Maintworld Magazine 4/2020

4/2020 www.maintworld.com 

maintenance & asset management 

To Transform or Tinker – 

That is the Question p 14 

TAKING CONTROL OF PLANT DATA WITH PLANTSIGHT DIGITAL TWINS PG 8 ULTRASOUND SPECTRUM ANALYSIS PG 20 BLACK SWANS IN MAINTENANCE PG 38

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Automatically Detect Faults 

ROI Typically Within 12 to 18 Months 

Library of Preconfigured Fault Rules 

Rich Visualization and Reporting 

Predict, Reduce and Eliminate Downtime 

Improve Maintenance Efficiency

EDITORIAL 

Dear friends, 

EVERYONE IN THE EFNMS (European 

Federation of National Maintenance 

Societies, umbrella organization for 

the non-profit National Maintenance 

Societies in Europe, www.efnms.org) 

family is proud to celebrate its 50th 

anniversary! 

Over the years, EFNMS has been 

a great place for cooperation and 

the exchange of knowledge. This has 

led to an increase in the number of 

EFNMS members (i.e. the National 

Maintenance Societies) reaching 24 

in total. On top of that, it is worth 

highlighting the efforts of the EFNMS 

Chairmen, all committed to their 

role, and the inspiration of people and 

organizations to support the EFNMS 

mission. The EFNMS Chairmen have been: Arjo Klijn from The Netherlands, 

Hans Overgaard from Denmark, Hans Klemme-Wolff from Switzerland, Alex 

Stuber from Switzerland and Herman Beats from Belgium. 

The team spirit and creativity within the EFNMS community is unique and 

has contributed to the delivery of constructive results. Main EFNMS activities 

include: EuroMaintenance (a bi-annual conference), the maintenance Body of 

Knowledge (BoK), Certifications (for Maintenance Managers and Maintenance 

Technicians Specialists), Workshops (in the topics of Benchmarking, Asset management, 

and Safety), Surveys (in the topics of Maintenance KPIs and Asset 

management), the GloMe guidebook (Global Metricators, harmonizing EN 15341 

KPIs and SMRP metrics) and the quarterly Newsletter (you can subscribe at www. 

efnms.eu/about-us/newsletter/). All these activities aim to provide added value to 

the members of the 24 National Maintenance Societies. 

It is worth mentioning that most of these activities were undertaken by the four 

EFNMS Committees: the European Asset Management Committee (EAMC), the 

European Certifications Committee (ECC), the European Health Safety and Environment 

Committee (EHSEC) and the European Maintenance Assessment Committee 

(EMAC). An additional Committee focusing on Industry4.0 technologies is 

planned to be established soon. 

EFNMS has also developed international cooperation. It is a member of the 

Global Forum on Maintenance and Asset Management (GFMAM, www.gfmam. 

org), having active participation in the development of its projects. Moreover, in 

cooperation with the Salvetti Foundation, it provides four Excellence Awards to 

encourage maintenance related research (www.salvettifoundation.com/awards). 

Finally, EFNMS is a partner of the European Agency for Safety and Health at Work 

(OSHA, osha.europa.eu) and actively participates in its campaigns. 

More activities have been scheduled for the near future. Any results from current 

activities or any other updates can be found on the official website of EFNMS 

or on the social media. In conclusion, we would like to thank everyone in the 

EFNMS community who has voluntarily contributed all these years and helped it 

grow and flourish. 

Looking forward to seeing you at the EuroMaintenance2021 (www.euromaintenance.net) 

at Rotterdam, The Netherlands, 29-31/03/2021, to celebrate, all together, 

the EFNMS 50 year anniversary! 

Sincerely yours, 

Cosmas Vamvalis 

EFNMS Chairman 

6 maintworld 4/2020 

28 

Safety is our topmost 

priority. In service 

operations, we need to have 

100 per cent traceability 

for all our tools, says Joni 

Janatuinen, Project Engineer 

at Finnair Technical Services.

IN THIS ISSUE 4/2020 

38 

Maintenance 

disasters caused 

by unexpected events, 

generally called “black swan” 

events, may be prevented by 

the development of Industrial 

AI (IAI). 

= 

47 

Interpersonal 

skills in particular 

have been emphasized a lot – 

nowadays engineering students 

often have experience of 

working in teams, and they have 

also trained their communication 

skills during the education. 

Taking Control of Plant Data with 

8 24 

PlantSight Digital Twins 

26 

To Transform or Tinker, that is the 

14 

Question 

28 

Connectivity is Key for Comprehensive 

16 

Maintenance Strategy 

Diagnosing Mechanical and Electrical 

20 30 

Faults Using Ultrasound Spectrum 

Analysis 

22 Searching for the New Equilibrium 

SonaVu… Powered by SDT 

Management of Ultrasonic Data 

in a Power Plant 

Finnair relies on Agilon in its 

Maintenance Repair Operations (MRO) 

warehouse 

Industry 4.0…. A Practical Maintenance 

Perspective and Significant Impacts on 

Our Maintenance 

34 

38 

42 

47 

What’s the right Inspection Frequency 

for Equipment? 

Black Swans in Maintenance 

and Industrial AI: Predicting the 

Unpredictable? 

Empower your Maintenance with a 

System 

Competency Requirements for Future 

Engineers 

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

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

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

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

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

4/2020 maintworld 7

PARTNER ARTICLE 

Taking Control of Plant Data 

with PlantSight Digital Twins 

In the old days, project handover from contractors to owner/operator involved a 

big tractor-trailer and forklifts that moved crates of documents from the design and 

construction contractors to the plant’s records room. Most of that paper was never 

looked at again because it was hard to find a specific piece of information, and what 

one did find was likely outdated. Fast-forward to 2010, and the truck was smaller, 

as more of the data was in digital form — but it was still hard to navigate through 

the different formats and data creation tools to find the specific bit one needed. 

This brief was created by SCHNITGER CORPORATION AT THE REQUEST OF BENTLEY SYSTEMS, INC 

IN 2020, we’re seeing the emergence 

of new tools like Bentley’s PlantSight, 

which aim to use cutting-edge technologies 

to capture, control, and serve out the 

many types of data that represent today’s 

operating asset. From process flow diagrams 

to 3D models of a plant, from asdesigned 

to as-built, and from desktop to 

cloud technologies, PlantSight helps authorized 

engineers and plant operators 

quickly find the one piece of information 

they need to keep their plant or project 

running. 

First, what is PlantSight? 

PlantSight aligns 1D, 2D, and 3D data into 

a single, visual representation of an asset. 

In the screen capture below, a virtual 

gate valve is aligned over the reality captured 

as-is model. Tag numbers are the 

link between the different data types and 

ensure continuity across them all. This 

view is served to users via a web browser, 

making it easy to access from the office or 

a networked tablet in the plant itself. 

Once PlantSight’s digital twin of the 

physical plant is created, it has many potential 

uses. Cloud access means the verified 

data is available to all authorized engineers 

and operators, regardless of their 

8 maintworld 4/2020


physical location. That means improved 

collaboration, on-site and with experts 

offsite, to assess and strategize to resolve 

issues. The unlimited computing power 

of the cloud, combined with analytics 

and artificial intelligence, can be brought 

to bear on predictive maintenance or 

analyzing economic alternatives for efficient 

operations. 

If desired, the digital twin can be updated 

with live data from sensors on the 

plant floor via the Industrial Internet of 

Things (IIoT). Modeling technologies 

such as CAD and reality meshes can be 

combined to create augmented and virtual 

reality models to train workers, simulate 

responses, and develop scenarios 

for refits or rebuilds. 

It does all start with a digital twin. It 

sounds simple: a digital twin is a digital 

representation of a physical asset. But it 

quickly gets complicated, since the digital 

version must include the processes 

and systems that create and operate the 

physical version if it is to duplicate it. In 

a plant, the first version of a digital twin 

is often the CAD model of the plant. But 

operators quickly realize that they need 

experts in using the CAD system that 

created the model to keep it current — 

limiting its usefulness and ultimately 

leading to it being so outdated that it is 

abandoned. And they also realize that 

the CAD model isn’t enough: it needs to 

be able to direct an engineer to operational 

data about specific pieces of critical 

equipment, to maintenance manuals, 

and perhaps to operating data to signal 

that failure may be imminent. In a perfect 

world, the model would also suggest 

corrective action. 

