Maintworld Magazine 2/2021

2/2021 www.maintworld.com 

maintenance & asset management 

30 Years 

of Adash p 6 

ASSET PERFORMANCE AWARDS NIGHT 2021 PG 10 STREAMLINING PROCESSES: WHY SHOULD WE? PG 22 MEASUREMENT TRACEABILITY FOR THE CONTROLLED ENVIRONMENT PG 36

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EDITORIAL 

DIGITALISATION – 

When the World 

Requires Realism 

YOU FIND AN INTERESTING WE- 

BINAR, article, or ad under the 

headline “Digitisation”. You 

participate in the event and 

get to know the subject enthusiastically. 

You paint in your 

mind incredible ideas on how to 

grow your business. In the end, 

however, you press your gaze 

to the ground and sigh deeply, 

because truly genuine solutions 

did not exist. There is still no 

moon in the sky -moment, but 

instead “let’s go brainstorming 

together”. 

There has been talk of digitisation ongoing for at least the last ten years, and 

various service solutions or products have been developed. In terms of maintenance, 

there have been discussions on analytics and simulation, artificial intelligence, 

machine learning, mobile solutions, and virtual and augmented reality. 

As a result, fault prediction, “pre-chewed” data with decision proposals (read 

artificial intelligence) and ready-made maintenance instructions for smart 

glasses have been promised. For the most part, digitalisation has been described 

as a “Superman,” but so far it has shown up more in its day job. 

In spring 2020, Covid-19 came and changed everything. Partly for an indefinite 

period of time, partly permanently. Earlier digitalisation was partly a 

choice, now it is forced. We are forced to find solutions and products that enable 

us to operate in changed circumstances. Solutions that can be implemented 

now, not in a five years’ time. Suddenly, digitalisation doesn’t have to be an allencompassing 

miracle from the planet Krypton. An honest and reliable engineer 

is enough. And here, I think, lies the whole thing. 

Ongoing digitisation discussion could be described as a 100-meter run. In 

the debate there has been a desire to run 100 meters in less than 10 seconds. 

This is possible, but the starting level has been at 13 seconds. Thus, we have to 

pull on our training clothes and humble ourselves to work towards the 10 second 

goal. It is good to know in which direction you are going, and there must be 

ambition. Personally, however, I also want to get a real picture and understanding 

of the present without the need to take off the mask. 

For example, would real-time and visualised data, online training spiced up 

with virtual reality exercises, and good mobile solutions be the realism that already 

provides significant benefits? 

4 maintworld 2/2021 

Erkki Mäkelä 

Business Manager, Kiwa Impact | Digital Services at Kiwa 

36 

The amount of information 

available and its use for 

analytics is growing exponentially, 

making it more 

important than ever to base 

decisions on accurate and 

reliable data.

IN THIS ISSUE 2/2021 

10 

The 

4th industrial revolution, 

IoT and predictive analytics 

offer unprecedented 

opportunities in the field of 

maintenance, reliability and 

condition monitoring. 

= 

30 

What 

do monkey wrenches 

and computer algorithms have 

in common? Surprisingly, 

wrenches and computer 

algorithms are both invented 

in the same year 1840. 

6 

30 Years of Adash 

22 

20 The Heat is On 36 

Asset Performance Awards Night 2021 

10 

Keep It Simple, Keep It Running: 

14 

24 

ICONICS Asset-based Management 

26 

and FDD Save Time and Expense 

Ultrasound and Vibration analysis: two 

18 

key elements of predictive maintenance 

30 

Streamlining Processes: 

why should we? 

SDT Proudly Announces Vigilant 

Reliability and Maintenance 

Management (RMM) - The frontline 

of maintenance 

From Wrench to Algorithm 

Measurement Traceability for the 

Controlled Environment 

38 

44 

48 

05-Steps to Develop Drilling 

Organization Asset Integrity 

Management Program 

Infrastructures Physical Assets High 

Performance Achievement based on 

Reliability and Maintenance Program, 

A.I and Asset Integrity Management 

Monetizing Data in Maintenance: 

Data-driven Spare Parts Managements 

(part 3) 

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). 

2/2021 maintworld 5

MAINTWORLD INTERVIEW 

Adash portable vibration 

analysis devices nowadays. 

30 Years of Adash 

The Adash Company, a producer of vibration analysis instruments and software 

is celebrating its 30-year anniversary this year. Though this is an uncommon 

article here in Maintworld, we have decided to online interview Mr. Adam Bojko 

and Mr. Radomir Sglunda who are founders and owners of the Adash Company. 

We would like to know more about their successful worldwide business and 

basically why and how they started. How is the current market situation and 

how do they see a future of machine condition monitoring? 

MAINTWORLD: My first question is 

probably no surprise. What brought 

you two together and what lead you to 

a decision to start your own company 

concentrating on industrial maintenance 

and predictive maintenance in 

particular? 

RADOMIR: We met at the Coal Research 

Institute here in Ostrava in the 


mid 80s. Adash is based here in Ostrava 

too by the way. We were in the 

seismic measurements department. 

That was our first touch with a vibration 

analyser. Nobody knew in the beginning 

how to operate the B&K 2034 

vibration analyser, which was bought 

by the Research Institute for our 

measuring purposes. It was no surprise 

that all manuals where in Eng- 

lish only and we needed to study a lot 

all the new terms for us. We found out 

how to set up correct time waveform, 

FFT and other types of measurement 

for our seismic measurement purposes. 

But why did the manual keep 

talking about some "rotating machinery 

analysis"? This area was totally 

unknown to us, and we were curious 

to know more about it. We managed


to get more materials about this 

method of predictive maintenance 

and started to offer our services to 

surrounding factories. 

MAINTWORLD: I presume that you 

were using competitors’ devices 

for your jobs in the beginning. But 

what was actually your first product 

under the Adash name? 

ADAM: We were young with not too 

much in the way of resources. We 

literally had just our PC. Therefore, 

there was only one way for 

us to start; to do some programming. 

Our first Adash product was 

software for Modal Analysis. Later 

on we got in touch with TEC from 

Knoxville and we made Operating 

Deflection Shapes at their instigation. 

However, this was specialized 

software for a limited amount of 

vibration experts. And we were 

hungry to catch a bigger audience. 

MAINTWORLD: Where did you see 

these bigger opportunities? 

The first Adash vibration analyser 

A4001 from 1995 

The first generation (1998) of the Adash 

bestseller. The legendary A4900 Vibrio M 

vibration data collector. Thousands of pieces 

and counting have been sold worldwide. 

ADAM: We wanted to attract the 

general public. Basically, guys in 

the field. People involved in dayto-day 

data collection. Adash was 

actually the first in the world to 

write route measurement management 

software DDS which was 

running under Windows 3.1 OS. We 

were selling it along with competitor’s 

data collectors and things finally 

started to move. We even sold 

tens of copies to our German rival 

Pruftechnik in the past when they 

did not have their own Windowsbased 

software yet. 

MAINTWORLD: So why did you also 

start hardware production if you 

were successfully selling your software 

with competitors’ hardware? 

RADOMIR: That is a simple answer. 

Competitors closed their hardware 

communication protocols. Therefore, 

our DDS software could not 

"talk" with their data collectors 

anymore. 

MAINTWORLD: Understood. I guess 

you started with a simple vibration 

meter, correct? 

ADAM: Actually no. Our first device 

in 1995 was a vibration analyser, 

data collector. We hired our 

first employee whose hobby was 

electronics, and he became our 

hardware development engineer. 

The vibration analyser A4001 was 

presented at the biggest Czech 

engineering fair in Brno that year. 

I remember the first version at the 

fair was not 100% ready for market. 

The batteries there drained in 

about 10 minutes, so we needed to 

be very quick while presenting to 

potential clients ☺. This battery 

Adam Bojko and Radomir Sglunda in late 80's. 



Adash online vibration monitoring 

systems nowadays. 

The first software in the world for route measurement 

management DDS running under Windows OS from 1993. 

The first Adash 

online vibration 

monitoring 

system A3600 

from 1996 

issue was of course fixed right after 

the show. 

MAINTWORLD: It seems there has been 

huge progress during the years as you 

have a wide range of portable and online 

monitoring equipment in your 

portfolio nowadays. How do you fight 

against competition and what is the 

current situation in your business area? 

RADOMIR: First of all we managed to 

find distributors in over 90 countries 

during the years. Competition will 

always be here which keeps us in pace 

to bring new things to clients. We are 

developing our products according to 

the end users’ requirements actually. 

We are listening to their suggestions 


of what should be improved or what 

features should be added to our software 

or hardware. Technical support 

from our distributors and from us is 

quite quick I think. We have unique 

measurement features and also, we are 

competitive price wise. Last but not 

least we offer 5 years warranty on our 

products. 

Our big competitors have been acquired 

by huge corporations during 

last few years. We think it will slow 

down their development in the future 

as they have lost independence. 

MAINTWORLD: Talking about the future. 

How do you see vibration condition 

monitoring in the future? What 

can we look forward to in Adash? 

ADAM: Despite concentrating now on 

our portable devices, I think factories 

will be turning more to 24/7 online 

monitoring systems. But we believe 

there will be still a group of vibration 

enthusiasts who will appreciate 

our portable systems. We also hear a 

lot these days about Cloud software, 

Wireless solution... We are currently 

working on Cloud-based software as it 

is a demand from clients to take a look 

at data from any part of the world and 

to look at the data via a mobile app. We 

are ready for Wireless data transmission 

to our devices. However, we have 

not found a reliable wireless accelerometer 

producer on the market so far. 

See more on www.adash.com

ASSET MANAGEMENT & TECHNOLOGY 

WIM 

VANCAUWENBERGHE, 

Director of BEMAS, 

Wim Vancauwenberge (BEMAS) 

about Asset Performance 4.0 

Conference & Exhibition 

"Opportunity to give assets vitamin D in a targeted way” 

On 26, 27 and 28 October 2021, BEMAS (the Belgian Maintenance Association) 

will organise Asset Performance 4.0, a hybrid conference and exhibition in Antwerp. 

The focus lies on the digitalization of maintenance; reliability and all other 

disciplines that influence the performance of assets. 

10 maintworld 2/2021


WIM VANCAUWENBERGHE, DIRECTOR OF BE- 

MAS, explains the context of the initiative: 

"Our world is in the middle of an extremely 

exciting phase of continuous digitalization. 

Sensors are becoming cheaper, machines and 

installations are getting a digital upgrade, data 

is being unlocked via the Internet of Things. 

The good old creed 'to measure is to know' is 

becoming more important than ever! Thanks 

to digitalization, this 'measuring' is done on 

an increasingly large scale, more accurately, 

more thoroughly ánd at a lower cost. 

The next step after measuring, the “knowing”, 

has also evolved greatly in recent years. 

Machine Learning and Artificial Intelligence 

allow sensor measurements and other data 

sources to be handled very differently. Thanks 

to smart algorithms, all data can be combined 

to detect deviations at an earlier stage, to predict 

what might go wrong and even to propose 

the necessary measures to predict problems. 

This may concern quality issues, abnormal 

energy consumption, and of course technical 

malfunctions. Digitization thus helps to take 

reliability and maintenance to a higher level. 

Vitamin D for Data 

Capturing and processing data is one thing. 

But the big challenge for an organisation is 

what you DO with it. The AP 4.0 conference 

will therefore also bring new insights and inspiring 

examples of how concrete benefits can 

be gained from the advanced digitization of the 

production process and asset management. 

"We see that organisations often already dispose 

of a lot of data. It is therefore especially 

important to set up those data and data flows 

correctly and to link them to intelligent algorithms. 

This allows all business processes to 

be boosted with the vitamin D of Data. We see 

that more and more organisations are combining 

existing good practices with new data-driven 

insights in order to create additional added 

value with their assets." 

Hybrid Conference 

& Exhibition 

"Since its launch in September 2020, the Asset 

Performance 4.0 platform has become a 

digital source of inspiration where more than 

900 users can find information and inspiration 

on digitization in asset intensive organisations. 

At the end of October 2021, we invite 

everyone with an interest in this topic to the 

FMCCA in Antwerp. Those who participate 

on site can network and have in-depth discussions 

with the guest speakers and colleagues. 

However, it is also possible to follow the presentations 

online. Both asset owners and experts 

bring inspiring insights and share their 

experience with the new technologies. We 

also organise a hackathon, where companies 

can present their solution to the case live on 

stage. At the exhibition (also open for visitors 

who aren’t attending the conference), specialised 

companies present their best practices 

and digital solutions in maintenance, reliability 

and asset management. During the Asset 

Performance Awards, companies can win a 

prize with a case about a (digital) improvement 

project. Of course everything will be 

organised in accordance with the COVID-19 

measures in force at the time. 

"We are strongly committed to interaction 

and the exchange of experience. AP 4.0 

will be an inevitable appointment for anyone 

who wants to gain new insights into the current 

digitis not only limited to the conference 

on 26, 27 and 28 October, but will also 

continue to offer weekly webinars via the AP 

platform. All presentations made during the 

conference will remain available digitally. 

Participating therefore means keeping up to 

date.", concludes Wim Vancauwenberge. 

