the maintenance costs 

of the equipment, but also the reasons 

behind the cost increase, equipment 

history and design/procurement solutions, 

life cycle plans, spare parts availability, 

and employee experience. 

Accessing all these data sources at the 

same time is difficult because there are 

many different forms of information. 

Images and drawings are in graphical 

form, possibly as 3D models. Operating


and maintenance instructions can be in 

paper documents or practical knowledge 

and the experience of operators is only 

available orally; maintenance data may be 

incompletely reported, and various devicespecific 

analyses are archived in design 

files. The employees must use several information 

systems, and log in each system 

with a special user ID and password. Collecting, 

retrieving, and combining information 

from such system silos is difficult and 

time consuming. Often important information 

remains in the field and is never 

recorded anywhere. However, Industry 

4.0 – and more specifically digitization and 

cloud technologies – not only enable information 

to be digitized, but also improve 

the accessibility of information at every 

decision-making level. Data can be stored 

in a central database instead of individual 

silos (Figure 1). From the database it can be 

accessed by any system and anyone within 

the organization. 

On top of a central database, we can develop 

completely new types of information 

systems that support the operating model 

of evidence-based asset management. Such 

systems should present asset-specific data 

and analyses in a way that is visual and can 

be quickly embraced and combine it with 

expert knowledge. One approach is to create 

role-based user interfaces that support 

the most common tasks of different user 

groups in maintenance and asset management 

and thus reduce information overload 

by eliminating unnecessary data. 

Figure 1. Connectivity to a single central database enables 

building new products and interfaces for different user groups. 



Display only what the 

user needs 

The “SEED - Solid value from digitalization 

in forest industry” (www.seedecosystem.fi) 

project was launched in the autumn of 2019, 

and forest companies opened their doors to 

application developers and research. The 

SEED ecosystem develops methods and 

tools for business-driven asset management 

and productivity improvement. The 

SEED ecosystem aims to demonstrate, 

through rapid experimentations (POC), 

solutions to the challenges described by 

industry, which can be further developed 

through user feedback through the collaboration 

of ecosystem actors, possibly even 

towards commercial implementation. 

The interviews conducted in the SEED 

project highlighted challenges in the availability 

and use of asset and maintenance 

information, as well as in the utilization of 

tacit information. As part of the project, 

role-based views were developed for field 

maintenance technicians and maintenance 

managers to support evidence-based fault 

diagnoses and equipment replacement 

decisions (Tervo, J. 2021. Evidence-based 

decision making for maintenance and asset 

management. Master’s Thesis. LUT University). 

The user interface was designed 

based on the wishes and needs gathered 

from users, and its development continues 

in the SEED project. The POC application 

combines information from different information 

systems so that the user needs to 

spend as little time as possible searching for 

information (Figure 2). It is built around a 

universal and versatile search function that 

helps the users find just the information 

they need. Item-specific documentation 

and visualized system entries are readily 

available, and information can be searched 

by item name, location code, or keyword in 

description texts. 

The problem of data quality 

It is obvious that evidence-based decisionmaking 

requires high-quality data sources. 

However, event logs and descriptions are 

short at best, and too often information 

may not be passed on at all to maintenance 

technicians and subsequent shifts. There 

may be several reasons for this, such as 

rush, lack of expertise, technical difficulties 

with the systems, or insufficient incentives 

to make high-quality entries. Proper forms, 

data validation and user motivation are all 

key to ensuring complete and efficient information 

transfer (IEC 60300-3-2:2004). 

Users of the systems don’t like writing long 

description texts if they don’t see them 

bringing tangible benefits in their work. 

This problem should be solved with the use 

of an information system that incorporates 

the system entries and descriptions 

into decisions and rewards for quality 

entries later as problem-solving speeds up. 

On the other hand, the pursuit of higher 

quality records may also require a greater 

change in the workplace culture and incentives. 

Mobile interfaces, on-the-spot dictation, 

“speech-to-text”-technology, and 

other new technologies may also contribute 

to the quality and comprehensiveness 

of human recordings in the future. 

Future of evidence-based 

decision-making 

Businesses are becoming increasingly datadriven, 

and many kinds of dashboard and 

reporting solutions are emerging. Instead 

of static dashboards and standard reports, 

evidence-based asset management calls for 

specialized reports that can be produced ondemand, 

according to the information needs 

of the time. The data sources behind the 

reports need to be reliable, transparent, and 

accessible for rapid processing. Numerical 

data can be processed to KPIs and visualizations, 

and written text can be analysed with 

text mining and language technology tools. 

This approach already forms a strong basis to 

utilizing evidence in maintenance and asset 

management, but there is still lots of work to be 

done in finding the best possible ways to store 

and display different forms of knowledge. 

Linguistic 

analysis 

Figure 2. With the help of language technology, the search function works regardless of 

non-standard words, typing errors or abbreviations. 


VIBRATION ANALYSIS 

THERMAL IMAGING 

ULTRASOUND 

MEASUREMENT 

EYESIGHT – HEARING – SENSITIVITY 

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Maintworld Magazine 4/2021

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