To be truly useful, the digital twin 

starts with 1D, 2D, and 3D data, then adds 

maintenance manuals and other critical 

documents, and overlays this with data 

from sensors and physical observation 

to keep it current. Adding this together, 

federating it, creates a one-stop portal 

into the asset, allowing engineers to 

visualize components, check status, perform 

analysis, and generate the types of 

insights that ultimately reduce risk and 

optimize performance. Operators see the 

real benefit of a digital twin when they 

look over the asset’s lifetime, using consistent 

data from design to operations, 

enabling trade-off decisions to be made 

that are based on fact rather than gut 

feel. This is what PlantSight is made for. 

Building the PlantSight Engine 

Bentley Systems and Siemens Digital 

Factory have been creating plant design 

and operation solutions for decades and 

saw the massive potential for improvement 

all along the chain from conceptual 

design to 3D modeling to operations and 

maintenance. Together, Bentley and 

Siemens created a vision for an open, 

cloudbased solution that federates data 

sources and provides specific role-based 

workflows via cloud-based services to 

build and use the PlantSight digital twin 

of the asset throughout its life cycle. 

The partners architected PlantSight 

to be extremely flexible, using Bentley’s 

iTwin technology as the building block 

schema for adding data, manipulating it 

and managing change, and serving it out 

to engineers: 

• Bentley’s iTwins are data repositories 

(called iModels) that could be 

drawings, specs, documents, CAD or 

analytical models, reality meshes, 

IIoT feeds, enterprise data and asset 

management data — in effect, any 

data that can be described 

• iTwin Services are cloud services 

that enable users to create, visualize, 

and analyze iTwins. Bentley is 

rapidly adding more services, but 

one existing iTwin Service is visualizing 

and tracking change, including 

changes in real-world conditions 

from sensors and drones. 

• The 2D/3D visualization iTwin Service 

is central to PlantSight since 

it is the visual front-end that ties 

together data created in multiple, 

often incompatible tools. This iTwin 

Service federates (or combines) 

repositories of different types and 

transforms the data into a standardized, 

open format that is suitable 

for visualization and analysis. 

The resulting information can be 

visualized in 2D or 3D using a web 

browser, manipulated and operated 

on. This iTwin Service uses Project- 

Wise Bridge Service, which supports 

intelligent design systems such as 

SmartPlant 3D, PDMS, and E3D and 

OpenPlant and also RVT, .DWG, 

.DGN and .IFC file types. 



• iModelHub is an iTwin Service that 

keeps a timeline of changes to each 

iTwin. Think of it as a record of who, 

what, and when for each iTwin. As 

an example: a complex project has 

many designers; who changed what 

system, on what date? Designers can 

roll backward and forward in the 

timeline, name significant versions, 

find differences between points on 

the timeline, and create reports. 

PlantSight also follows best practices 

for cloud solutions, as do all of Bentley’s 

and Siemens’s well-established products: 

state-of-the-art cyber-security and access 

control, change management, and 

data traceability. 

PlantSight embodies its creators’ belief 

that an open architecture is critical 

because all plant projects and operations 

today rely on dozens of IT solutions from 

increasingly deep supply chains. Every 

complex plant project relies on many 

IT providers’ solution sets. PlantSight’s 

open architecture means that engineers 

can build a digital twin from drawings 

created using one vendor’s tools, 3D 

models from another, and reality models 

from a third, and links to data from any 

number of enterprise and operations 

systems. 

And, as you can see, the user interface 

is clean, visual, and easy to navigate, for 

both expert and novice users. 

In the long-term, PlantSight futureproofs 

the installation, making it flexible 

to whatever IT emerges over time. 

PlantSight is a digital platform that is 

open to whatever engineering applications, 

repositories and files systems, and 

file formats and schemas designers and 

operators might need, today and in the 

future. 

Bentley and Siemens realize that they 

cannot be the source of all connectors, 

supplying bridges for both commercially 

available and in-house tools. They have 

created an open, connected data environment 

(CDE) with iTwin Services, 

including iModel.js. iModel.js is an 

open-source library that enables anyone 

(who has the knowhow) to connect their 

data to a digital twin. Bentley believes 

that making the library open source 

will foster innovation and lead to many 

more novel uses of digital twins, far more 

than Bentley alone has the bandwidth to 

pursue. 

The early adopters we spoke with 

are already using Bentley’s iModel and 

iTwin Services to connect their data 

to engineers and operators. For them, 

PlantSight creates a visual front-end 

that they intend to use across projects, to 

contextualize many different data silos, 

and to make it all easily accessible. 

Why Bentley + Siemens? 

Bentley Systems and Siemens AG have 

brought together their technology 

stacks, domain expertise, and unique 

capabilities to create PlantSight. Siemens 

brings its experience as a software 

powerhouse to PlantSight, providing 

IoT connectivity and analytics via its 

MindSphere and XHQ platforms, all supported 

by the COMOS 1D/2D process design, 

engineering, and plant automation 

platform. Bentley adds its deep bench of 

3D plant modeling and reality capture 

technologies, the AssetWise asset performance 

and reliability platform, and 

the iTwin Services and iModel.js opensource 

platform to form the PlantSight 

technology stack. 

The companies will be bringing other 

joint products to market — in each case, 

the idea is to create a solution to specific 

industry issues. With PlantSight, Bentley 

and Siemens want to address the 50 

to 100- year lifespan of many operating 

plants, and the reality that few have consistent, 

reliable, and easily-accessible data 

about the asset. Once that data exists in 

the form of a PlantSight digital twin, they 

want operators to start using it to improve 

performance and reliability, for a higher 

return on their investment. How? By using 

PlantSight to digitally simulate physical, 

production, or reliability-based engineering 

changes before implementing anything 

in the operating asset. 

Both companies have repeatedly said 

that PlantSight and their other joint offerings 

will be agnostic. This means that 

data will not be locked into a Bentley or 

Siemens ecosystem and that engineers will 

be able to use the CAD, asset management, 

IIoT, or other tool of their choice. 

Building a PlantSight Digital 

Twin 

Most engineers usually start building their 

PlantSight digital twin with the central 

driver for most refineries and chemical 



process plants: the process and instrumentation 

diagram (or P&ID). Even in 

older assets, the P&ID is usually up-todate 

and so makes a good starting point 

for a digital twin. Next, they layer on 

more data, more data types, and work 

processes. PlantSight doesn’t require a 

3D model — 2D schematics are sufficient 

for many uses. But that’s not really the 

best use of PlantSight. 

PlantSight is a visual front end to the 

digital twin, so a 3D model increases the 

utility of the twin for nonexperts. 3D 

models can help engineers quickly locate 

physical objects in a crowded space, perhaps 

overlaying operating parameters 

or maintenance instructions using augmented 

reality tools. 

If a CAD model is available, Plant- 

Sight can read in all major formats 

using iModel bridges, or create a 3D 

representation from laser scans or photogrammetry 

to update older CAD data 

or create a 3D model from scratch. Using 

a combination of techniques, PlantSight 

can be used to create the twin of a single 

piece of equipment or a complex, fully 

operating plant, that’s brand new or decades 

old. 

What kind of data might you incorporate 

into PlantSight? You might use 

engineering models, reality meshes, and 

schematics from OpenPlant, Siemens 

COMOS, SmartPlant 3D or AVEVA E3D, 

or most other toolsets. You could use asset 

models from AssetWise, XHQ, SAP, 

IBM, or any data historian, computerized 

maintenance management system 

(CMMS) or enterprise asset management 

systems (EAM). As you can see 

from the example below, it could be an 

overview photo of the site, with system 

data laid on top. But the point here isn’t 

a list of what you could use; it’s that the 

iTwin microservices allow you to create a 

container for almost any data type. 

Once PlantSight has acquired the 

1D/2D/3D data, it aggregates this data 

and links it to IIoT, or other data, using 

the item’s tag as the linking mechanism. 

Then, it’s time to visualize it and publish 

it out to engineers. The digital twin is 

only updated when assets are updated 

to reflect the current reality in the field. 

Each engineer can now walk through the 

model, analyze data, link to ERP or other 

external sources, and make informed decisions 

to today’s particular problem. 