OUR EARLY BIRD RATES ARE VALID UNTIL 1ST OF AUGUST 2021. 

Register now and you even get free full access to all recordings of the 2020 edition! 



Asset Performance Awards Night 2021 

ON THE 27TH OF OCTOBER, the first day 

of the Asset Performance Conference & 

Exhibition, BEMAS (the Belgian Maintenance 

Organisation) organizes the AP 

Awards Night. The goal is to highlight 

achievements in maintenance and asset 

management. Asset owners and service 

providers can participate in three categories: 

• Asset Performance 4.0 Award 

• Best Improvement in Maintenance 

and Asset Management 

• Technical Team of the Year 

After the presentation of the cases, Peter 

Hinssen will kick off the Awards Night 

with a keynote on “The day after tomorrow 

in Asset performance”. After the 

winners’ announcement, you can enjoy 

the rest of the evening with a luxurious 

walking dinner and an opportunity to 

network at the exhibition. 

PROGRAMME: 

09h00 : Reception 

11h00 : 3 cases ‘Best Improvement in 

Maintenance & Asset 

Management‘ 

14h00 : 3 cases ‘Asset Performance 

Award 4.0‘ 

16h00 : 3 cases ‘Technical Team 

of the Year‘ 

17h30 : Reception 

18h30 : Asset Performance Awards 

Night with keynote by 

Peter Hinssen 

19h15 : Announcement of the winners 

20h00 : Walking dinner and networking 

at the exhibition floor 

Sign up now to be present at the 

Asset Performance 4.0 Awards Night 

(in Antwerp or online!) via 

www.assetperformance.eu/2021 ! 

You can still participate 

as a contestant! 

Do you have an inspiring case in 

one of the three categories? Sign up 

before July 15th, and get a chance to: 

• Show that you are proud of the 

achievements of your organisation 

and the employees in 

your team. 

• Increase the image of your 

technical department and/or 

your team and company. 

• Give a nice incentive to your 

technical team 

• Win a fantastic prize and put 

your company in the spotlight 

for one day long. 

Get more info & register via 

www.assetperformanceawards.eu 

Who should attend the Conference & Exhibition? 

ASSET PERFORMANCE 4.0 provides a comprehensive and attractive 

agenda of learning opportunities for anyone with 

a keen interest in increasing asset performance. The Asset 

Performance 4.0 initiative offers essential insights on how to 

increase Reliability, Production Output and Quality by using 

smart solutions and new technologies without omitting the essential 

basics and best practices. 

We address both technical and managerial practitioners 

active in asset intensive industries across Europe and 

throughout the world. The audience includes Operations 

& Maintenance Managers, Plant Managers, Production 

Managers, CTO’s, Asset Managers, Maintenance & Reliability 

Engineers, Service Managers, Quality Managers 

and of course Digitisation Managers, Industry 4.0 Program 

Managers, AI & Data Engineers and IIoT specialists. 


MODULAR SYSTEM 

Each OnTrak is capable 

of 16 sensors. Easily 

scale OnTrak systems 

to thousands of 

sensors to one 

central dashboard 

BEARING LUBRICATION 

REIMAGINED 

Remote and Real Time 

Bearing Monitoring and Lubrication 

COMMUNICATION 

(Ethernet, wifi or cellular) 

SINGLE POINT 

LUBRICATOR 

Dispense lubricant with 

precision only when 

needed from up to 

16 single point 

lubrication devices 

The OnTrak SmartLube is a unique remote bearing monitoring 

and lubrication system. Designed to monitor and lubricate 

bearings remotely. With remote condition-based lubrication 

you can greatly reduce bearing failures. 

System uses ultrasonic 

sensors: identify 

bearing issues beyond 

lubrication at the 

earliest possible point 

Lubricate bearings 

remotely with a push 

of a button, using 

always the right grease 

and the right amount 

MOBILE VIEW 

Viewable on any 

network connected 

device; pc, laptop, 

tablet, phone using 

a standard browser 

ALARM NOTIFICATION 

Built-in events system, 

which is configurable, 

and has the ability to 

display, email and text 

any alerts the system has 

All data accessible anytime, 

anywhere, via user-friendly 

dashboards 

Easy to install, affordable 

and scalable 

CONTACT US FOR A DEMONSTRATION 

WITH REAL LIFE DATA! 

UE SYSTEMS UK & IRELAND - CHRIS HALLUM 

+44 (0) 7930 352 188 | chrish@uesystems.com 

System includes single 

point lubricators: no 

more lubrication issues! 

Integrates with existing 

databases and CMMS

PARTNER ARTICLE 

Keep It Simple, 

Keep It Running 

ICONICS Asset-based Management 

and FDD Save Time and Expense 

Simplicity can often be its own reward and that’s especially true when it comes 

to control and monitoring systems for asset-based management and maintenance. 

Undoubtedly, in this digital age, there are multiple possible choices for keeping track 

of an organization’s connected assets as well as for maintenance operations. 



MELISSA TOPP, 

Senior Director of 

Global Marketing, 

ICONICS, 

melissa@iconics.com 

FOR THOSE COMPANIES considering a 

change, or assessing new solutions, it 

might be helpful to understand their 

many possible options. Some only add 

needless complexity and more work 

than necessary, while others accomplish 

the goals of saving time, making 

larger systems more easily manageable, 

and helping organizations to 

become more proactive with their 

maintenance concerns. 

ICONICS (https://iconics.com), a 

group company of Mitsubishi Electric 

Corporation, has created a system 

incorporating an easy-to-maintain, 

tree-based asset management structure 

that allows for data analysis 

add-ons, such as for fault detection 

and diagnostics (FDD). The Foxborough, 

Massachusetts-headquartered 

company, now celebrating its 35th anniversary, 

is a global automation software 

provider of advanced industry 

4.0, web-enabled, OPC UA- and 

BACnet-certified visualization, analytics, 

and mobile software solutions for 

any energy, manufacturing, industrial, 

or building automation application. 

Asset-based Management 

for Easier Control and 

Monitoring 

ICONICS created its AssetWorX 

technology, found within its 

GENESIS64 HMI/SCADA and 

building automation suite and configured 

via its Workbench management 

environment, to make it easier 

for organizations to monitor and 

control digitally connected assets. 

It is an ISA-95-compliant solution 

that provides an architectural layer 

that greatly reduces engineering time 

while improving operator consistency 

and offering intuitive navigation. It 

enables the system to be engineered 

and operated based on an intelligent 

asset model configured to represent 

your organization. 

AssetWorX consists of a tree-like 

structure in which users can build 

a digital twin of their enterprise by 

mapping physical locations and business 

units, as well as equipment and 

machinery, all in one centralized 

system. This asset structure then 

provides a functional hierarchy for 

navigation and for data roll-ups. 

Lower-level equipment combines to 

form higher levels in the structure. 

Physical locations and areas of responsibility 

can be identified in the 

hierarchy. 

The asset tree provides a way 

to organize data sources (e.g., OPC, 

BACnet, database, web services, etc.) 

and visualizations (e.g., HMIs, charts, 

heatmaps, reports, etc.) in a logical 

hierarchical structure. For example, 

rather than OPC data sources being 

organized based on the address space 

of the server itself, these data sources 

can be organized based on the geographic/physical 

locations of the associated 

sensors (for example, by site, 

building, floor, machine, etc.), providing 

an invaluable level of contextualization 

for insightful analysis. 

THERE ARE MULTIPLE POSSIBLE CHOICES FOR KEEPING TRACK 

OF AN ORGANIZATION’S CONNECTED ASSETS AS WELL AS FOR 

MAINTENANCE OPERATIONS. 



Instead of configuring a separate 

system for each application (e.g., data 

management, aliasing, alarming, etc.), 

data from multiple applications can 

be configured into a single tree and 

organized in a logical structure that 

users define. Behind the scenes, 

AssetWorX uses existing configuration 

providers and services (as plugins) 

to configure existing applications. 

AssetWorX comes with multiple 

predefined nodes at the highest level 

of the structure. These nodes are: 

• ASSETS - where users can configure 

equipment and house their hierarchical 

asset tree. 

• EQUIPMENT CLASSES - templates 

that can be created for building the 

asset tree. 

• TREE VIEWS - allow users to better 

filter and reorganize items within 

tree-based navigation. 

• PRODUCT CONFIGURATION - where 

users identify preferences for 

AssetWorX settings. 

Within any enterprise, equipment 

can be interconnected according 

to its physical location or by the 

business units into which they are 

organized hierarchically. Users can 

easily define any number of those interconnected 

relationships using the 

asset tree. Each piece of equipment 

gets its own node in the asset tree, 

along with its own associated properties. 

A property might be a variable 

data source, a reference to an HMI 

graphic, an alarm, or a static value. 

It is within these equipment properties 

where the connection to additional 

possible analytics can be made 

to one of ICONICS popular analytical 

tools. One such tool, FDDWorX, is 

ideally suited for asset management 

and maintenance. 

Fault Detection and Diagnostics 

for a Proactive Maintenance 

Strategy 

FDDWorX is a predictive building 

automation solution that uses 

ICONICS’ advanced Fault Detection 

and Diagnostics technology. Configured 

within AssetWorX via Equipment 

or Equipment Class properties, 

it incorporates algorithms that 

weigh the probability of faults and 

advise maintenance personnel, operators, 

and management of actions 

to prevent equipment failures or 

excessive use of energy. When equipment 

failures occur, advanced software 

technology provides automatic 

guidance to a list of causes sorted 

by probability, resulting in reduced 

downtime and lower costs to diagnose 

and repair. 

FDDWorX collects equipment 

process data using industry-standard 

data collection mechanisms. 

It can automatically generate fault 

notifications and reports. Operators 

can use real-time displays to analyze 

relevant data such as the status of 

equipment operating outside the 

parameters of a given fault rule. 

The technology also takes into account 

several National Institute of 

Standards and Technology (NIST) 

concepts. 

ICONICS’ AssetWorX and 

FDDWorX technology makes it easier 

to connect digital assets for main- 



tenance operations, whether building 

out new facilities/plants 

or adding new equipment. 

Multiple instances of the same 

equipment can be easily added 

and updated through a templatized 

Equipment Class. Another 

option users can take advantage of, 

especially for initial deployment, is 

to setup repeatable assets through 

ICONICS’ Bulk 

Asset Configurator, an optional 

utility that automatically instantiates 

equipment in AssetWorX based 

on Equipment Classes and unique 

parameter values for each instance 

of equipment. 

The Bulk Asset Configurator 

makes use of a Microsoft Excel 

file that contains AssetWorX path 

structures to where equipment will 

be instantiated, Equipment Classes 

to reference, and values for each parameter 

that exists in the Equipment 

Class used. An additional sheet within 

the Excel file can be used to define 

alarms and historical data tag definitions 

for each property specified in 

the given Equipment Class. 

ICONICS aims to provide those 

responsible for maintenance with 

an easy-to-manage framework for 

operations. Asset-based management 

coupled with fault detection and 

diagnostics technology has been successfully 

tried and tested by numerous 

customers around the world, resulting 

in saved time and related costs. 

FREE Trial Download of ICONICS Suite Version 10.97 

Visit https://iconics.com/Download-ICONICS-Suite to download a trial copy of the latest version of ICONICS Suite, 

containing updated AssetWorX and FDDWorX technologies. 

ICONICS Institute - FREE Expert-Led Overviews with No Sign-Up 

ICONICS continues to add expert-led training videos to its ICONICS Institute video library, including entire sections on 

“Asset Organization” and “Fault Detection & Diagnostics”. Visit https://iconics.com/ICONICS-Institute to see the latest today! 


ASSET MANAGEMENT 

Ultrasound and Vibration 

analysis: two key elements 

of predictive maintenance 

Vibration analysis has been for many 

years the technology of choice for 

maintenance professionals to monitor 

the condition of rotating assets. 

However, in the last years ultrasound 

has also emerged as a very popular 

technology for condition monitoring. 

The question that many are now asking 

themselves is: which one is best? 

Ultrasound or vibration? In this article 

we will focus on the role of ultrasound 

as a condition monitoring tool, and why 

using vibration and ultrasound together 

is the best way to reach excellence in 

your maintenance practices. 

Why vibration analysis? 

Vibration analysis is an incredible tool: it detects and measures 

small vibrations and what is causing them, thus allowing 

maintenance professionals to detect early failures on rotating 

equipment. Furthermore, vibration analysis gives us a very 

deep diagnostic and allows us to identify the failure’s root 

cause, and thus correct it to avoid further issues in the future. 

Plus, there are a great number of vibration sensors and solutions 

on the market to choose for, so maintenance teams can 

find a solution that is suitable for their needs. 

Why Ultrasound? 

Ultrasound is considered by many the first line of defence 

when it comes to bearing failures, since it can give a very early 

warning of a potential problem, even with lubrication issues. 

The way ultrasound does that is by monitoring the friction levels 

on rotating equipment. The concept is simple: as a bearing 

starts to fail, or if it has not been lubricated properly (under or 

over lubricated), the friction levels rise. Friction creates ultrasound 

emissions that can be picked up by an ultrasonic handheld 

device or sensor and translated to low frequency sounds 

that the inspector can hear. Ultrasound equipment will also 

provide a decibel level – and the higher the decibel, the higher 

the friction. 