PlantSight is a specific iTwin Service 

for the process industries. Bentley’s generic 

iTwin services can be used for any 

type of infrastructure project – roads, 

bridges, railways, buildings, and even cities. 

Early adopters are trying iTwin Services 

on all sorts of other projects. One 

EPC is using it to monitor construction 

on a large project, using drone flights to 

capture as-is construction status. In this 

case, the digital twin is a reality model 

that is combined with other data to track 

construction and visualize change over 

time using iTwin services. This process 

also creates a digital record that will 

enable future workers to know exactly 

where underground utilities are buried, 

for example, or to see the sequence of 

installation of crucial equipment. This 

process feeds into later operations of the 

plant, jump-starting creation of the digital 

twin of the asset. 

Once the bones of the digital twin are 

in place, it’s time to consider how it will 

be managed. Remember that the point 

of the twin is to help operate the physical 

asset, so updating as things change 

is critical. The team building PlantSight 

needs to consider what data to access, 

connect, analyze, and visualize. Plant- 

Sight doesn’t replace asset management 

or maintenance management systems, 

so having access to real-time data is 

likely not necessary. Incorporating near 

real-time data such as analytical results 

of process parameters or inspection reports 

could be useful. 

Using a PlantSight twin 

How engineers and plant operators will 

interact with PlantSight, what types of 

issues they are likely to face, and how 

they should respond are vital in designing 

any specific PlantSight instantiation 

— and each will be different. 

Consider engineers working with an 

aging asset, where the goal is to maintain 

acceptable levels of production with as 

little plant change as possible. In this 

case, a minimal model may be sufficient, 

as long as it allows operators to do shutdown 

planning, and track maintenance 

issues like leaks and corrosion. 

On the other hand, if you’re looking 

at a plant that’s about to undergo a significant 

upgrade, you’ll need to add data 

required for collaboration between the 

engineering and design teams, such as 

specs. You’ll also need to facilitate planning 

and visualization by internal and 

external stakeholders, so you may need 

GIS data or a plot plan. 

Or perhaps the plant is ticking along 



nicely, but in need of constant monitoring 

because of varying prices of inputs. 

You might want to focus your PlantSight 

digital twin on risk-based inspections 

and reliability strategies. Look for the 

data you need to inform your asset performance 

management and predictive 

engineering tools. 

Whatever today’s starting point, the 

PlantSight digital twin will likely be built 

for one purpose —say troubleshooting 

yield issues— but will evolve to address 

many more topics. PlantSight is flexible 

and agile, so you can always add or 

change but do need to pick a point from 

which to start. Consider which workflows 

matter most to you, today: 

• Troubleshoot problems in the plant 

• Reduce engineering design time 

• Involve more experts in and speed 

up design and other review processes 

• Simplify the creation of work requests 

• Improve operator training 

• Speed up root cause analysis 

• Share issues with suppliers and contractors 

• Lower the HSE incident rate 

through better planning and preparation 

Then gather the specific inputs 

needed to address that one issue. Going 

too big, too soon, is overwhelming. Start 

small, prove success, and grow from 

there. 

Getting Started 

Perhaps the most essential aspect of embarking 

on digital twins isn’t technological 

at all but goes to the heart of a company’s 

strategy. How digital do you want to be? 

How do you want to relate to your contractors, 

clients, and employees? Major engineering 

firms are already on a digitalization 

journey, looking for new ways to apply 

technology to make their projects more 

successful, reduce risk, and ensure timelines 

are met. They’re also looking for new 

business opportunities and see PlantSight 

as a way to stay relevant to their clients 

after handover, by creating a PlantSight 

twin for operations and, depending on the 

scenario, maintaining it on behalf of the 

plant’s operator. Owners see digital twins 

as a way of more effectively operating their 

assets, reducing risk, managing across a 

portfolio of old and new, underperforming 

and exceptional, assets to apply the best 

lessons across all. 

So, it’s likely that the first and most 

crucial step to creating a digital twin is to 

create a corporate culture that values data, 

its creation, and its maintenance, above 

pushing paper. Capture, sanitize, and control 

data in case you have a downstream 

use for it. Start building the confidence 

that the data you have is accurate and accessible. 

Only then can you start encouraging 

people to rely on it for daily operations, 

perhaps with PlantSight as the front end. 

Once the operations team is using 

PlantSight, start looking for more uses. 

What types of analytics are you currently 

doing, and what can you do in the future — 

and what data do you need to do that? The 

first PlantSight twin you build will likely 

evolve as you discover use cases — be sure 

to leave that door open. 

Where’s the Proof? 

The PlantSight use that’s gaining the most 

interest is for remote operations. Plant- 

Sight enables multiple users to have access 

to the same data at the same time — 

all they need is a browser, connectivity to 

the cloud, and the correct access credentials. 

Everyone can review the digital twin 

without having to go on-site. They see, 

zoom, mark-up, and otherwise interact 

with the digital twin to jointly make decisions. 

This up-to-date, always-on access is 

critical in a world where a plant is often far 

from decision-makers; it connects the plant 

to the office in a new, actionable way. 

PlantSight can also be used to simulate 

potential alternative solutions to a problem: 

will a new piece of equipment fit into a designated 

footprint? Can we maneuver it into 

position without removing anything else? 

If connected to a CMMS, engineers can 

try out alternative maintenance scenarios. 

Simulating tasks reduces risk, saves money 

and time, and protects human life as well as 

the asset. 

But back to the opening example: handover. 

In both traditional all-at-once and 

piecemeal progressive handover, the operator 

and contractors need to transfer and validate 

hundreds or thousands of documents 

across many disciplines, each created with 

a different toolset. Projects that choose a 

digital, data-centric approach for handover 

and operations have a head start: they avoid 

the mistakes and losses that seem to be the 

norm with paper-based processes. 

If the contractor and operator agree that 

a digital twin makes more sense, they must 

work together to define this “handover 

twin.” They must design the twin so that 

it will fit into the plant’s maintenance and 

operations process and then integrate it into 

the operator’s digital environment. This 

could mean connecting PlantSight to IIoT 

data, to the operator’s ERP system, to the 

CMMS for maintenance procedures and 

work orders, to the plant’s process simulation 

tools for start-up testing and training, 

and to build up a library of what-if scenarios 

for operations. 

Transferring a PlantSight digital twin 

instead of paper, 

• Creates confidence that the operator 

will have the data they need to deal 

with both routine and exceptional issues 

from day one 

• That data will be accessible from one 

easy-to-navigate portal, rather than 

distributed across platforms and data 

formats 

• The data will be in context, meaning 

each piece of equipment will know 

where it is in the plant’s physical layout, 

and what system it connects into. 

Any other pertinent data is a mouseclick 

away 

• As the source data is updated, Plant 

Sight will also update, meaning that 

the data as current as the latest sync. 

• In summary, PlantSight delivers a 

complete, living digital twin that can 

help plant operators run with greater 

confidence. 


ASSET MANAGEMENT 

Improving reliability 

is rewarding but 

challenging. But the 

question is, should 

you aim to transform 

the organization and 

develop a culture that 

values reliability and 

performance, or should 

you make technical 

changes here and there, 

and ‘tinker’ with the 

current state in order 

to eliminate the root 

causes of reliability? 

To Transform or Tinker, 

That is the Question 


JASON TRANTER, 

ARP-L 

Mobius Institute 

tial of the plant over the longest period 

of time. 

Around the world there must be millions 

of dollars of condition-monitoring 

systems and other reliability improvement 

tools sitting in cabinets gathering 

dust. Most plants go through a cycle 

of reliability: a period of enthusiasm 

(among a select few), a period of apparent 

THERE ARE PROS AND cons to each 

approach. In the author’s opinion the 

answer is to transform. 

There is no doubt that purchasing 

condition-monitoring systems, lubrication 

products, alignment tools, and 

developing competencies in a range of 

areas (our CBM, planning and scheduling, 

etc.) will all contribute to the goal 

of improving the reliability and performance 

of the physical assets in the plant. 

The challenge is that for all of the technical 

improvements you may make, it is 

not until you have won the hearts and 

minds of everyone, from senior management 

to the people working on the plant 

floor, that you will achieve the full potenprogress 

as tools and systems are being 

implemented, a period where some benefits 

are enjoyed, followed by the collapse of 

the program. 