Ultrasound or vibration? 

There is no easy answer to this question, but one thing is for 

sure: if a maintenance team wants to reach excellence, both 

technologies should be used. Ultrasound will provide the 

earliest warning of failure and is also very easy to use, since it 

relies on simply trending decibel levels. Vibration analysis is 

extremely complete and will give maintenance professionals 

a deep overview of the issue and the root cause of such issue. 

Almost as if ultrasound is the doctor who detects the problem, 

and vibration is the health specialist that will diagnose it properly. 

We will now talk about a few situations where, in general, 

ultrasound can be used instead of vibration analysis. 



Slow speed bearings 

Slow speed bearings are difficult to monitor. Since they rotate 

very slowly, it is difficult for vibration sensors to pick up 

significant changes in vibration. Even with an ultrasound instrument 

it may be difficult to pick up failures if we rely only 

on decibel levels, since in extreme slow speed bearing applications 

(usually less than 25rpm), the bearing will produce little 

to no ultrasonic noise. However, high-end ultrasonic devices 

will allow for sound recording: by recording the sound of the 

bearing and checking it in a spectrum analysis software, we 

can easily find peaks in the sound spectrum amplitude which 

indicate a fault in the bearing. 

First line of defence, easy to use 

For a maintenance professional to properly work with vibration 

analysis, significant training and experience are needed. 

On the other side, ultrasound has a much quicker learning 

curve. And this is because of how the technology works: since it 

is monitoring friction levels and translating them to dB values, 

we can easily check for potential problems with our rotating 

equipment. Once we setup a dB baseline for a bearing, we just 

need to trend the dB value overtime. So, if the baseline for a 

bearing is 20dB, but the ultrasonic instrument reads 32db, we 

already know there is a problem simply by comparing values. 

Lubrication 

Again, because ultrasound is based on the friction levels, it 

is perfectly adequate for bearing lubrication. Is the bearing 

lacking lubrication? Then the friction levels will increase, 

and we can hear that through the ultrasonic instrument and 

see it in the dB levels. If we start lubricating the bearing, 

most likely we will see a decrease in the sound intensity and 

the dB levels. Did the bearing receive too much lubricant? 

Then again, friction levels will increase, and we will know 

that using the ultrasonic instrument. Thus, ultrasound is 

perfect to avoid under- and over-lubrication issues. 

Versatility 

While vibration analysis is an extremely powerful tool, its 

uses are limited to mechanical equipment. On the other hand, 

ultrasound has a wide range of applications which makes it 

a very versatile technology. One of the most popular applications 

of ultrasound, besides condition monitoring, is energy 

savings. Since turbulence also creates ultrasound emissions, 

the ultrasonic instruments can easily be used for leak detection 

(compressed air and other gases), steam traps inspection and 

even for electrical inspections, to detect issues such as corona, 

tracking and arcing. 

Conclusion 

We believe, as many other maintenance professionals nowadays, 

that using multiple technologies that complement each 

other is the way to go. Therefore, the question is not ultrasound 

vs vibration, but instead ultrasound AND vibration and when 

we should be using one or the other. Both are very powerful 

condition monitoring technologies and, when used properly together, 

can really take any maintenance and reliability program 

to the excellence level. 



ROMAN MEGELA GAZDOVA 

Senior Reliability Engineer, Easy-Laser AB 

The heat is on 

All rotating machinery are subjected to thermal 

exposure. The machines will react depending on 

temperature and material. Either by expanding or 

shrinking. And that is a fact. Thermal growth is a 

serious thing when you think about it. 

The machines might expand differently 

ALL ROTATING MACHINERY are installed 

in trains. Trains means there is a driver, 

which is the motor, and driven which can 

be the pump, blower, compressor, or any 

different process machine. When rotating 

machinery is installed, precision shaft 

alignment is performed. Shaft alignment 

will ensure both shafts (driver and driven) 

are collinear. Collinear means that both 

rotational centrelines are positioned as if 

they were one. 

Different situations 

When the machines are started the driver 

and driven heat up in very different ways. 

A compressor in a hot environment will 

quickly increase in temperature due to 

friction of its internal rotating parts, and 

compression of the media will generate 

and add more heat. Comparing to the 



driver, which can be an electrical motor, the 

situation is very different. The temperature 

will increase to a certain level and then 

remain the same. Two machines with two 

different behaviours. 

So, what happens when one of them 

increase its temperature respectively to the 

other? It’s simple; the machine will start expanding. 

And when the machine expands, 

it will grow in all directions and move its rotational 

centre out of collinearity and cause 

misalignment. But not only misalignment. 

Since there is a change in the machine geometry, 

pipe strain might also appear adding 

more stress to the housing. 

Many consequences 

There are so many consequences of 

thermal growth in rotating equipment. 

Misalignment will for example also result 

in bent shaft. Bent shaft will result in unproper 

distribution of forces in the bearing 

which in turn will lead to failure of the 

lubrication. Therefore, we must be able to 

anticipate thermal growth by using available 

information from the OEM, or by 

performing the calculation by ourselves. 

How do we do that? The key is to identify 

how much growth is expected. This 

ALL ROTATING MACHINERY 

ARE SUBJECTED TO 

THERMAL EXPOSURE. 

THE MACHINES WILL 

REACT DEPENDING 

ON TEMPERATURE 

AND MATERIAL. 

Thermal expansion by material 

number must be used when performing the 

shaft alignment to “intentionally misalign” 

the machines prior to start. Let us use the 

compressor as an example again. If we assume 

that the compressor will operate at 

higher temperature than the motor, when 

aligning, we must place the compressor below 

the rotational centreline of the motor. 

How much will be determined by expected 

thermal expansion growth of the material. 

Test run 

When the machine is aligned considering 

the thermal growth, it must run and operate 

until it reaches its full operating condition. 

Then it must be stopped, and the shaft 

alignment must be verified. This is our test 

run of the machine to confirm proper and 

reliable installation to be able to achieve 

full operational life. We want to test before 

we go to full production to make sure our 

thermal expansion calculation was right. 

Think about aircraft maintenance. When 

there is an aircraft engine replacement, to 

make sure it is operating as it should, the 

pilots perform test flights until everything 

can be confirmed. And you don’t want to be 

on the plane knowing nobody performed 

the test run, do you? 

Reveal Your Potential 

Get a Reliability and Maintenance Assessment 

Call us +1 919-847-8764


Streamlining 

Processes: 

why should we? 

Admittedly, writing out uniform work 

processes with associated roles and 

responsibilities is not exactly a task to 

look forward to. It is like cleaning up 

the attic, which over the years has become 

cluttered with boxes. However, if 

you put some effort into this you will 

benefit from overview, structure, clarity, 

and a much more efficient way of 

working. Ready to roll up your sleeves? 

Text and images: MAINNOVATION, SHUTTERSTOCK 

DOES YOUR ORGANIZATION HAVE WRIT- 

TEN MAINTENANCE PROCESSES? Is the 

content of the functions within maintenance 

exactly the same in every department? 

And are ‘best practices’ easily 

shared between branches and departments? 

– Chances are you have to admit that 

something could certainly be improved. 

We hear this a lot, says Peter Decaigny 

of Mainnovation. 

– But anyone who can answer the 

above questions with ‘yes’ has a good 

basis for streamlining the organization 

and/or successfully implementing an 

IT tool. And this is the foundation for 

further improvements in effectiveness 

and efficiency. 


Clutter in the Attic 

Many Maintenance and Asset Management 

organizations have grown organically 

and there is nothing wrong with 

that. It all makes sense to the people 

involved in this growth. Maybe some 

choices were a compromise under pressure 

from stakeholders at the time. 

Sometimes organizations are built 

around people and not based on clear 

roles and responsibilities. Some IT tools 

are configured to mimic the functionality 

of old IT tools. And often, in large 

companies, the different maintenance 

departments or teams work in completely 

different ways. 

It is like ‘clutter in the attic.’ In the 

beginning there was a system, a certain 

storage structure, but more stuff was 

added and eventually you find you first 

have to move other boxes, before you 

find the right box. "Efficiency can be 

gained. This starts with cleaning up 

and drawing up a plan on how to prevent 

this cluttering." 

Uniform work processes 

With a process map you can describe 

the future (desired) situation. This 

document is a detailed description of all 

processes that are important within the 

Maintenance and Asset Management 

organization. 

Uniform work processes can be written 

down in two ways: you either start 

from scratch with noting down all steps 

in a process, or you can use an existing 

process map. "Obviously, both ways have 

advantages and disadvantages," explains 

Decaigny. "With a blank start, everyone 

can have their say, which results in 

commitment. However, it is very timeconsuming." 

By using an existing process 

map, there is already a considerable basis 

and this is of course a time saver. "But in 

this case that basis must be good, obviously. 

After all… reorganizing the attic 

when the floor is rotten, wouldn't be a 

good idea." 

Based on best practices 

That is why Mainnovation uses the 

VDM XL Process Map. This Process Map 

is based on proven ‘best practices’ of leading 

Maintenance & Asset Management 

organizations from various industries. 

This method is written down in more than 

60 work processes that together form the 

VDM XL Process Map. This template is then


critically assessed in various workshops 

within your company. What is missing 

and what is or is not applicable for this 

organization? 

– And yes, that works, says Decaigny. 

– Process operations are often very 

generic and by fine-tuning them we get 

a process map that is very customerspecific. 

– There are companies that claim 

that they have already written maintenance 

processes in place, says Decaigny. 

– But in most cases they are based 

on what the ISO 9.001 manual provides. 

However, these process descriptions 

are of little value for restructuring an 

organization. They are too high level 

and do not provide enough guidance for 

the next steps. 

The next step would be to use the 

processes – that describe what needs 

to be done – for assigning the roles and 

associated responsibilities and KPIs. 

And the ultimate result is the practical 

work instruction that emerges from 

this. This describes the necessary actions 

with a focus on how things should 

be done. 

Roll up your sleeves 

This probably sounds like a tremendous 

amount of work. Sitting at the 

computer for days and writing, evaluating 

and writing again. Arguing the 

right approach because nobody wants 

to change their way of working. You 

can of course save this task for a rainy 

day, but the clutter in the attic will 

pile up ... 

And there are several reasons to 

roll up your sleeves anyway. 

– A uniform process map is a perfect 

starting point for ISO certification. 

The VDM XL Process Map connects 

seamlessly with ISO 55000. 

When a multisite company decides 

to implement one EAM system for all 

locations – which occurs regularly – 

the process design can be used as a 

blueprint for this (future) common 

IT tool. 

– Of course, there can still be differences 

in how the organization is set up. 

When responsibilities are assigned to 

a particular role rather than a specific 

job, uniform procedures can still be 

used. After all, you want the implementation 

to take place in a way that 

is labelled as most effective and most 

efficient. It goes without saying that 

this way of working delivers value, Decaigny 

explains. 

A uniform working method also has 

advantages for training (new) employees. 

– In short, it pays off. It takes time 

and commitment but rolling up your 

sleeves now means creating room for 

improvement and growth in the future. 

Are you interested in the VDM XL Process 

Map? The Mainnovation consultants 

are happy to help you structure and 

implement uniformity in processes, organization, 

IT tools and KPIs. Check the 

website for more information or send an 

email to info@mainnovation.com. 



SDT Proudly Announces Vigilant 

SDT Ultrasound Solutions is excited to announce 

the release of Vigilant, the newest 

addition to its family of permanent condition 

monitoring solutions. Vigilant is an 8-channel 

online condition monitoring solution that combines 

the versatility of ultrasound diagnostics 

with the analytics of vibration data. An 

additional 4 channels allow inputs for more 

conventional machinery information such as 

temperature, RPM, and other process data. 

VIGILANT is a stand-alone solution. With its own embedded 

software contained within the measurement pod, anyone with 

network credentials has access to critical asset health via a web 

browser. Using standard communications protocols like Ethernet, 

OPC, and Modbus TCP, Vigilant’s communication capabilities 

makes the sharing of asset data to other information systems easy. 

“ There are many applications where using a single condition 

monitoring technology to attempt to identify a failure mode doesn’t 

net the outcomes needed by reliability planners”, explains Vigilant 

product specialist Robert Dent. “ Vigilant combines insights from 

two proven technologies to bring conditional data into a common location, 

while providing industry standard tools to perform analysis.” 

As assets become more heavily guarded for safety protocol, condition 

monitoring teams need more creative ways to access collection 

points. Vigilant solves the accessibility dilemma by mounting 

low-cost, high-quality permanent ultrasound and vibration sensors 

to the asset, and then connecting the data directly to the asset owner. 

“ SDT Ultrasound Solutions has always provided ways to collect 

and combine Ultrasound and Vibration measurements with our 

handheld portable systems”, said SDT’s Benoît Degraeve, area sales 

manager and Vigilant product specialist in Europe. “ Today, we do the 

same thing with Vigilant, in a safe, reliable and permanent way.” 

Vigilant is available in two configurations: Mobility and Permanent. 

Vigilant Mobility comes packaged in a rugged, environmentally 

protected carrying case and is designed to travel with you to fieldlevel 

critical assets where temporary 24/7 monitoring is required. 