The program typically collapses because 

the one dominant person driving 

the program is promoted or leaves for 

greener pastures at another company for a 

consulting firm. Or it fails because senior 

management no longer understand the 

need for the reliability team and their ongoing 

costs when it appears that reliability 

is no longer the problem it used to be. 

Programs fail to achieve the full potential 

for a variety of reasons: 

• Technicians are given new tools (laser 

alignment systems, torque wrenches,


etc.), often without adequate training, 

and almost certainly without 

any buy-in to the process. They 

may or may not use the tools. They 

almost certainly do not achieve precision 

on an ongoing basis. 

• People with condition-monitoring 

systems achieve a level of competence 

but their recommendations 

are ignored. That may happen 

because the maintenance department’s 

ability to plan and schedule 

is limited, or simply because they 

do not believe in the technology or 

believe in the philosophy of “condition-based 

maintenance.” 

• Condition-monitoring recommendations 

may themselves be the issue. 

For fear of being blamed if their 

diagnosis is incorrect, their recommendations 

are vague and often 

come too late. And to top it off, in 

many cases, only a fraction of the capability 

of the condition-monitoring 

system is used. 

• Reliability specialists take root cause 

failure analysis (RCFA) training, and 

may even buy dedicated software, 

but it is not used frequently, and 

even when the root cause is discovered, 

it is not eliminated. 

• Vast sums of money and time are 

spent developing a maintenance 

strategy with a technique such as 

Reliability Centered Maintenance 

(RCM) yet its recommendations are 

ignored or “cherry picked” - deciding 

to follow just some of the recommendations 

without eliminating the 

existing PMs that deliver zero value. 

• New lubrication dispensing containers 

are purchased and desiccant 

breathers are added to gearboxes. 

But the containers are misused and 

the desiccants are not renewed. 

• Ultrasound assisted bearing lubrication 

tools are purchased but they 

still mix the greases, they grease the 

bearings infrequently and pump dirt 

from the nipples/Zerk fitting into 

the bearing. 

• People on the plant floor make suggestions 

for improvement which are 

ignored. 

• Design and purchase decisions are 

made which result in equipment 

with maintainability or reliability 

issues being added to the plant that 

only add to the number of failures 

that occur. 

• Equipment is operated in such a way 

that places additional strain 

on the component thus reducing 

the life of those components. That 

includes the way they are started 

and stopped. 

There are many more examples where 

programs either fail completely or simply 

fail to achieve their full potential. 

There is a common thread through 

all of the points made above. They are all 

people issues. And that is why it is recommended 

to take the transformation path. 

With an asset reliability transformation: 

• Senior management are not only on 

board with the program, but they 

also drive it. They recognize the 

importance of reliability in the same 

way they value safety and quality 

(and hopefully, the environment). 

They appreciate the financial benefits 

because the business case has 

been developed. Plus, they recognize 

the impact reliability has on safety, 

quality, and the environment. 

• With strong senior management 

support, every manager will be 

focused on the reliability improvement 

process. Reliability will not 

be simply viewed as a maintenance 

issue. It will not be viewed as a temporary 

project. It will be understood 

that you cannot simply spend some 

money and the problem will go away. 

Unfortunately, “transformations” 

do not enjoy a high probability of success. 

In fact, it is widely reported that 

only 70% of transformations succeed. 

But there are ways of putting the odds in 

your favour: 

• It must start with senior management 

and a commitment to remain 

focused. This includes consistent 

reinforcement of the key messages. 

• Take it seriously and seek to achieve 

targets within a limited timeframe. 

There must not be a vague commitment 

to improve reliability and thus 

performance. You must understand 

the current state, set achievable but 

impressive goals, and establish a 

timeframe for achieving the goals. 

There needs to be a degree of 

urgency. With the COVID-19 

pandemic, most organizations have 

a very well understood reason for 

seeking to improve the performance 

of the organization. 

• Engage everyone. Everyone must 

understand how they personally 

benefit. Everyone must be encouraged 

to contribute. You must demonstrate 

that you respect their opinion. 

The author would also recommend 

that, where possible, you allow the 

person who makes the suggestion to 

take ownership of the improvement 

process. You congratulate them publicly 

for making the suggestion, and 

you congratulate them for completing 

the project. 

• Manage all of the individual projects 

correctly, with review meetings at 

least every 10 weeks. There must be 

a clear understanding of roles and 

responsibilities, with targets, deadlines, 

and a review process to identify 

when a project is stalled. 

The reliability transformation has 

been made easier thanks to the safety 

transformation. Everyone understands 

how they benefit when working in a safe 

plant. Everyone understands they must 

contribute to the safety of the plant. 

Everyone is constantly reminded about 

the importance of safety. Plant safety 

was not improved simply by buying some 

software, training one or two people in 

the safety department, and having one 

or two people focused on identifying and 

eliminating safety. 

The same must now be true for reliability. 



FOR MAINTENANCE 

TECHNICIANS, THE 

NATURE OF THEIR WORK 

WILL CONTINUE TO 

CHANGE AS TECHNOLOGY 

CONTINUES TO PROGRESS. 

CONNECTIVITY IS KEY 

for Comprehensive 

Maintenance Strategy 

MELISSA TOPP, 

Senior Director of 

Global Marketing, 

ICONICS, 

melissa@iconics.com 

Familiarity with a wide range of equipment, and with 

each machine’s role in an organization’s business, is 

a necessity for maintenance. Today, with the rapid 

increase of digitization of most business functions, 

connectivity between multiple systems has 

become just as important a concern. Maintenance 

managers can benefit from choosing automation 

vendors whose solutions cover a wide array of 

communications protocols and systems integration. 



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

company of Mitsubishi Electric Corporation 

and global developer of automation 

software headquartered in 

Foxborough, Mass., provides solutions to 

improve productivity, reduce integration 

time and operating costs, and optimize 

asset utilization with visualization and 

automation software. Over its 34-year 

history, the company has increased the 

“universal connectivity" of its software, 

including its GENESIS64 HMI/ 

SCADA and Building Automation suite, 

with multiple disparate systems and 

technologies. 

The following are connectivity/communications 

technologies with which 

ICONICS software is compatible and 

with which maintenance managers 

should be familiar. 

OPC UA 

OPC UA is a high performance and very 

secure interface and communications 

protocol designed to share real-time, historical, 

and event data between systems 

and clients. Software integrated with 

the OPC UA standard, such as ICONICS’ 

GENESIS64, can visualize virtually any 

equipment, process, operation, or business 

data. In addition to securely accessing 

registers and data from almost any 

PLC or DCS system, these same OPC UA 

clients can also access equipment maintenance 

records from most maintenance 

management systems. With an eye toward 

security, integration with OPC UA 

technology also provides multiple layers 

of protection, including user-generated 

and authority-generated certificate 

authorization, symmetric encryption, 

signatures on all communications and 

strict user authentication. 

ICONICS provides OPC-to-the- 

Core solutions ranging from a suite of 

OPC servers and clients to a toolkit for 

developing OPC Servers. ICONICS is a 

charter member of the OPC Foundation 

(https://opcfoundation.org) and has assisted 

over the years with creation of OPC 

standards, development of the OPC Foundation 

sample code, and participating and 

hosting OPC Interop testing. Currently 

ICONICS serves on the board of directors 

for the foundation. 

BACnet 

BACnet integration ensures that systems 

can be modernized and integrated with 

most existing building automation systems 

by simply installing the software 

and connecting to the network, without 

requiring any new hardware. What makes 

BACnet unique is that its rules relate specifically 

to the needs of building automation 

and control equipment. They cover 

things like how to ask for the value of a 

temperature, define a fan operating schedule, 

or send a pump status alarm. Common 

BACnet applications include HVAC 

controls, fire detection and alarm, lighting 

control, security, "smart" elevators and 

utility company interfaces, to name a few. 

ICONICS GENESIS64 was the first 64- 

bit automation software to be certified by 

the BACnet Testing Laboratory (BTL) to 

the highest level of BACnet compliance, 

the B-AWS profile. 

Modbus 

Modbus is a data communications protocol 

designed/published in the late 

1970s by Modicon to communicate with 

programmable logic controllers (PLCs). 

Primarily used as a means of connecting 

industrial equipment, Modbus is a popular 



communications standard due to being 

openly published and royalty-free and its 

support of communication to and from 

multiple devices with the same cable or 

Ethernet network connection. ICONICS 

provides multiple Modbus connectivity 

methods including a Modbus OPC 

Server. 

SNMP 

Just as BACnet is more specified to 

building systems, SNMP is typically 

more specified to IT equipment and connected 

systems. SNMP stands for Simple 

Network Management Protocol, and is a 

simple protocol that allows devices to expose 

useful information to other devices. 

This information can be the CPU fan 

speed of a computer or the routing table 

of a router. Almost every network device 

answers to SNMP requests. ICONICS’ 

SNMP compatibility allows network 

managers access to information from 

nearly every network-connected device. 

Databases 

Although database connectivity doesn’t 

typically fall under hardware maintenance, 

some technicians may be curious 

as to the types of databases their control 

systems can access. In the case of 

ICONICS GENESIS64, it can work with 

generic OLEDB, ODBC, Oracle, SAP, and 

SQL connections. 

Web Services 

Again, most maintenance technicians 

might not need to work with web services 

between machines over the internet, 

but some might be interested to know 

that ICONICS GENESIS64 can work 

with both Simple Object Access Protocol 

(SOAP) and Representational State 

Transfer (REST) options. 

New - EtherNet/IP 

EtherNet/IP is an industrial protocol 

defined by ODVA, a "standard development 

organization and membership association 

whose members comprise the 

world's leading industrial automation 

companies". According to ODVA, EtherNet/IP 

"was introduced in 2001 and 

today is the most developed, proven and 

complete industrial Ethernet network 

solution available for manufacturing automation." 

EtherNet/IP is mostly used 

in hardware devices created by Rockwell. 

ICONICS will implement EtherNet/ 

IP connectivity in its upcoming version 

10.96.2 of its automation software suite. 

New - Mitsubishi Electric Factory 

Automation Connector 

As a group company of Mitsubishi Electric 

Corporation, ICONICS is developing 

a Mitsubishi Electric Factory Automation 

(FA) Connector to communicate 

with Mitsubishi Electric PLCs, eliminating 

the need for third-party OPC Servers. 

Created by working closely with the 

Mitsubishi Electric development team in 

Nagoya, Japan, the FA Connector is the 

first product to make ICONICS software 

work directly with Mitsubishi Electric 

devices. Included in the new version 

10.96.2 of ICONICS automation software 

suite, the FA Connector can be configured 

through ICONICS’ Workbench 

utility and provides automatic network 

discovery for Ethernet-enabled Mitsubishi 

Electric PLCs. 

IoT Connectivity 

Over the past few years, ICONICS has 

developed solutions to integrate with 

the Internet of Things (IoT), which has 

expanded its universal connectivity 

even further. Now, in addition to OPC 

UA/OPC Classic, BACnet, Modbus, and 

SNMP compatibility, ICONICS software 

can support AMQP, MQTT, JSON, and 

REST for IoT connectivity. ICONICS’ 

IoTWorX software is compatible with 

AMQP for bidirectional communication 

between IoTWorX and cloud services 

like Microsoft Azure, MQTT for outbound 

communication between IoT- 

WorX and third-party apps, REST communication 

with web services, and JSON 

user-configurable messaging. 

Why Does Universal 

Connectivity Matter? 

For maintenance technicians, the nature 

of their work will continue to change 

as technology continues to progress. 

Today, using solutions such as ICONICS 

GENESIS64, IoTWorX, CFSWorX 

connected field worker software, and the 

MobileHMI remote expert feature, a 

technician can get extraordinary detail 

about an assignment before he or she 

even reaches the targeted equipment. 

Universal connectivity means that connected 

hardware and systems; whether 

involving industrial, building, or IT-specific 

protocols; can provide the information 

needed to get the job done directly 

to a technician’s preferred device (laptop, 

smartphone, tablet, wearable, smart 

speaker, or head mounted computer). 

Connectivity and Beyond: 

FREE ICONICS Whitepapers 

For more information on ICONICS Universal 

Connectivity or on a wide range of 

other applications and technologies, visit 

ICONICS Downloads at https://iconics. 

com/whitepapers. 


YOUR PARTNER IN 

ULTRASOUND 

INSTRUMENTS 

Leak Detection 

Bearing Condition Monitoring 

Bearing Lubrication 

Steam Traps & Valves 

Electrical Inspection 

TRAINING 

CAT & CAT II Ultrasound Training 

Onsite Implementation Training 

Application Specific Training 

CONTINUOUS SUPPORT 

Free support & license-free software 

Online Courses 

Free access to our Learning Center 

(webinars, articles, tutorials) 

UE SYSTEMS 

www.uesystems.com 

info@uesystems.com 

CONTACT US FOR AN 

ONSITE DEMONSTRATION


ADRIAN MESSER, 

CMRP 

adrianm@uesystems.com 

Diagnosing Mechanical and 

Electrical Faults Using 

Ultrasound Spectrum Analysis 

Ultrasound technology has become one of the essential tools in predictive maintenance, 

condition monitoring and reliability, due to its quick learning curve, ease of 

use and flexibility. Leak detection has been one of the most common applications 

for ultrasound, but we now see the technology more and more being used together 

with sound analysis software to diagnose specific mechanical and electrical faults. 

Ultrasound Spectrum Analysis 

Ultrasound technology can be used 

in different applications such as leak 

detection, steam traps & valves inspection, 

bearings condition monitoring and 

electrical inspections. In some cases, 

for example when trying to evaluate the 

condition of mechanical or electrical 

assets, it may be necessary to use an 

instrument with sound recording 

capabilities. This allows the inspector to 

load the recording into a sound analysis 

software to more accurately diagnose 

the type of fault. 

Diagnosing Mechanical Faults 

Mechanical inspections with ultrasound 

include diagnostics such as bearing 

faults, pump cavitation, and valves condition. 

When it comes to bearings, users 

usually monitor their condition by relying 

on what they hear through the headset 

or by trending decibel levels. This is a 

simple and effective method. However, 

in some cases, maintenance professionals 

will need to dig deeper and record the 

sound from the asset for further analysis 

on the software. This practice is espe- 

cially useful in two situations: inspecting 

slow speed bearings and pinpointing 

where the failure is. 

When it comes to inspecting slow 

speed bearings, in many cases there is 

not enough “noise” to trend the condition 

using decibel levels. In this case it’s 

necessary to look at the sound spectrum. 

Sound Spectrum of a 1 RMP bearing. 

Here we can see the sound spectrum 

from a 1 rpm bearing on a furnace 

application. Note all of the anomalies that 

appear in the Time Wave Form from the 

“crackling and popping” sounds that were 

produced by bearing fatigue. This issue 

could only be properly diagnosed by using 

a sound spectrum analysis software. 



We can also use this type of software 

to identify where the fault 

is, if there is an integrated bearing 

fault calculator. By entering in the 

speed (rpm) and the number of balls 

(bearings), an outer race, inner 

race, ball pass, and cage frequency 

will be calculated. 

In this case, the speed was 

1708rpm and the number of balls 

was 8. The fault frequency calculated 

by the spectrum analysis software 

that was of interest was an outer 

race fault at 91Hz. 

Use of a bearing fault calculator. 

Diagnosing Electrical Faults 

Ultrasound can be used to listen for 

electrical conditions such as corona, 

tracking, and arcing. Each anomaly has 

a distinct sound and can easily be identified 

and confirmed through the use of 

ultrasound spectrum analysis. 

Corona, the ionization of air surrounding 

an electrical connection above 

1000 volts, is heard using the ultrasound 

instrument as a steady, uniform, static 

sound. When looking at the recorded 

ultrasound of corona in spectrum analysis 

software, very distinct and evenly 

spaced peaks or harmonics can be seen. 

The harmonics appear every 50Hz or 

60Hz (USA). You can also see frequency 

content, peaks within the peaks, between 

the 50Hz or 60Hz harmonics. These 

are signature features to look for when 

analyzing recorded ultrasounds of 

corona. Being able to detect corona 

with ultrasound is particularly helpful 

because corona typically does not produce 

significant heat to be detected with 

infrared. 

With electrical inspection, the welldefined 

50Hz or 60Hz harmonics will 

diminish as the condition becomes more 

severe. The example below is from a recorded 

sound file of Tracking. Tracking 

typically has a more distinct continuous 

frying and popping sound. Also, notice 

the increased amplitudes indicating a 

more intense sound versus the amplitudes 

of corona. 