Vigilant Permanent installs and remains on an asset for its life cycle. 

Protected in your own enclosure, Permanent requires a 24V power 

source and communications connection. 

Vigilant manages both Static and Dynamic data, creating an opportunity 

to establish long-term trending, analysis, and diagnosis at 

the earliest point in the failure curve. 



Reliability and Maintenance 

Management (RMM) 

- The frontline of maintenance 

Many Reliability and Maintenance improvement initiatives fail to deliver 

sustainable (and continuously improving) results in improved safety, 

manufacturing throughput, and costs. There are many reasons for this, but the 

lack of engaged, visible, and caring plant leadership is the most common of them. 

CHRISTER IDHAMMAR, Founder IDCON INC. 



TAKING REGULAR WALKS to visit the frontline of maintenance, 

or Gemba walks, is an excellent way for a leader to demonstrate 

engagement. Such walks take management to the front lines 

showing that they are an engaged, visible and caring leader 

and also enables them to learn first-hand what improvement 

opportunities exist. You are visible and you show that you 

care. This is key to successfully improving your maintenance 

organizations’ performance and delivering continuously better 

results. 

Gemba, sometimes spelled Genba, is Japanese for “where 

things happen,” or "the real place." It can be anything from an 

actual crime scene to the context of an RMM organization, 

where planners, front-line leaders, coordinators, engineers, 

operators, crafts people cand storeroom employees close to the 

manufacturing floor make things happen. They are the ones 

who will execute your initiatives, such as preventive maintenance, 

work management, stores organization including, 

delivery and “kitting” of parts and material, and many more 

improvements. If they do not execute your initiatives, then all 

you have is a plan. 

As leaders, we are often pressed for time, but carving time 

to engage with the frontline is crucial for success. Remember - 

improving reliability and maintenance performance is “90%” 

about people and processes, so it is important that you are visible 

and available. 

If you organize and schedule frontline walks well, you will 

find that it does not have to take much time. You will also find 

it interesting and rewarding when you see the appreciation and 

improvements. 

I suggest the following steps for your frontline walks initiative: 

1. Organize and plan 

2. Inform 

3. Observe and learn 

4. Ask questions, meet face to face 

5. Act and follow up 

1.Organize 

Depending on the size of the Maintenance organization, the 

plant manager might do a frontline walk once a quarter per 

production area. Operations and Maintenance managers 

might do the walk together once a month in their respective 

areas. Supervisors should do it weekly. 

Set up an improvement activity to focus on for each walk. 

Do not include things already on your meeting agendas. No 

need for duplication. 

It is very beneficial if you have done an assessment of improvement 

opportunities and a plan on how to close the gap 

between how good you are now to how good you can become in 

three to five years. 

If you have done that and have identified the gaps you need 

to close, it will serve as a setup- and focus guide, for each walk. 

This will most likely include the following areas to improve 

upon. 

• Lubrication practices 

• Precision maintenance practices 

• Basic inspections and predictive technology 

• Work management 

• Storeroom management 



By taking full advantage of the 

purpose of a frontline walk, you 

will show that you are engaged, 

visible (accessible) and caring. 

There can be other areas, but as good maintenance always 

requires—the basics must be executed well before you will be 

in a position to move towards excellence. Most plants I work 

with need to focus on the areas mentioned above. 

In a small plant the walk could include several improvement 

areas, while it will be necessary to select one or two focus 

areas in a bigger plant. Try to limit the “Observe and learn” 

and the “Ask questions, meet face to face” to 30 minutes each. 

These steps can be done the same day or on different days. 

2.Inform 

When you organize and plan your frontline walks, it is very 

important to share why and how you will do them with those 

involved. 

If you, as a plant, maintenance or operations manager, have 

a habit to be visible on the manufacturing floor and often talk 

with the frontline people, the walks will help organize your 

visits towards defined and selected improvement areas. For 

frontline leaders this is, of course, not much of an issue as 

you work closely with your people daily. If you, as a manager 

have kept a distance to the frontline organization it may be a 

welcome change. If your visibility was limited in the past, your 

appearance may be associated with trouble. This will change 

when people understand why and what the Gemba walks 

mean to the whole organization. 

3.Observe and learn 

Before you do “Ask questions, meet face to face” walks—visit 

the chosen production area you will observe and study the 

IN A SMALL PLANT THE WALK COULD 

INCLUDE SEVERAL IMPROVEMENT 

AREAS, WHILE IT WILL BE NECESSARY 

TO SELECT ONE OR TWO FOCUS AREAS 

IN A BIGGER PLANT. 

equipment so you can ask the right questions. The first time you 

do this walk preparing questions might take a bit more time. 

If you chose lubrication practices in one area you should look at: 

• Blown out seals indicating too much grease and poor 

seals. 

• Use of tools to measure grease volume on grease guns. 

• Clean tools and grease points. 

• Oil levels in gears, pumps etc. 

• Keep oil level indicators clean to see levels and colour of 

oil. 

• Right oil levels marked. 

• Lubricant type marked with a symbol. 

• Lubrication stores clean and organized. 

• Air breathers/filters installed where needed. Change in 

colour? 

• Leaks. 

• Long grease lines for manual greasing. 


If you chose Precision maintenance practices in one area you 

should look at: 

• Jacking bolts installed for precision alignment. 

• Jacking bolts backed off and not pushing on alignment 

object. 

• More than three shims used for alignment. 

• Beat marks on equipment feet. 

• Vibrating fans and high-speed equipment. 

• Filters for mechanical seals and hydraulic fluids. 

Other operating practices you should look at include if 

your redundant pumps are running equal time per each 

schedule and who is responsible for shifting the pumps 

according to that schedule. For more tips on what to 

look for during your walks, take a look at IDCON’s new 

series of Gemba videos at our website or our YouTube 

Channel. 

4. Ask questions, meet face to face 

After “Observe and learn”, you have the material to be 

well prepared to meet face to face and ask questions. 

Meet with the people in production who are responsible 

for various areas like lubrication, for example. Ask 

open-ended questions such as: “I saw a lot of grease 

pushed out from the drive side bearing of the fan pump, 

what can we do about that?” The answer might be: “That 

seal was gone a long time ago, so we need to purge out 

the water to save the bearing, we have asked to have it 

replaced many times. We also have new lubricators who 

have not been trained and think the more grease the 

better. We have also asked to get grease meters to put 

on the grease guns, so we know how much grease each 

point gets.” 

Other possible questions: “What would you like to 

spend more or less time on?” 

For the lubrication program, “What is the most important 

thing to improve upon?” 

The discussion will teach you a lot. But perhaps more 

importantly is the motivation you create by being engaged, 

visible, and caring as a leader. This goal will only 

be reached if you continue these walks. 

5. Act and follow up 

Be prepared for suspicion. Many people have seen new 

improvement initiatives come and go and may not be impressed. 

You have to prove this will be a continuous practice. 

During the frontline walks it is important to take notes 

and pictures. This will take less and less time as it becomes 

routine. It is important to inform your people what you decided 

to act upon. If you decided to get the equipment lubricators 

needed and train all lubricators, for example, then you 

might want to change the damaged seals. It is equally important 

to share what you decided against, and why. 

When well-organized, the five steps of the frontline walks 

will take a manager in a big plant about an hour per quarter/ 

area. For operations and maintenance managers walking 

together it might take an hour a month per area, and frontline 

leaders much less time per week. I am sure you will find 

it worth the time as your Safety, reliability and cost will 

improve.


From wrench 

to algorithm 

From “seeing” to “self-optimizing”: the past, present and future of maintenance 

What do monkey wrenches and computer algorithms have in common? Surprisingly, 

wrenches and computer algorithms are both invented in the same year 1840. 

DIRK DE NUTTE, CEO The Grain 

THE MONKEY WRENCH was invented 

by American knife manufacturer Loring 

Coes. On the other side of the ocean 

countess Ada Lovelace produced the 

very first "computer program" for the 

mechanical Analytical Engine (A.E.), 

invented by Charles Babbage named the 

father of computers, to calculate Bernoulli 

numbers. 

Ada Lovelace was not only a brilliant 

mathematician, she also discovered the 

full potential of this engine, developing 

the first algorithm[1]for the A.E. and 

becoming as such one of the first computer 

programmers. And now, some two 

centuries later, computers and wrenches 

seem to come together in a new era of 

maintenance. 

Nothing has changed 

but everything changes 

Maintenance and repair have been 

around as long as mankind exists. From 

the time-based sharpening of man’s 

earliest spears and tools to the maintenance 

concepts for modern equipment 

and technologies. Over these last couple 

of centuries maintenance has made an 


evolution and gradually merged from 

a corrective approach known as “break 

and fix”, over a preventive or time-based 

maintenance towards a predictive or 

condition-based maintenance. And 

now we are moving into the new era 

of an integrated and digitized phase of 

prescriptive maintenance, combining algorithms 

of computer science with common 

maintenance techniques. By using 

artificial intelligence (AI), we can now 

master all kinds of data like asset health 

data, process data, historical maintenance 

data and contextual-operational 

data from various sources. We get better 

and in-depth insights on asset behaviour, 

allowing us to act before failure modes 

are manifested. With the AI approach we 

can now forecast asset failures modes, 

and thus optimize not only the asset 

performance but the total plant performance 

and production cycle. 

Even though our machines, industry 

and technology changed over time, the 

true foundational elements of how assets 

fail remain unchanged, i.e. the failure 

patterns and the P-F curve. To see what 

our future brings, it’s wise to know our 

past, and that’s why we’ll make a brief 

journey through the history of modern 

maintenance. 

From the first industrial 

revolution until WOII 

During this period maintenance gradually 

changed from “break and fix” to 

“time-based maintenance”. This is the 

era of descriptive maintenance where 

engineers “see” and “feel” failures. 

Maintenance management has its origins 

in the manufacturing industry. The 

invention of the steam engine, together 

with other inventions like telephone and 

radio announced the start of the first 

industrial revolution in the 18th century. 

At the same time a gradual shift from 

human labour to machine production 

began. The maintenance strategy was 

very simple: keep the machine running 

until it fails, only fix when broken. This 

is also known as corrective maintenance. 

Machine breakdowns were tolerated and 

considered “normal”. 

During the second industrial revolution, 

mid-to-late 19th century, electricdriven 

machines were invented and


those required a more sophisticated 

maintenance approach. Plant engineers 

became more “proactive” to maintain 

their equipment. They put a time-based 

maintenance strategy in place, and 

machine parts where replaced at specific 

time intervals. Unfortunately, this 

maintenance strategy was, and still is, 

quite expensive, as machines need to be 

shutdown causing production losses, and 

wasteful as parts are being changed according 

to a strict schedule whether this 

is necessary or not. 

Machinery at that time was rugged 

and “slow running”, instrumentation 

and control systems were very basic. 

Economy and production requirements 

were not as demanding as they are today, 

and breakdowns were a less critical issue. 

A break and fix approach and/or 

time-based maintenance strategy were 

adequately enough. At that time machinery 

was “over-engineered” and build 

very sturdily and inherently reliable. 

Post war till late sixties 

During this period maintenance matured 

from “Planned preventative maintenance” 

to the first concepts of “Reliability 

Centred Maintenance”. This is 

the era of early diagnostic maintenance 

where maintenance engineers “investigate” 

and “understand” failures. 

From the 1950’s onwards, the post 

war economy picked-up quite rapidly. 

Industries needed to be rebuilt, especially 

those of Japan and Germany. International 

economy started flourishing, 

creating a more competitive marketplace. 

Machine downtime was no longer 

tolerated and labour cost became an important 

factor leading to mechanization 

and automation. Machinery became less 

sturdy, was built lighter and ran at higher 

speeds. Equipment was used more intensively 

causing more wear out, more 

vulnerable and so less reliable. 

Japanese engineers started following 

the manufacturer’s instructions. This 

was also the period that the first maintenance 

associations and societies were 

created, and first global networks established. 

That trend gave birth to what 

we know as “preventive maintenance” 

today. Gradually, those associations encouraged 

technicians and other specialists 

to develop time-based maintenance 

schedules: machine lubrication, machine 

inspections, reporting any observations 

to help prevent machine damages. 

Maintenance and inspection checklist 

where used but the disadvantages of this 

strategy soon became obvious. The fact 

that very often machines needed to be 

shut down to perform these inspections, 

caused production losses. Repetitive and 

often boring interventions lead to negative 

“human behaviour” such as “PM 

creep” (adding or increasing frequency 

of PM’s to the program for no failure 

mode related reason) and “pencil whipping” 

(signing off on work that has not 

been done) causing ineffectiveness and 

unnecessary costs. 

With the arrival of the Boeing 747 

in the late sixties, the aircraft industry 

needed to improve reliability and therefore 

defined a detailed maintenance 

strategy reducing the risk of equipment 

failures. Risk had become a new driver 

for maintenance. This challenged the 

current maintenance strategies and 

the long-established basic assumption 

that the older equipment gets, the more 

likely it is to fail. Reliability centred 

maintenance (RCM), a new maintenance 

strategy approach was developed 

by Nowlan and Heap. The term was first 

used in public by United Airlines. Shortly 

after, the concept was quickly adopted by 

other industries. 

With time, other industries began 

understanding the value of maintenance 

and realised that it had a strategic impact 

affecting the bottom line. Since then 

proactive elements were increasingly integrated 

in well-balanced maintenance 

strategies, giving rise to other methodologies 

such as risk-based inspections 

(RBI), overhaul, etc. 

While predictive maintenance, focussing 

on eliminating failure modes, had 

become common practice in the aviation 

industry, the more conservative industry 

was lagging behind and still relying on 

time-based maintenance. 

From late 60’s till late 80’s 

During this period the notion of “maintenance 

service” and “condition-based 

monitoring” emerged. This is the era that 

diagnostic maintenance made its first 

steps into the broader industry. 

Between the 1960’s and 80’s, maintenance 

was considered a side activity 

and seen as of minor importance, only 

necessary when a breakdown occurred. 

The maintenance department’s scope was 

restricted and mainly limited to electrical, 



mechanical repairs or greasing work. The 

notions of prediction or prevention were 

not at all integrated, and maintenance often 

suffered from a bad image. 

The industrial world, as well as the implications 

of failures, were however very 

different from the ones we know today. 

At that time, industry was burgeoning, 

consequences on production lines weren’t 

the same at all. Production shutdowns disrupted 

the production but weren’t leading 

to huge losses like today. During this period 

companies became progressively aware 

of the impact and importance of safety. 

Due to industry gearing to mass production, 

machines had become fast running, 

more advanced, more complex and were 

driven to their limits, causing higher risk. 

Hence, maintenance gained importance. 

The first maintenance procedures 

were developed, reducing working accidents 

and avoiding critical breakdowns. 

Interesting to note is that the main driver 

was human integrity (safety) rather than 

economic reasons. That gave a boost to 

the maintenance evolution in this period. 

Maintenance norms and standardization 

were progressively implemented, and 

they became necessary to train and certify 

technicians. The first trainings and certifications 

were implemented in this period. 

Precision maintenance slowly became 

common practice, extending the lifecycle 

of assets by integrating proper procedures 

and standards during installation or repair 

of equipment. 

From the 80’s till early 2000 

During this period maintenance and reliability 

engineering methodologies start becoming 

common practice in industry. This 

is the era of predictive maintenancewhere 

maintenance engineers are “prepared for 

what will happen next”. 

Surging globalisation gave rise to 

a further evolution of equipment and 

technology. Between 1980 and 2000, the 

industrial world changed in many areas: 

IT, maintenance, purchasing, communications, 

production, quality, safety. In 

the maintenance world “optimization” 

became crucial to survive. New concepts 

such as total productive maintenance 

(TPM), total quality management (TQM) 

and “lean” originating from Japan were 

implemented. The industrial sector was 

forced to modernize and adapt to secure 

their place on the globalizing market. It 

is then that computer maintenance management 

systems (CMMS) and quality 

management standards such as ISO-9000 

(1987) and others were implemented. 

FURTHER INTEGRATION OF THE 

DIGITAL AND PHYSICAL WORLD 

ENABLES CLOSE INTERACTION 

BETWEEN MACHINES, 

ALGORITHMS AND HUMANS. 

Many industries focused on increasing 

production and lowering production 

costs. So, equipment reliability and availability 

became of the utmost importance. 

Early detection of failures, increasing 

meantime between failures (MBTF), reducing 

the meantime to repair (MTTR), 

performing pro-active maintenance activities 

became the focus of maintenance 

departments. 

Engineering departments started taking 

maintainability and reliability considerations 

into their design, extending 

the asset’s life cycle. The awareness grew 

that the maturity and quality of a maintenance 

program is determined by being 

ahead of the P-F curve. 

During this period industry and their 

maintenance departments were confronted 

with increasingly and numerous 

hurdles. Apart from the fact that economic 

drivers put margins under pressure 

and cost control was often affecting 

maintenance departments first, maintenance 

also struggled, and even so today, 

to attract young and skilled people. This 

is mainly due to a lack of good image and 

the true understanding of the job content 

and how rewarding it is. 

Although much has happened since 

the start of the industrial revolution the 

past 200 years, it appears that the most 

dramatic changes have occurred within 

the last 30 years. Especially from the 

year 2000 onwards (the IT bug-century 

flip) to date, innovations in technology 


P 

The foundational elements of maintenance & reliability management 

will never change. But the way technology can deliver value to asset 

management programs is changing faster than ever. 

The Grain combines its expertise in industrial asset management 

and data science to enhance the performance and reliability of your 

assets by building customized, accessible and scalable AI solutions. 

F 

It is our mission to facilitate day-to-day work of maintenance 

practitioners, reliability engineers and operators. 

We embed the power of advanced analytics to accelerate the process 

of learning by combining signal data, maintenance logs or any other 

operating context information to predict the asset behavior, add 

new insights enabling you to prepare the right actions at the right 

time. We believe that blending artificial and human intelligence is 

key to exponential performance of your assets. 

Welcome to the age of prescriptive maintenance. 

Want to be part of it? 

Find out more on www.thegrain.pro/innovators or call +32 3 376 33 50 

Industrial AI applications


in every area of life are more than they 

have ever been in human history and 

maintenance management is not left out. 

A growing awareness of the value of continuous 

improvement and optimised asset 

management became obvious. During 

this period, an accelerated shift and interest 

in developing failure mode driven 

equipment maintenance plans, boosted 

predictive maintenance. 

From early 2000 to 2025 

The period where maintenance is evolving 

from “analysis to analytics” – the 

quantum leap. A new era of prescriptive 

maintenance where asset managers, maintenance 

managers and operators jointly 

use artificial intelligence to predict asset 

behaviour and define “what’s the best that 

can happen”. We are gradually entering a 

period where algorithms predict and allow 

automated self-optimisationactions and 

humans get a new role defined! 

Leveraged by accelerated technology 

evolutions and driven by globalised market 

mechanics and a fast-changing world 

of “instant” and “green” expectations, 

the need for continuous perfection and 

optimisation have now become prime for 

various industries and are key to survive. 

More than ever this new truth and the 

need to transform from flexible production 

to agile production becomes clear, 

now that mass production is quickly moving 

towards mass customisation. Current 

strategies are being questioned and many 

amongst us find ourselves at the tipping 

point between exploitation and exploration 

of our businesses and individual roles, 

often leading to new business models. 

Even though one already understood 

that the true value of asset management 

lies beyond the physical asset itself, it now 

becomes possible to connect asset data 

with context-, process and organizational 

data in a better and more efficient manner. 

Or to phrase in our language: “Failures 

leading to losses due to technical problems, 

generally referred to as special cause 

losses, can now be blend with operational 

inefficiency parameters, referred to as 

common cause losses. Here lies the new 

window of opportunity to excel, improve 

and gain that last overall equipment effectiveness 

(OEE) or total plant performance 

improvement. 

With the emergence of Industrial IoT 

(IIoT) collecting data from equipment is 

moving from paper-based, excel sheets 

and manual inspections to fully automated 

systems, enhancing both data quality and 

quantity. IIoT enables remote asset monitoring, 

also exponentially increases the 

quantity and variety of parameters that 

can be monitored and this at a better cost. 

With artificial intelligence (AI), predictive 

analysis (deductive) will shift to prescriptive 

analytics (forecasting), allowing us 

to move from ‘what happened’ to ‘what’s 

the best that can happen’, improving total 

plant performance instead of only uptime. 

In this early 21st century period, driven 

by advanced analytics, we can notice two 

major strategic shifts in maintenance 

strategies. 

A first strategic shiftis one that moves 

our maintenance practice from a failure 

mode driven asset strategy to an OEE 

driven strategy. By combining equipment 

data with process data, quality data, performance 

data, contextual-operational 

data and other relevant information, artificial 

intelligence provides new insights 

in equipment and process behaviour. This 

will provide maintenance technicians, 

planners and operators with comprehensive 

new, accurate and real-time insights 

into asset performance, risks, (..) and allow 

them to maintain higher levels of asset 

availability. 

Root cause analysis (RCA), by applying 

fi. fault-tree analysis as well as causeand-effect 

or failure-modes-and-effects 

analysis (FMECA), is a fundamental part 

of any organization’s maintenance and 

reliability strategy. Today, however, these 

activities are often conducted manually, 

and their outcomes are rarely recorded 

in a centralized manner. Hence through 

digitization and advanced analytics these 

methodologies will be automated and updated 

continuously. Possible root cause(s) 

will be suggested by the system in which 

the maintenance expert and/or operator 

will acknowledge the ‘real’ root cause, so 

improving the accuracy of the analytical 

models. Similarly, this can also be applied 

for reliability centred maintenance 

(RCM), helping teams choose the right 

maintenance strategy for each (critical) 

equipment. This is a gamechanger in predictive 

maintenance. 

A second strategic shiftis moving from 

an OEE-driven to an Operational Excellence 

driven strategy. Combining OEE 

data with loading, planning and product 

supply chain data allows to improve total 

plant performance. Maintenance schedules 

will be aligned with production schedules 

to optimise total production, balancing 

demand and supply. Using AI within 

production enables now to shift from mass 

production to mass customization. 

Further integration of the digital and 

physical world enables close interaction 

between machines, algorithms and humans. 

New digital maintenance execution 

systems augmented (AR) and virtual reality 

(VR) will support technicians in real- 



time to perform standardized repairs in a 

safe way by using the right tools, procedures 

and instructions. Know-how will be captured 

digitally, visualised and made available 

for everybody. Operators will receive 

optimized setpoints for their production 

lines, can feed the systems with additional 

relevant information that will further improve 

the analytical model. Digitisation and 

humans go “hand in hand”. 

From 2025 and beyond: 

The future is a concept – 

it doesn’t exist. 

We now enter the unknown era of exponential 

change, new business models and 

renewed anthropocentrism. The self-optimising 

prophecy now becomes reality. 

In spite of what I stated above and 

recognising an accelerated evolution and 

change in our asset management landscape, 

realise this: “we ain’t seen nothing 

yet”. Until present evolution, even though 

continuously accelerating, still was very 

much a linear evolution. The way we think, 

work and act are still very much the same 

as they always have been. Admitted, we 

evolved and matured, got wiser and our 

working models went from a very “centralised” 

way of thinking, organising and behaving 

to a “de-centralised” format in the 

‘90s. But the reality today is that change, 

boosted by technology is shifting gear into 

an exponential acceleration. A mind trap is 

that often we overestimate the change that 

will occur in the next two years but underestimate 

the change that will occur in the 

next ten, affecting our strategic judgement. 

Think fi. about quantum computing, a 

technology rapidly maturing in the shadow 

of today already for some seemingly futuristic 

scenarios. 

As IIoT will make everything and everybody 

interconnected, and everything 

is continuously changing and interacting 

in these new agile environments, we need 

to accept and embrace new distributive 

models. Boundaries becoming vaguer, our 

world and our industries will continue 

to converge. We are undergoing an exponentially 

accelerated merge between the 

physical and the digital world. Therefor we 

need to escape from our traditional way of 

thinking, working and organising within 

our maintenance or production silo, and 

step into this volatile and complex new environment. 

We need to re-invent ourselves, 

our methods and our solutions and stop 

ignoring this change by progressing in a 

stubborn linear pattern: “We cannot solve 

our future problems with the same way of 

THE MOST IMPORTANT IMPACT 

OF TECHNOLOGY IS HOW IT 

CHANGES PEOPLE. 

thinking we used when creating them.” 

Based on real-time information we 

will be able to accurately predict and 

balance supply and demand improving 

our company’s bottom-line without 

compromising the quality of service and 

reach an almost optimum performance, 

by increasing productivity and profit to 

unseen limits. The newly required skills 

often go beyond our human capacity and 

that’s where technology gives us a helping 

hand. It is up to us, people, asset managers 

to question, master and control that 

change. 

Blockchain technology will verify 

every step of the production process or 

provide full transparency in contracts between 

companies, suppliers and vendors. 

Digital fingerprints (encoded asset certificates) 

of each asset and process will be 

created, ensuring reliable and qualitative 

information. All information will be indisputably 

and incorruptibly recorded in 

appropriate registers, ensuring increased 

security of information. Smart automation 

will help us to transform processes 

that require interaction, data interpretation 

and decision making. Robotics will 

allow us to perform smart operations by 

automating processes. 

I could continue and elaborate on many 

other new technologies, but the moral of 

this story is: “nothing has changed, but 

everything changes”. Allow some slight 

exaggeration here, but we are not inventing 

anything new, though. We’re just using 

old, improved algorithm technologies from 

the 9th and 19th century. Just like we used 

thousands of years ago hieroglyphs to communicate 

between different tribes, today 

we call them “emojis”. Wrenches will probably 

still be used, but with an improved design 

or attached-integrated to a robot. And 

algorithms will not be calculated by human 

brains but will run on supercomputers. 