The analysis of arcing is even more 

evident of the loss of the uniform 50Hz 

or 60Hz harmonics. With arcing, the 

electrical discharge becomes more 

erratic and has sudden starts and stops 

of the discharge. This can be seen in the 

time series view of a recorded sound file 

of arcing. 

Corona shows distinctive 50Hz harmonics. 

Sound Spectrum of Tracking. 

Sound Spectrum of Arcing. 



SEARCHING FOR 

the new equilibrium 

"Corona forces maintenance and asset 

management organization to be agile" 

THE CORONAVIRUS PANDEMIC has tightened its grip on the 

world. In ‘the new normal’, where certainties from the past no 

longer exist, we need to make changes. Increase or decrease 

production? Focus on uptime or cost control? What does a different 

way of working mean for the organization and for the 

individual employee? A new reality requires clarity, agility and 

a new vision for the future. 

Mainnovation 

The impact of the coronavirus on the economy is enormous. 

This means the maintenance and asset management organization 

must deal with a new reality as well. 

– Keeping distance, a stop on expenditure and a completely 

revised long-term vision. What do you need to focus on? What 

choices do you make, and can they withstand an eventual new 

crisis? Mark Haarman, managing partner of Mainnovation, 

consultancy firm in maintenance and asset management, says. 

Uncertainties 

All over the world, measures are implemented to prevent the 

coronavirus from spreading. This means that the product demand 

has changed. 

– The food companies that supply the catering industry are 

seeing a drastic decline in demand, while food companies that 

supply the home market are working overtime. Furthermore, 

fewer cars are being manufactured and the need for fuel is 

now minimal. In contrast, manufacturers of disinfectants can 

barely cope with the increased demand, Haarman says. 

22 maintworld 3/2020 4/2020

THE CORONAVIRUS PANDEMIC HAS 

TIGHTENED ITS GRIP ON THE WORLD. 

IN ‘THE NEW NORMAL’, WHERE CERTAINTIES 

FROM THE PAST NO LONGER EXIST, 

WE NEED TO MAKE CHANGES. 

– On the other hand, there are also companies, particularly 

in the public sector, that need to invest in their infrastructure 

to stimulate the economy. 

And for all companies, because of COVID, more attention 

must be paid to personal safety. Then there is the factor of 

time. How long will this take? 

– In the field of processes, organization and technology, 

choices must be made, despite all the uncertainties. What is 

the new equilibrium? 

Clarity and agility 

Mainnovation is the founder of VDM XL , a worldwide renowned 

method for Value Driven Maintenance and Asset 

Management. 

– Our VDM XL methodology has proven to be a crisisresistant, 

integrated improvement approach that helps the 

maintenance organization to make well-founded choices to 

create value. The credo is 'Keep it simple'. In the search for 

new equilibrium, VDM XL offers methods and tools to shift the 

focus, says Haarman. 

– The maintenance and asset-management organization 

needs to be agile and flexible in this new reality, in order to 

find the new economic optimum between technical availability, 

maintenance costs, investments and safety. In addition, 

clarity is still a cornerstone that, in these times of turmoil and 

uncertainty, helps companies to go forward, nevertheless. 

VDM XL was, is and will remain the powerful and proven 

methodology for making the right choices. For now, and in the 

long term. 

FOR MORE INFORMATION 

visit our website www.mainnovation.com. 

For questions contact Laura van der Linde, Marketing 

and Communications Coordinator at Mainnovation, 

0031-6-48077995 or laura.van.der.linde@mainnovation.com


SonaVu… Powered by SDT 

SDT Ultrasound Solutions introduces SonaVu, the premiere acoustic 

imaging camera from the world’s favourite ultrasound company. 

SONAVU detects sources of airborne 

ultrasound using its 112 highly sensitive 

ultrasound sensors from up to 50 

metres (164 feet) away. Defects are 

shown on visual images allowing operators 

to easily pinpoint faults that 

produce ultrasound. SonaVu brings 

the power of superhuman hearing to 

focus on its vibrant 5” color display. 

SonaVu is an acoustic imaging 

camera that visualizes airborne ultrasound 

from compressed air and gas 

leaks, as well as dangerous partial discharge 

in electrical assets. 

Compressed Air Leak 

Management with SonaVu 

Compressed air systems are integral to 

modern manufacturing. 

More often than not, compressed 

air systems are poorly maintained and 

full of leaks. 

Leaking compressed air systems 

can impact product quality and detract 

from production efficiency. 

It’s a wonder that compressed air 

systems are neglected when they are a 


COMPRESSED AIR SYSTEMS 

ARE INTEGRAL TO MODERN 

MANUFACTURING. 

pivotal element of production. 

Compressed air leaks account for 

as much as 35-40% of total demand. 

That's 35-40% of your electricity wasted 

for nothing. 

Problems from inefficient compressed 

air system aren’t always immediately 

noticeable to maintenance 

professionals, but over time, compounding 

waste energy adds up. 

With SonaVu… Powered by SDT, 

finding leaks has never been easier. 

Maintenance practitioners can perform 

air leak surveys utilizing SonaVu 

acoustic imaging camera and 

SDT LEAKChecker. 

Connecting it with the high-quality 

noise attenuating headphones, you can 

hear what SonaVu hears. Listen for 

the characteristic hissing of the compressed 

air leaks in the headphones 

and watch the touchscreen color display 

light up with the precise location 

of the leak. 

Inspectors document leaks by taking 

a picture or video with SonaVu, 

providing an easy roadmap for repair 

crews to make things right again. 

Choose to create a still image (camera 

icon) or a video (video icon) for generating 

leak survey reports. SonaVu 

saves the leak image in either photo or 

video format. 

SonaVu makes locating leaks in 

your compressed air system – wherever 

they may occur – effortless, and 

even fun. 

CONTACT 

SDT ULTRASOUND SOLUTIONS - 

Talk To An Ultrasound Expert: 

https://sonavu.com/


Management of Ultrasonic 

Data in a Power Plant 

How modern ultrasonic 

testing technology 

supports maintenance 

personnel in a power plant 

IN THE PURSUIT OF INDUSTRIAL NET- 

WORKING, Maintenance 4.0 is finding its 

way into many power plants. In order to 

detect damage before it occurs, ultrasonic 

testing is a common and widespread 

practice. With ultrasonic testing technology, 

compressed air leaks and partial discharges 

can be detected and steam traps 

and bearings checked at an early stage. 

New software now supports employees 

in planning and evaluating maintenance 

activities. 

The SONAPHONE® testing device is 

used in power stations to maintain different 

plant units. An airborne sound sensor 

or a structure-borne sound sensor is 

used, depending on the application. 

Main fields of application: 

• Testing rolling-contact bearings 

on standard machines 

• Testing steam traps in steam 

systems 

• Locating and evaluating leaks in 

compressed air systems 

• Electrical inspections of air-insulated 

assets 

With the mobile ultrasonic testing 

device SONAPHONE from SONOTEC, 

the maintenance engineer can make an 

initial assessment of the condition of the 

equipment in the power station right 

there and then on-site. With the new 

software platform SONAPHONE Data- 

Suite, users now also receive the right 

tool for the management of all relevant 

measuring points and the evaluation of 

the ultrasonic data. 

Creating test points in the 

power station 

Before testing in the power plant, the 

maintenance planner creates the test 

points relevant for the ultrasonic meas- 

Steam trap testing in a power plant with digital ultrasonic technology. 



Specialized mobile app AssetExpert for upgrading SONAPHONE devices for route-based 

data capturing and onsite inspection and evaluation of asset health. 

Modular software-hub SONAPHONE DataSuite with an overview of potential applications 

during the maintenance process. 

urement in the asset trap on the PC. 

The basic structure can be adapted to 

the KKS or RDS-PP designation system, 

both of which are commonly used in 

power stations. Photos and texts can 

be stored with the test point. This also 

makes the job of the user easier, as they 

know how the sensor should be connected. 

In order for the measuring point 

to be clearly identified, an ID and the 

type of comparison can be specified in 

the system, e.g. via QR code. 

Red alert? 