Conclusion: “Prediction is very difficult, 

especially if it’s about the future …” 

At “The Grain” we strongly believe that 

combining artificial and human intelligence 

is key for future success of both our 

industry and humanity, hence our wellbeing 

and our welfare. The role of people 

will remain strategic, only the content of 

our jobs will change. Thanks to technology 

we can get rid of repetitive and often boring 

routine work, in many cases leading to malpractices, 

and concentrate on rewarding 

and true valuable input. Or to put it simple, 

“We are for questioning, AI for answering”. 

Einstein already stated it clear: “If I had an 

hour to solve a problem, I'd spend 55 minutes 

thinking about the problem and five 

minutes thinking about solutions.” Hence, 

Technology provides the know-how, humans 

the know-why. 

The most important impact of technology 

is how it changes people. Therefore, 

it is at its best when it brings people together 

and services our purposes! Hence, 

we should not be concerned about the exponential 

change in artificial intelligence 

or robotics, but more about the stagnant 

attitude in human intelligence. Beware 

however, it is not the robots taking over, 

but it is the men who play with toys that 

are to be feared. That’s were ethics enters 

into the equation. Technology is ethically 

neutral, until wrongly applied by us! 

Let us embrace technology and use it for 

the right purpose with a clear anthropocentric 

focus, i.e. for the use and benefit 

of humanity and in this case our maintenance 

engineers, operators and asset 

managers in general. Allow me to quote 

Einstein once more, confirming that indeed 

nothing really changes: “Why does 

this magnificent applied science which 

saves work and makes life easier bring us 

so little happiness? The simple answer 

runs: Because we have not yet learned to 

make sensible use of it.” 

Well dear Albert, we might get there. 

Humans will remain human, technology 

will just leverage our skills, if done 

right! Man, and machine will keep living 

and working side by side like they’ve 

done for ages, but now for the better. AI 

will provide answers and insights to our 

questions in maintenance, process and 

organisation. Thanks to AI, we have the 

opportunity to embrace that necessary 

change. We are heading for a renewed 

era of Uber-anthropocentrism, unlocking 

human potential and driving a new 

industrial renaissance. Human ingenuity 

will excel! 

[1] As a matter of facts algorithms can 

be traced back to the 9th century and 

linked to the Persian mathematician-astronomer 

Abdullah Muhamad bin Musa 

al-Khwarizmi, father of algebra(..) 



Measurement Traceability for 

the Controlled Environment 

JUSTIN WALSH, Business Development Engineer, Vaisala Inc. 

The measurement of environmental 

conditions is increasing 

in relevance and influence 

in industry. As efficiency 

standards and capabilities 

continue to evolve, these 

conditions are being better 

monitored and controlled to 

optimize performance in a 

number of applications. The 

amount of information available 

and its use for analytics is 

growing exponentially, making 

it more important than 

ever to base decisions on accurate 

and reliable data. 

SOMETIMES OVERLOOKED or taken 

for granted, the data from environmental 

sensors for parameters such 

as relative humidity, temperature and 

carbon dioxide provide powerful control 

understanding to the operations 

they serve. Relevant examples are data 

centers, who rely on accurate humidity 

and temperature measurement to 

protect their IT infrastructure, as well 

as the CO2 sensors responsible for the 

efficiency and agility of a Demand Controlled 

Ventilation (DCV) system in a 

school or office. While the general task 

of these sensors is not especially difficult, 

problems from sensor failure or 

inaccuracy can have a large impact on 

critical operations. Measurement underperformance 

can lead to decreased 

efficiency, non-compliance of operating 

conditions, unplanned down-time, 

or production loss. 

Maintaining accurate measurements 

is the key to long-term operational efficiency, 

and Computerized Maintenance 

Management Systems (CMMS) can use 

sensor data from more locations and in 

greater detail than ever before. Incorporating 

environmental sensor data 

into these systems and others, such as a 

digital twin, or Industry 4.0 and IoT networks, 

affect the analytics and predictive 

indicators that we gain from those systems. 

This places more responsibility on 

the measurements from these sensors, 

and our efforts to maintain them to a 

high standard. 

Information from the calibration of 

humidity, temperature and CO2 sensors 

can be tracked to provide insight into 

sensor health and stability that will improve 

preventive maintenance schedules 

and to help mitigate risk. Digitalization 

of these operations relies on data from 



sensors whose readings will inherently 

drift in their accuracy, requiring calibration 

and adjustment to maintain peak 

performance. 

Calibration ensures 

data integrity 

All measurement sensors are going to 

lose accuracy over time, through normal 

continuous use or environmental 

influences. One of the best ways to understand 

an instrument’s measurement 

performance is to assess its accuracy, and 

calibration is the only way to definitively 

determine how accurate these instruments 

are. During calibration, it is determined 

how much, if at all, the measurement 

deviates from a defined reference, 

and adjustments can be made to preserve 

measurement accuracy, and quality data. 

The maintenance activities for some 

of these sensors can pose challenges of 

their own, as it can be inconvenient or 

impossible to remove and send an instrument 

to a calibration lab for service. 

In many cases it is preferred to calibrate 

in-situ with a spot-check or in-house 

calibration laboratory. An example of 

this is in cleanrooms, where the transport 

of equipment in and out of the 

space is a time-consuming process. Difficulty 

lies in performing that calibration 

or adjustment to a high standard, 

as not all calibration tools are the same, 

and the quality of references is of decisive 

importance. 

The right tool for the job 

Portable reference equipment can 

provide a versatile and cost-effective 

way to maintain sensors at a high 

level by using them as transfer standards. 

They can be of high accuracy 

that meet or exceed the unit being 

evaluated and maintain traceability 

to international standards. Transfer 

standards are used to transfer a 

measurement parameter from one organization 

to another, from a primary 

standard to a secondary standard, or 

from a secondary standard to a working 

standard in order to create or 

maintain measurement traceability. 

Traceability is the relating of 

the measurement back to the international 

system of units (SI units) 

through an uninterrupted chain of 

comparisons, all with stated uncertainties. 

Establishing a high degree of 

confidence in the measurement becomes 

even more important for portable 

instruments, as their accuracies 

ONE OF THE BEST WAYS 

TO UNDERSTAND AN 

INSTRUMENT’S MEASUREMENT 

PERFORMANCE IS TO ASSESS 

ITS ACCURACY. 

do not often exceed that of the units 

under test. It is very difficult and expensive 

to maintain reference equipment 

of higher accuracy than many 

high end humidity, temperature, barometric 

pressure, and CO2 sensors 

for occupied condition monitoring. 

They are likely to be a 1:1 comparison, 

or slightly better, but rarely meeting 

the traditional 4:1 test accuracy ratio. 

Therefore, it is of great importance 

that your instrument be calibrated 

against an SI-traceable reference to 

ensure the quality of measurement 

data. Additionally, proper procedure 

and best practices must be followed, 

because even the best reference used 

improperly can degrade accuracy. 

Further increasing the trustworthiness 

of a traceable calibration 

is the accreditation of the service 

provider that has calibrated the reference 

standard or transfer standard. 

ISO/IEC 17025 accredited calibration 

service providers have been certified 

to provide traceable calibrations with 

detailed uncertainty information, 

proper environmental conditions, 

and methods by competent personnel. 

In order to ensure these high 

standards, these laboratories are audited 

regularly. 

Assessing traceability 

How do you know if your instrument is 

indeed SI-traceable? One way is to study 

its calibration certificate. For example, 

the following information should be 

available: 

1 Calibration results include measurement 

uncertainties 

2 All calibration references are identified 

3 Notes on how uncertainties are 

determined and what uncertainty 

sources are included 

4 Description of how the SI traceability 

was established 

5 Reference and ambient conditions 

Conclusions 

The more important and integrated 

the sensor data is to your operation, 

the greater the assurance is needed 

for that data to be accurate and verifiable. 

It is encouraged that equipment 

and facility managers select the best 

methods and providers when planning 

their calibration and maintenance 

activities. 



05-Steps to Develop Drilling 

Organization Asset Integrity 

Management Program 

Asset Integrity is the ability of an asset to perform its function effectively 

and efficiently throughout its lifecycle while safeguarding life and the 

environment. In other words, an asset in question is required to perform its 

intended function as per the design intent. An Asset Integrity management 

system is about taking care of the aspects of people, processes and 

equipment interaction effectively to achieve the design intent of an asset. 

MOHAMMAD R. ASHRAF & NASSER M. BALHARETH, 

Asset Reliability & Integrity Management Division, Consulting Service Department, Saudi Aramco, Dhahran, Saudi Arabia 



AN ORGANIZATION SHOULD DEVELOP A UNIFIED CORPORATE ASSET INTEGRITY MANAGEMENT FRAMEWORK 

BASED ON ASSET MANAGEMENT, INSPECTION, MAINTENANCE & OPERATION REQUIREMENTS. 

A MAJOR ACCIDENT HAZARD (MAH) is the 

failure of one or more factors related to 

people, process and equipment. To achieve 

an effective asset integrity, the organization 

needs to have more focus on major accident 

hazards as these incidents are rare, but the 

consequence is large. Drilling operations 

are exposed to a number of hazards such as 

corrosion, fatigue, accidental damage, extreme 

weather conditions, geological, geotechnical, 

change in used technology etc. 

throughout their life cycle which may lead 

to a major accident. Drilling operation may 

also have additional complexities due to the 

involvement of many stakeholders, such 

as drilling contractor, drilling equipment 

supplier, operator etc. It therefore becomes 

more important for drilling to develop a 

structured asset integrity management 

program where the proper integration of 

different stakeholders is established with 

clear roles defined to reduce the probability 

of hazard realization from a major accident 

hazard (MAH). This brings the organization 

focus on managing the people, process 

and equipment/machinery-related weakness 

to avoid the MAH and ensures that the 

asset functions as per the design intent. 

In this article, A 05-step process is suggested 

to develop an overall Asset Integrity 

Management program for a drilling organization 

to manage a Major Accident Hazard 

threat and its realization. 

The operation of a drilling company is 

more complex as several stakeholders, such 

as operator (drilling, reservoir, geological, 

operations team), Drilling service companies 

(drilling fluids, cement, BOP manufacturer, 

well casing design, drilling bits 

etc.), classification societies and Drilling 

company/contractor (Rig design & maintenance, 

driller, rig crew etc.) are involved 

in achieving the common goal of drilling. 

Due to the very nature of a drilling operation, 

the organizations need more focus on 

major accident hazards as these incidents 

are rare, but the consequence is large. A 

major accident hazard is the failure of one 

or more factors related to people, process, 

and equipment. While drilling organisations 

manage control and critical drilling 

equipment well, based on proponent 

in-house standard and/or international 

standard such as API/ISO, the complexity 

and various stakeholder involvement often 

lacks the availability of a structured asset 

integrity program to manage the major accident 

hazard. An asset Integrity Management 

Program in any organization requires 

the integration and utilization of company/ 

stakeholder’s internal process, procedure, 

standard & guidelines and tools e.g. forms, 

checklist etc. for proactive control & mitigation 

of a major accident hazard. 

In this paper, A simplified 05-step process 

is proposed for a drilling organization 

to develop an organization-specific Asset 

Integrity Management program. 

Step 1- Development of 

Asset Integrity Management 

Framework 

An organization should develop a unified 

corporate Asset Integrity Management 

framework based on asset management, inspection, 

maintenance & operation requirements. 

The framework should detail the 

integration of a drilling company/contractor 

internal process/procedure and other 

stakeholder process/procedure to achieve 

avoidance/mitigation of a major accident 

hazard. 

The framework should define the people, 

process and equipment/system integrity 

requirements to avoid a potential major accident 

hazard by evaluating following basic 

aspects e.g. 

• Do we understand what can go wrong? 

• Do we know what systems are in place 

to prevent this from happening? 

• Do we have assurance and verification 

functions that these systems will work? 

• Do we have proactive visualization of 

integrity performance based on leading/lagging 

KPI? 

Based on the evaluation of the above 

aspects, the framework should include 

a high level requirement of the following 

Integrity management element 



• Policy for Asset Integrity Management 

and utilization of any current/ 

existing management system to ensure 

Asset Integrity 

• Structure & governance of asset 

integrity management to effectively 

manage the asset integrity related to 

activity and function 

• Identification of barrier and its 

component e.g., key competency, 

safety critical element (equipment/ 

component/system), critical process 

/ procedure, interface requirements 

for drilling service and function. 

• Development of Assurance-function 

based on existing operation, engineering, 

technical, maintenance 

requirements 

• Verification requirements/activity 

based on regular review of current 

integrity status (Use existing forms/ 

checklist, program to the maximum 

extent) 

• Visualization of existing rig integrity 

status based on identified Safety 

critical element (SCE) integrity status 

and current status review 

• Performance monitoring based on 

leading and lagging KPI related with 

asset integrity management. 

Step 2- Develop Asset 

Integrity Management 

Structure 

Drilling company/contractor should ensure 

that the existing management program 

provide the integrity management 

structure and function to specifically 

support the verification of safety critical 

equipment/element (SCE) assurance 

functions to avoid/mitigate MAH. This 

should include an asset Integrity Management 

team both at corporate/site 

level with details RASCI (responsible, 

accountable, support, consultative and 

information) chart to execute the Integrity 

management specific task leading to 

adequate focus on major accident hazard 

and related risk realization. 