The integrated traffic light function 

shows immediately when threshold values 

are exceeded. The monitoring of the 

threshold values protects maintenance 

staff from unpleasant surprises. The 

threshold values are defined for the first 

time by comparing them with the initial 

measurement or with identical equipment. 

If the status of a measuring point 

changes from green to yellow or red, appropriate 

maintenance measures must 

be initiated. 

After the maintenance planner has 

created all inspection points, he creates a 

specific work plan in the form of a route. 

The routes can be put together individually, 

e.g. filtered according to critical 

measuring points or following a recurring 

test plan. 

From the PC to the test in the 

power plant 

Once the routes have been created 

on the PC, they are transferred to the 

SONAPHONE digital ultrasonic testing 

device and are available for the technician 

to process in the AssetExpert app. 

Power plant areas can be systematically 

walked through using the routes. If the 

routes are well prepared, reproducible 

measurements can be carried out quickly. 

Has the condition of the steam trap 

or the roller bearing changed? The 

SONAPHONE provides the answers 

directly on site. New measurements can 

be compared with historical values and 

the condition assessed. Depending on the 

application, appropriate airborne and 

structure-borne sound sensors are used 

for the measurement. The ultrasonic signals 

are displayed in the spectrogram and 

level curves. In addition, images, voice 

memos and texts can be added to the 

measuring point. 

Analysis is the be-all 

and end-all 

The route is then evaluated on the PC. 

All measured values and context information 

such as photos, text memos, etc. 

that were recorded in a route are transferred 

to the SONAPHONE DataSuite. 

They are then available at a central point 

for analysis tasks and documentation 

purposes. 

What should be done if the threshold 

value is exceeded on a steam trap and 

the traffic light switches from green to 

red? First of all, the measuring point in 

the SONAPHONE DataSuite should be 

examined more closely. The data is visualized 

in the level curves and in the spectrogram. 

In order to evaluate the trap, it 

is important to know which type of trap 

has been tested, because steam traps 

have different functional principles and 

noise characteristics depending on the 

type, manufacturer and installation location. 

Depending on the interest, areas in 

the spectrogram can be zoomed in and 

important measurement settings, photos, 

text and voice memos can be viewed 

in the DataSuite. If the tester comes to 

the conclusion that the steam trap is 

defective, it should be replaced as soon 

as possible. 

With the introduction of the modular 

software platform SONAPHONE Data- 

Suite, SONOTEC brings a central hub 

for the organization of maintenance 

processes onto the market. The software 

platform provides maintenance 

engineers with the key figures on the 

positive and negative development of 

their equipment. 



TEXT and IMAGES: KONECRANES INC. 

“Safety is our topmost priority. In 

service operations, we need to have 

100 per cent traceability for all our 

tools,” says Joni Janatuinen, Project 

Engineer at Finnair Technical Services. 

Finnair 

RELIES ON AGILON 

in its Maintenance Repair 

Operations (MRO) warehouse 

Finnair Technical Services offer component repairs for the complete Finnair fleet 

– Airbus, Embraer and ATR aircraft types – at its hangars at the Helsinki-Vantaa 

Airport. The target is to carry out all maintenance work at the right time with full 

transparency, as safely and cost efficiently as possible, so that planes can take off 

according to their tight flight schedules. 

– SAFETY IS OUR TOPMOST priority. In 

service operations, we need to have 100 

per cent traceability for all our tools 

to fulfil the requirements of the Part 

145 Approval issued by the European 

Union Aviation Safety Agency (EASA), 

says Joni Janatuinen, Project Engineer 

at Finnair Technical Services. 


All MRO tools have been individually 

laser engraved. It is essential to know 

exactly on which aircraft each tool has 

been used, by whom and when. 

– One of our challenges was that the 

shift change times of our logistics partner 

HUB logistics, who is responsible 

for the MRO warehouse, sometimes 

differed from those of our aircraft repair 

shop. Because of this different shift 

change rhythm, our mechanics were 

not always able to get the tools they 

needed from the MRO warehouse. 

Another and even bigger challenge 

had to do with the working time spent 

at the counter while checking out or


Now the tools occupy only about 30 

square metres of warehouse space, when 

they earlier occupied 100 square metres. 

Earlier, the MRO tools management 

was housed in a closed external application. 

The new system that operates 

24/7/365 has been integrated into the 

aircraft repair shop’s AMOS enterprise 

resource planning system through 

which everybody is able to keep track 

of and monitor the MRO tool management. 

The AMOS enterprise resource 

planning system manages Agilon’s access 

control, too. 

The user interface has been tailored 

to meet the needs of about 500 mechanics. 

It is as simple and fast as possible to 

use with a bar code reader, among other 

features. Agilon is also equipped with a 

carbon dioxide fire suppression system. 

The user interface has been tailored to meet the needs of about 500 mechanics. 

It is simple and fast to use with a bar code reader, among other features. 

returning tools. It accounted for 45 per 

cent of the total resource time of all 

warehouse transactions. A work study 

showed that waiting accounted for 80 

per cent of the working time spent at 

the check-out counter. 

– To be able to start work without 

delay, it is necessary to check the tools 

out immediately without any waiting 

at the warehouse counter. Due to the 

above challenges, we started to chart 

various self-service and automation 

solutions to boost our operations, implement 

a 24/7-without-delay principle 

and minimize waiting time,” Janatuinen 

goes on. 

Solution: a tailored system 

that works 24/7/365 

In November 2018, HUB logistics 

started to use a Konecranes Agilon® 

materials management system in 

Finnair’s largest aircraft hangar. The 

14-metre-long and 6.1-metre-high device 

features two access points, robots 

and walkthroughs. There are about 

1,300 tools in Agilon with an occupancy 

rate of over 80 per cent. 

Automation retains full traceability 

and saves time 

As far as tool monitoring is concerned, 

Agilon fulfils the self-service approach 

that the aircraft repair shop was targeting, 

while retaining 100 per cent traceability 

for tools. The photos taken by 

the system and user identification at 

the access points improve traceability 

even further. Also, Agilon eliminates the 

need to make an inventory of stock and 

manual entries. 

Janatuinen expresses his satisfaction 

with Agilon and the benefits it brings. 

Currently, about 60 per cent of tool 

check-outs are done using the system. 

The rest of the tools, which cannot be 

stored in the Agilon system because of 

their size or other properties, are still 

checked out at the warehouse counter. 

– Agilon saves us a lot of time. Automated 

tool check-out and return, as 

well as other automated transactions, 

have freed up resources at the counter 

for other warehouse tasks. This enables 

our logistics partner to provide us with 

higher-quality and more efficient services, 

for example, collecting and shelving 

items. 

FOR MORE INFORMATION: 

MIKAEL WEGMÜLLER 

Vice President, Head of Agilon Business 

Mobile +358 40 7762 314, email 

mikael.wegmuller@konecranes.com 

konecranes.com/equipment/agilon 


INDUSTRIAL INTERNET 

Industry 4.0 … 

A Practical Maintenance Perspective and 

Significant Impacts on Our Maintenance 

A 

For many years, we 

have seen the evolution 

of technology and the 

acceleration of digital 

hybrid technologies. What 

is industry 4.0 and how 

will it affect our factories 

in the short term? 

GREG FOLTS, 

Marshall Institute 

I MUST FIRST CONFESS that I am not an 

expert in this topic, merely an explorer on 

a journey to understand more about how 

practical, useful, and impending the digital 

transformation will be and how this 

will impact maintenance. 

First, let’s look at some definitions of 

Industry 4.0, the Industrial Internet of 

Things (IIOT), or Digital Transformation, 

specifically in an industrial sense. 

When we search for definitions, we 

find that Industry 4.0 is considered the 

next massive change in technology that 

will change society, or our industries 

fundamentally. The first three industrial 

revolutions (Steam Power, Mass Production, 

and Digital Technology), spanned 

from 1760 to 1960. The fourth industrial 

revolution, currently unfolding with 

many chapters yet to be written, is the 

integration of digital technologies from 

the third industrial revolution, into our 

lives in complex interconnected ways. 