The company should create an Identify 

Asset Integrity Management/Engineering 

team, and assign responsibilities 

to address specific asset integrity-related 

activity/task such as: 

• Development, execution and monitoring 

of asset integrity management 

specific internal procedure & guide 

• Operational risk management guide, 

risk register development and utilization 

of risk register as a tool to 

manage the risk. 

• Development & utilization of Well 

barrier schematic for different types 

of Well, and an area to support asset 

integrity 

• Evaluation of MHA related safety 

critical task analysis (SCTA) and its 

effectiveness through assurance & 

verification process to minimize human 

error. 

• SCE (people, process, equipment/ 

system) identification and its management 

based on SCE performance 

standard development, execution 

& update. Below is a list of some required 

performance standard examples 

• Well test and control 

• Blow out preventer system 

• Choke & Kill system 

• Rig move & structure 

• Main power system & supply 

• Emergency power system & supply 

• Fire and gas detection 

• Third party equipment 

• Emergency shutdown system 

• Lifting Equipment 

• Execute, verify & monitor SCE integrity 

assurance based on existing 

Inspection, Maintenance, testing 

(IMT) and operating procedures 

• SCE (both hardware and software) 

Performance monitoring and continuous 

improvement 

Step 3- Create effective risk 

management process to avoid 

Major Accident hazard 

The organization should develop a decision 

support flow diagram with inbuilt 

action details for all the key causal risk of 

any major hazardous event (Refer Figure-1) 

to develop an effective risk management 

for asset integrity. This flow diagram 

will provide the necessary insight 

about the inclusion of required assurance 

and verification task related with process, 

people and equipment in the applicable 

performance standard of identified SCE. 

To have a detailed insight about the 

availability of control, and mitigation 

barrier of an identified causal risk, an 

organization can use other safety study 

tools such as SWIFT (Structured what 

if technique), Bow-tie analysis etc. The 

generated information can be used to 

develop a risk register at rig level with 

existing control and mitigation details 

on all the operation, process, mechanical, 

and people-related causal risks. The 

developed risk register can be aligned 

with the overall ERM (Enterprise Risk 

Management) process of the organization 

and used as a tool for SCE integrity 

management with focus on control & 

mitigation effectiveness. A risk tracking 

system should be available to monitor 

the status of identified risk based on the 

listed control & mitigation barrier/SCE 

effectiveness. The risk management 

process should define a clear RASCI 

(Responsible, Accountable, Support, 

Consultative and Information) chart for 

the employee/management and devise 

a mechanism to review and update the 

identified risk on a regular basis. 

Step 4- Establish Asset 

Integrity Model Barrier and 

SCE management process 

Drilling company/contractors should 

develop a barrier model for people, process 

and Well control/critical equipment 

Figure-1 Decision support flow diagram based on hazard 


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(both well and rig related) in some form of 

barrier model to have visibility and clarity 

of any hazard/threat realization concept 

across the organization. It is worth mentioning 

that the representation of hardware 

equipment/SCE in the form of a rig 

barrier along with Well barrier schematic 

(for hardware & software) will provide the 

required visualization of a hazard/threat 

as an integrity model being followed by the 

subject organization operation. 

Shown below (Figure-2) is a typical 

barrier concept based on the swiss cheese 

model which represents the hardware 

(equipment) flaws for hazard and threat realization 

being followed for Asset Integrity 

Management as an AIMS Model representation 

for a process plant. Drilling organizations 

can develop a similar AIMS model as 

applicable. 

Further, organizations should develop 

a clear process for SCE identification and 

its review/update process for the effective 

monitoring of a Major accident hazard. A 

Safety Critical Element (SCE) is defined 

as a system, Item of equipment, Person or 

Procedure that is specifically identified 

using HEMP (Hazard effect management 

process) and included in the facility Safety 

Case (highly recommended to develop for 

drilling organization) as an asset that: 

• Failure of which could cause or 

contribute substantially to a Major 

Accident or Major Environmental Accident; 

and/or 

• Purpose of which is to prevent or limit 

the effect of a Major Accident or Major 

Environmental Accident. 

Once an SCE has been identified, the organization 

should align the identified SCE 

(hardware & software) with any operational 

risk as captured in the risk register and 

develop a barrier & related safety critical 

equipment/system performance standard 

based on key IMT (Inspection, Maintenance 

& Testing) and operational assurance 

activity, to avoid the risk realization. 

A performance standard defines the 

critical function, specific, verifiable, realistic 

and achievable performance requirements 

with which the SCE shall comply 

throughout the lifecycle of the installation. 

It can be expressed in qualitative or quantitative 

terms, but should produce a Pass 

or Fail result, which is used as the basis for 

managing the hazard. 

A barrier/SCE management process 

should utilize a performance standard, 

its verification and reporting along with 

other activity based on a PDCA (Deming 

cycle) concept. This process should 

include all the required instruction and 

linking of existing process/procedure, 

along with the roles & responsibilities of 

the Asset Integrity assurance team, the 

OME team and the Asset integrity verifier. 

All the identified barrier/SCE should 

be verified against these performance 

standards to ensure its integrity. 

A typical SCE management flow diagram 

(Figure-3) based on performance 

standard is depicted below for reference. 

Step-5 Identify leading and 

lagging asset integrity specific 

KPIs and develop Asset 

Integrity dashboard 

The identification of leading and lagging 

indicators are key to managing the organization 

asset integrity performance factors 

proactively that are responsible for major 

accident hazard realization e.g. the lack of a 

risk management process, deficiency in internal 

procedure & guidelines, lack of barrier/safety 

critical element management 

and lack of compliance audit & verification. 

On many occasions, clear boundaries in 

a leading and lagging indicator are fuzzy. A 

gas kick for example, is a leading indicator 

for a possible blow out, whereas it is a lagging 

indicator for well bore fluid that has 

already entered the well bore due to a barrier 

failure. 

A leading indicator is considered to 

be a predictive set of parameters/course 

of action which delivers early information 

on barrier performance. It must be 

measurable and recognizable and provide 

benchmark operation & organization performance 

for asset integrity management. 

Lagging to leading events for drilling 

operation can be presented as progression 

arrow define by Nafiz Tamim et. al. (2016) 

in Figure-3 

Organizations should develop a process 

for the identification, review and 

monitoring of leading & lagging indica- 



Figure-3 Transition of lagging to leading indicators in drilling operation 

Figure-4 Leading Indicator identification tree with example 

tors based on the concept of progression 

arrow defined in Figure-3 and below is an 

example of a leading indicators identification 

tree for drilling operation, Refer 

Figure-4 proposed by Nafiz Tamim et. al. 

(2016). 

Organizations can further evaluate 

and adopt the below KPI as applicable for 

Asset Integrity performance monitoring. 

• Corporate level lagging KPI “Asset 

Integrity Index” based on rig level 

integrity related KPIs e.g., corrosion 

management, process & procedure 

Compliance, gas kick, BOP, well control 

etc. 

• Leading KPIs related with SCE management 

and its integrity status 

• Number of processes & procedures 

in compliance 

• Number of anomalies related with 

barrier/SCE that were not closed out 

by the planned due date at the end of 

each quarter 

• Number of barrier/SCE not meeting 

performance standard 

• Number of activations of barrier/ 

SCE e.g., gas kick 

• Number of barrier/SCE past a 

planned completion date (barrier/ 

SCE backlog) 

• Number of barrier/SCE did not 

follow MOC process 

Based on KPI monitoring, an organization 

should develop a dashboard for the 

clear visualization and reporting of all the 

identified SCEs, and an overall integrity 

status. 

A typical dashboard reporting for 

safety critical element based on the swiss 

cheese model is represented below in 

Figure-5. 

Conclusion 

Establishing a structured asset integrity 

management program is key to managing 

a major accident hazard. Using the 

05-step process as detailed in this paper, 

an organization can develop a drillingspecific 

asset integrity program to 

achieve excellent performance in overall 

asset management, while addressing the 

avoidance and mitigation of a major accident 

hazard. 

REFERENCES 

[1] Saudi Aramco (2015), Asset Integrity Management System, issued March 2015, Dhahran, Saudi Arabia. 

[2] CCPS, 2010. Guidelines for Process Safety Metric, Center for Chemical Process Safety, John Wiley & Sons, Inc. 

[3] IOGP, 2011, Process Safety-Recommended Practice on Key Performance Indicators, Report No. 456, International Oil & Gas Producers, UK. 

[4] UK Energy Institute (2007), Guidelines for the Management of Safety Critical Elements, Second Edition, Published March 2007, ISBN 978 0 85293 462 3, 

[5] UK Health and Safety Executive (2006), Assessment Principles for Offshore Safety Cases (APOSC), issued March 2006, 

available online from: http://www.hse.gov.uk/offshore/aposc190306.pdf, 

[6] ANSI/API, April 2010. API Recommended Practice (RP) 754. Process safety performance Indicators for the Refining and Petrochemical Industries. API Publication. 



Infrastructures Physical Assets High 

Performance Achievement based on 

Reliability and Maintenance Program, 

A.I and Asset Integrity Management 

Nowadays the world invests around $2.5 trillion a year in infrastructure physical 

assets such as transportation, power, water, and telecom systems on which businesses 

and populations depend. Yet this amount continues to fall short of the world’s everexpanding 

needs, which results in lower economic growth (MGI’s 2013 report). 

INDEED, THE ECONOMIC CRISIS IN 2008 already 

led to enormous spending cuts across 

the globe. In Europe, the post-war infrastructure, 

especially bridges, is ageing. Despite 

that, the maintenance backlog, i.e., the 

amount of maintenance and rehabilitation 

that should have been completed in order to 

maintain infrastructures in good condition 

but has been deferred, is growing considerably. 

This problem could be being amplified 

because of the COVID-19 Pandemic, that 

causes further service cancellations, delays 

and consequently spending cuts. 

This article aims to demonstrate the 

importance to implement the reliability and 

maintenance program during infrastructure 

concept and design phase, as well as A.I 

integrated to Asset Management and Asset 

DR. EDUARDO 

CALIXTO, 

Eduardo Calixto Consulting 

(ECC), RAMS Engineer and 

Asset Management Expert, 

EFNMS member 

Integrity Management during operation 

phase. 

2 – Reliability and 

Maintenance Program for 

Infrastructure 

The maintenance activities applied to infrastructures 

physical assets need to be under 

the context of a Maintenance Program, that 

considers different reliability engineering 

methods to be implemented throughout 

the different life cycle phases from concept 

to the decommissioning phases. 

The Reliability Centred Maintenance 

(RCM) is a method initially applied 

during the design phase, that aims 

to define the maintenance tasks based 

on the infrastructure failure modes, 

causes and effects as well as the associated 

risk. The information that is input 

in order to perform the RCM are the 

infrastructure’s failure mode and effect 

analysis (FMEA). In the Railway Industry, 

the infrastructure’s systems play an 

important role in terms of the safety and 

performance of the railway. One good 

example is the rail component that in 



case of failure, will trigger impact on a 

Railway System’s operational availability 

and may trigger a major accident such as 

derailment as shown in figure 1. 

Figure 1 describes the rail RCM where 

the risks are assessed as intolerable based 

on the combination of the cause frequencies 

and the consequence severity. Based 

on such assessment, different maintenance 

task types and the frequencies at 

which they are carried out are defined to 

mitigate the risk, such as Visual inspection 

and Track Road Vehicle Inspection 

(Ultrasonic Test). Similar methods comparable 

to the RCM approach is the RBI. 

The Risk Based Inspection (RBI) 

is applied initially in the design phase 

and later during the operation phase. 

The RBI Infrastructure scope focuses 

on a failure that can trigger a major 

accident. The RBI method is implemented 

based on specific procedures 

and standards such as: API 580, API 581 

and EN 1691, which can be qualitative 

or semi-quantitative based on the RBI 

application levels. 

In addition to qualitative methods, 

it is important to have quantitative 

analysis to predict the Infrastructure 

Physical Assets RAM performance such 

as Lifetime Data Analysis, also popular 

known as Weibull Analysis, RAM 

Analysis and Reliability Growth 

Analysis. These methods can be applied 

to assess, verify and validate the infrastructure 

system’s RAM performance. 

However, the Probabilistic Degradation 

Analysis (PDA) is more 

appropriate to predict such an infrastructure’s 

reliability performance. The 

PDA aims to define the infrastructure’s 

physical asset reliability based on integrity 

degradation failure data related 

to the thickness of a crack, corrosion 

and erosion measured by non-destructive 

test methods. By applying these 

methods, it is possible to predict when 

the functional failure will be achieved 

based on the trend of degradation such 

as thickness or depth (Crack or Corrosion) 

and by considering the degradation 

limit as shown in figure 2. 

The pink line 1.5 mm in figure 2 is the 

limit of corrosion, where a functional 

failure is expected to occur. The other 

different lines are different measurements 

that predict the trend of the 

evolution of corrosion a different points 

in time. Therefore, if we project the 

interception of each of these lines with 

the straight line (1.5 in Y axis) in x axis, 

there will be different times of functional 

failures. These functional failure times 

are used to predict the reliability and 

the failure rate function. Based on such 

information it can be defined when the 

inspection needs to take place. 

Figure 1: Rail FMEA/RCM ECC Database. Source Eduardo Calixto 2021. 

Figure 2: Probabilistic Degradation Prediction. Source: Calixto E, 2021 – Software 

Weibull++ HBK. 

3 - Maintenance 4.0 applied 

for infrastructures. 

Artificial Intelligence (A.I) aims to enable 

a machine to think and make its 

own decisions based on data collected 

and assessed automatically without 

any human intervention. Based on the 

EFNMS – European Committee Maintenance 

4.0 (ECM4.0) 2021, Industry 4.0 

is a new paradigm and the last industrial 

revolution, that has been implemented 

across the globe intensively in the past 

five years and is supported by the utilization 

of Enabling Digital Technologies 

named 4.0. 

Concerning the application of A.I for 

Infrastructures Physical Assets, machine 

learning is applied for the equipment 

criticality and critical alert levels classification, 

failure regression predictions and 

the automatic application of the Prognostic 

Health Management (PHM). In 

the case of an Infrastructure system, the 

stress factors measured by sensors are 

vibration, voltage, temperature, humidity; 

Non-destructive test measurements 

are also taken such as crack thickness, 

corrosion depth and other physical parameter 

that lead equipment degrade to 

functional failure. 

The Deep Learning (DL) methods, a 

special type of Machine Learning, can 

also be applied to support the preventive 

maintenance of an Infrastructure 

System. The DL is a more sophisticated 

machine learning method, that applies a 

deep neural network that encompasses 



several hidden layers as shown in figure 

3. The principles of Deep Neural network 

consider different layers such as Convolution 

Layer, Pooling Layer, ReLu, Fully 

Connected, Softmax and the output image 

classification. (https://www.eduardocalixto.com/paper-2021/) 

Despite the advantage of applying A.I 

Deep Machine Learning and other A.I 

methods as well as reliability engineering 

methods, it is necessary to integrate such 

methods in an Enterprise Asset Management 

System. This will enable the management 

of the preventive maintenance 

tasks defined for all these methods and 

will ensure that the proper resources are 

allocated in the proper time to mitigate 

the Infrastructure Physical Asset risk of 

unavailability along time and the possibility 

of a major accident. The next item will 

discuss the Asset Integrity Management 

as part of the Asset Management. 

4 - Infrastructure Asset 

Integrity Management (AIM) 

An Infrastructure Physical Asset integrity 

failure may lead to unavailability, or a major 

accident with multiple fatalities. Therefore, 

the so-called safety critical elements (SCE) 

are the physical assets, which in case of failure, 

may lead to a major accident such as jet 

fire, toxic cloud release, explosion, fire, toxic 

product spill, aircraft crash, trains collision 

or derailment. In fact, a major accident can 

be triggered by Infrastructure integrity failure, 

software, hardware or human error or a 

combination of such factors. 

In order to mitigate such risks of a major 

accident it is necessary to implement a 

Reliability & Maintenance (R&M) Program 

immediately at the first stage of the concept 

and design of a physical asset’s life cycle and 

implement all recommendations from such 

R&M methods. After that, it is necessary to 

implement the risk management and inspection 

& test program concerning the A.I 

technologies during the operation phase. 

The Asset Integrity Program can apply 

the same elements of the AM defined in ISO 

55000 such as context of the organization, 

leadership, planning, support, operation, 

and performance evaluation but needs to 

focus on the critical safety elements management. 

Since 2010, the new era of Industry 4.0 

has become a reality for many industries 

across the globe. In the last five years new 

IOT technology development has been 

integrated with EAM solutions concerning 

technologies such as Big Data, PHA and Machine 

Learning, Reliability 4.0 and the usual 

Figure 3: Flow Chart of A.I Deep Learning applied for Bridge. 

Source: Mohammad Noori.0 2021. 

Figure 4: AIM flow into AM Process. Source: Stewart Paul. Integrity PRO, Enkelt,2018 

maintenance management routine. 

Despite all development that enables 

integrated AM, too much focus has been 

given to availability performance and 

maintenance, with a lack of effort for safety 

concerning the safety critical element 

management. 

However, it is very important to establish 

a process to enable an effective AIM 

flow integrated to the AM process. Figure 

4 describes the AM and AIM flow, highlighted 

in green, as part of the AM flow. In 

the case of AIM, safety management takes 

place in the fourth step, which encompasses 

the safety routine management 

(safety meeting and incident reports) 

as well as the Barrier Management. The 

Safety Barrier model is part of the barrier 

management that defines the level of risk 

of each SCE automatically, based on real 

online data. 

Since the SCE is defined based on previous 

risk analysis considering severity 

criticality, the Risk Management of such 

SCE performed by the Barrier Model is 

automatically updated, enabling Asset Integrity 

and helping Safety managers manage 

the risk of the SCE on a daily basis. 

The Infastructure’s Physical Assets 

need to have in the end, all information 

integrated in an EAM that encompasses 

the best A.I technologies and reliability 

engineering methods to enable the leaders 

to make a fast and reliable decision. 


MAINTENANCE 

& RELIABILITY 

IN PRACTICE 

A Virtual Case Studies Event 

June 23-24, 2021


TOMÁŠ HLADÍK, 

Principal Consultant, 

Logio S.R.O. 

Part 3 

Monetizing Data 

in Maintenance: 

Data-driven Spare Parts Management 

Management of spare parts and other materials 

needed for realization of maintenance processes 

is one of the key functions in physical asset 

management. Especially in power generation, oil 

and gas and heavy chemical industries, spare 

parts inventories can easily add up to tens 

of thousands of various items, in a value of 

hundreds of millions of euros. 

EFFICIENT SPARE PARTS inventory management 

can have significant impact on the 

financial performance of the company. Better 

spare parts management can lead to improvement 

of financial performance of the 

company. Spare parts inventory can lock 

in significant amounts of working capital. 

This article summarizes recommendations 

for effective spare parts inventory management 

and spare parts optimization using 

various sets of data and statistical analytical 

methods. 



1 Eight rules of good spare 

parts management 

In our previous research, we refined the 

following eight rules – best practices – for 

good spare parts management: 

1) Focus on preventive maintenance 

– for preventive maintenance no 

inventories of spare parts need to 

be held. 

2) Solve problems in spare parts processes. 

3) Segment your spare parts portfolio. 

4) Evaluate spare parts criticality. 

5) Apply suitable forecasting methods 

and verify their accuracy and reliability. 

6) Use special methods for intermittent 

demand items. 

7) Treat your master data well: Identification 

and naming of spare parts 

8) Consider the whole lifecycle of your 

assets while making decisions related 

to spare parts. 

In this issue of Maintworld we will focus 

on forecasting – the essential element in 

inventory management. Follow the rules 

5 and 6 to apply suitable forecasting 

methods and use special forecasting 

methods for spare parts with intermittent 

consumption. Add rule 7 to improve 

identification and naming of your 

spares in master data. 

Spare parts management starts 

with good forecasting 

The next step in the specification of 

optimum spare parts inventory management 

regime is the prediction of future 

consumption of the items in stock. The 

forecast is always based on transactional 

data from information systems – history 

of spare parts consumptions, which must 

be representative (meaning sufficiently 

long). In the case of spare parts, we usually 

work with a history of three to ten years 

(depending on industry). Three years of 

recorded history seems to be the minimum 

for intermittent items. A general 

rule here applies: the longer the history, 

the better and more reliable the forecast. 

When analyzing historical consumption, 

we need to carefully distinguish between 

material consumed for planned maintenance 

(planned shutdowns, turnarounds, 

preventive maintenance) and spare parts 

issued for unplanned (corrective) maintenance 

– repairs. In forecasting, we must adjust 

the history for planned maintenance. 

In the forecasting process, items should 

be treated individually, according to the 

character of their consumption. Items with 

common demand patterns (high runners 

– fast moving items like fasteners, etc.) can 

be forecast using a number of standard statistical 

methods normally used in inventory 

management (moving average, exponential 

smoothing, Holt’s exponential smoothing, 

trends, seasonal indexes, Winter’s method, 

etc.). Items with intermittent demand require 

a special suitable method to be applied. 

The use of standard methods of prediction 

and inventory management in case of intermittent 

items results often in a substantial 

overestimate of future consumption and 

therefore excessive inventory level. 

Figure 7: Intermittent demand in maintenance (item: Spiral sealing DN25-40 RF) 

Intermittent demand is the pitfall 

of spare parts management 

One of the specific problems in spare parts 

inventory management is the nature of 

spare parts consumption - intermittent 

demand. If we analyze the consumption 

history of a typical spare part, we find that 

the historical consumption in most of the 

analyzed periods amounted to zero. Such 

infrequent or intermittent demand, usually 

with demanded quantity of just a few pieces, 

is very typical for spare parts and other 

maintenance inventories. An example of 

the consumption history of an intermittent 

demand item is presented in Fig. 9. 

In maintenance, intermittent demand 

is quite often combined with long supplier 

SPARE PARTS MANAGEMENT AS A PART OF PHYSICAL 

ASSET MANAGEMENT HAS SIGNIFICANT IMPACT ON 

FINANCIAL STATEMENT OF THE COMPANY. 



Figure 9: Validation table with potentially duplicate items found in master data. 

leadtimes. For maintenance inventory 

management, intermittent demand and 

long leadtimes are a tricky complication, 

often leading to large overstock. The main 

problem with managing and forecasting 

intermittent items is that the standard 

forecasting methods used for fast moving 

goods (for instance moving averages, exponential 

smoothing, Holt’s and Winter’s 

method, constant or regression models 

with seasonal indexes, etc.) simply do not 

seem to work for these items. In case of 

intermittent consumption, special statistical 

methods (such as bootstrapping 

or Smart-Willemain method) need to be 

applied. 

Smart and Willemain (2004) suggested 

a stochastic simulation forecasting 

method. Using this method it is possible 

to specify minimum inventory level (reorder 

level) in order to ensure fulfilment 

of requirements with target probability 

(logistic service level, target of availability). 

The method is based on random sampling 

from the history of consumption. 

In statistics, similar methods are called 

bootstrapping. 

Besides intermittent items, in a large 

maintenance inventory we can also find 

fast-moving items with stable and high 

regular consumption. These are especially 

items of common consumables like fasteners, 

generic gaskets, or bearings. For 

these items, standard methods of inventory 

management and future demand 

forecasting can be applied. 

Identification of spare parts and 

cleaning master data 

In spare parts optimization projects, we 

quite often face various problems with 

quality of spare parts master data – especially 

naming: issues with unstandardized 

naming or incorrect names, names in various 

languages, different word order, typos 

etc. hinder significantly all efforts in spare 

parts optimization and generally result 

in duplicate (or multiplicate) master data 

records for identical materials (identical 

spare part is stored in several master data 

records with different names). In order 

to clean spare parts master data, we apply 

advanced data analytics on master 

data to identify or cluster duplicate (or 

similar) items. This key analytical method 

is “matching” – comparing names and 

certain master data attributes to assess 

similarity of spare parts. For this we use 

multicriterial comparisons. To analyze 

spare parts’ names, metrics like Levenshtein 

or Hamming distance are used and 

combined with triplets analysis (comparing 

all triplets-threes of characters found 

in all names in master data). 

The result are clusters of similar or duplicate 

items found in master data. These 

groups of highly similar items are given 

to maintenance engineers for validation. 

After duplicate items are confirmed, master 

data can be rectified – correct items is 

selected to be used and all duplicate items 

are erased or deactivated. Examples of 

Matching are presented in Figure 8 and 

Figure 9. 

Good spare parts management 

has significant impact and benefits 

It can be concluded that spare parts 

management as a part of physical asset 

management has significant impact on 

financial statement of the company. Good 

spare parts management brings the following 

benefits: 

• Optimum spare parts quantities are 

purchased 

• Optimal purchasing cashflow 

• Lower inventories 

• Less unused inventories 

• Higher availability of needed spare 

parts 

• Good risk management 

Various sets of data can be used to support 

or optimize spare parts management, especially 

in spare parts segmentation, criticality 

assessment, forecasting, master data 

rectification. The examples demonstrated 

in the paper indicate ways to utilize vast 

amounts of data available in organizations 

today – to monetize the data by improving 

efficiency and effectiveness of spare parts 

management processes. 

REFERENCES 

IAS 2 (2009) International Accounting Standard 16 – Inventories, IASCF 2009 

IAS 16 (2009) International Accounting Standard 16 – Property, Plant and Equipment, IASCF 2009 

IAS 16 (2009) Property, Plant and Equipment – Clarification on classification of servicing equipment as inventory or PP&E, IASCF 2009 

Hladík Tomáš, Tulach Petr: Are your spare parts really critical? Euromaintenance 2014, Helsinki Finland, 2014 

Nielsen Helms Erik: Impact on financial performance by physical asset management. The Asset Management Conference 2015, London, UK, 2015 

Tomáš Hladík, Petr Tulach, Eva Heringová: Financial impacts of spare parts inventory management – Finance driven spare parts inventory management. Euromaintenance 2016, Athens, Greece, 2016 

Willemain, T.R, Smart, C.N. and Schwarz, H.F.: A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20, 375-387, ISSN: 0169-2070, 2004 


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