One example might be a heads-up display, 

originally deployed in fighter jets, migrating 

to mid-level family cars. In this example 

we are overlaying digital data onto our 

world, to improve decision making. We 

might also consider how simple services 

such as “Netflix” or “Spotify” predict your 

viewing/ listening preferences. Alexa or 

google home translate your voice commands 

into actions (turn on a light) or 

internet searches. These are examples of 


Acoustic Imaging Camera 

Acoustic Imaging Camera 

Bring Bring compressed air air leaks, leaks, fugitive emissions, 

Bring compressed air leaks, fugitive emissions, 

and and electrical discharge into into focus. focus. 

and electrical discharge into focus. 

www.sonavu.com 

www.sonavu.com


THE LINE BETWEEN THE 

INFORMATION TECHNOLOGY 

DEPARTMENT AND THE 

MAINTENANCE DEPARTMENT 

WILL GET LESS CLEAR. 

the integration of complex digital systems 

into everyday human life. A final way to 

see the integration evolving is augmented 

reality. By using our phones or headsets, 

we can immerse ourselves into a hybrid 

digital/ physical world. Google maps projecting 

digital labels of building names, 

technicians using headsets to see virtual 

tags indicating equipment component 

names, or even Pokémon Go are examples 

of augmented reality integrating digital 

information with the physical/ biological 

world. 

In industry, there are many predictions 

of robots displacing the workforce, “lights 

out” factories, and robots repairing robots. 

I think that while labour-intensive, 

difficult, dirty, and dangerous jobs are 

prime targets for labour displacement, we 

will see more of a shift in skill sets than 

displacement of maintenance labour. 

This skills shift will lead to the first significant 

impact from the transition. The 

adoption and integration of increased 

digital technology will contribute to 

the already widening skills gap in our 

skilled trades workforce. 

While it is difficult today to find and 

maintain skills to troubleshoot and 

maintain our equipment, it is likely that 

this gap will widen, as we need skill sets 

to maintain our assets, including a new 

infrastructure of digital equipment, enabling 

our Industry 4.0 insights. This new 

technology will need maintenance, ranging 

from troubleshooting errant signals or 

readings, to the replacement of batteries. 

Some of the recent technologies use vibration 

or heat to generate electricity, powering 

the sensor and wireless transmitters, 

but even these will take some level of 

maintenance and calibration to ensure 

data integrity. 

Next, I think that the line between 

the Information Technology department 

and the maintenance department 

will get less clear. While there 

has traditionally been a significant line 

From Sense’s website, you can see (below) the pattern of current related to a washing 

machine. As the motor encounters increased load, from the clothes “sloshing around”, 

it creates a unique current signature. The time, pattern, and on/ off cycle create a 

unique “fingerprint” for a typical washing machine. 



https://sense.com 

The Sense cloud database uses this current pattern to identify the device as a washing 

machine, and presents the result back to the user for confirmation. Below is the 

waveform pattern for a typical furnace start up. We can see how the pattern has a 

unique signature that can be used to identify the asset. 

In addition, 

once the 

pattern for 

a normal 

furnace is 

established, 

a pattern for 

a furnace 

beginning to 

experience 

a failure 

mode could 

be identified. 

The example 

below shows a 

failed ignition 

sequence. 

between IT and Maintenance, the roles 

will get closer. We already have networks, 

interconnected equipment, PLCs, CNC, 

and robots that communicate to each 

other. The addition of smart sensors and 

dedicated wireless and wired networks 

to communicate data will bring us closer 

to the IT department, either in the need 

to partner, or overlapping roles. I have 

often seen the parallel between our maintenance 

organizations’ workflow and the 

IT departments’ workflow. The processes, 

not the technology, are very similar. Both 

have routine maintenance of the assets, 

equipment upgrades, and emergency work 

requests. I think we will continue to see 

increased partnership with the IT department. 

The next impact, and this is very positive, 

is that we will see earlier detection 

of failure through the use of pattern 

recognition. In my recent SMRP presentation, 

I used two home-based digital 

automation devices to help the attendees 

understand how pattern recognition can 

be used to identify early failure. Both 

Sense Energy (electrical current) and 

Phyn (water pressure) use wave forms to 

detect equipment in the home and can be 

used to identify failures early in the P to 

F curve. The Sense device installs in your 

house electrical panel and uses two current 

transformers, clamped around your 

main incoming feed wires, to measure 

electrical current at very sensitive levels. 

What has been revealing for me, in my 

journey to understand pattern recognition 

in industry, is understanding how Sense 

captures a wave form, compares that 

waveform to a cloud database of equipment 

(stoves, washers, microwaves, refrigerators…) 

and accurately identifies the 

equipment. It then presents me with an 

educated guess and asks for confirmation 

of the information. 

So, it is easy to see how this could move 

our detection of failures “up the P to F 

curve” using pattern recognition. If technology 

such as Sense and Phyn can detect 

equipment using current, temperature, 

and pressure, then we can use similar 

technology to detect normal and abnormal 

patterns in our industrial applications. We 

could detect faults, such as motor overcurrent, 

excessive fluid leakage, or overtemperature. 

In conclusion, while I believe there are 

other impacts of the transformation happening 

around us, the skills, IT support, 

and earlier failure detection are impacts 

we are likely to see relatively soon. 



WHAT’S THE RIGHT 

Inspection Frequency 

for Equipment? 

If an inspection frequency 

that is too short, 

equipment will be over 

maintained, and resources 

are wasted. If it is too 

long, some failures are 

going to be missed. 

HOW OFTEN DO YOU NEED to inspect 

equipment? The short answer is that 

it will vary depending on component, 

operating context, environment, and 

load. But you must understand how 

to estimate the correct inspection frequency. 

It is not based on criticality, not 

the amount of resources you may have 

at your plant, and it is not based on life of 

the equipment. 

Let’s start with defining what we mean 

by inspections. Inspections include all 

objective and subjective inspections. 

• Objective inspections (we measure 

something) by observation or use 

an instrument. Instruments can 

include vibration analyzer, infrared 

camera, voltmeter, flow meter, or 

ultrasonic. 

• Subjective inspections are those 

look-listen-feel-smell inspections 

In order to set the frequency of your preventive 

maintenance inspections, you need 

to understand what Failure Developing 

Period (FDP) is. 


Failure Developing Period 

(FDP) (or Pf Curve as many 

calls it) 

The FDP is the time period from when it is 

possible to detect a failure until breakdown 

occurs. A failure is when a system or equipment 

is operating correctly within given 

parameters but has signs of problems. 

For example, a centrifugal pump may 

be cavitating, but is still providing the 

required flow for the operation; this is a 

failure, but not a break down. The cavitations 

in our example will eventually 

develop into a breakdown. The breakdown 

occurs when the pump is unable to 

perform its intended function. 

The FDP is the time difference 

between the failure and the break 

down. If the pump started to cavitate at 

6 am and it broke down 6 pm 6 days later, 

the FDP is 156 hours. 

So, what’s the Inspection 

Frequency? 

The theoretical answer to the question 

is very simple. The inspection frequency 

should roughly be: 

For example, if the estimated failure 

developing period is 14 days and we need 

some time to plan and schedule the corrective 

maintenance for that failure to 

avoid a break down. A reasonable inspection 

frequency is 7 days (FDP/2). If the 

inspection frequency is longer than 14 

days, we may miss the failure and we will 

have a breakdown. 

Inspection Tools 

changes the FDP 

FDP changes when we have access to better 

tools. For example, we may be able to 

detect a problem with a pillow block bearing 

by listening to it with a stethoscope. 

This method may give us a warning period 

of a few days (on average depending 

on situation). However, if we use a vibration 

analyzer, we can probably detect the 

same failure at least 8 weeks in advance.


The failure is the same, but the 

FDP has changed! For the most part, 

the only reason we buy inspection tools 

is to extend the FDP. 

In reality, the ability to detect a failure 

during the FDP also depends on the 

person’s ability to do the inspection, 

environment (lighting, temperature, 

indoor vs. outdoor, etc.), and operational 

parameters at the time of inspection, 

equipment design and accessibility, and 

much more. 

Too many variables 

Some variables that trip up many plants 

when calculating the FDPs are: 

• Each component has many failure 

modes and each failure mode can 

have different FDPs. 

• FDP may change depending on the 

inspection tool, technique, the skills 

of the person doing the inspection, 

and more. 

• Each component is running at 

different speeds, different environment 

and different load. 

All these variables inevitably lead many 

plants to do the wrong thing…start a 

massive study to find the answers to all 

these variables for each component. 

Why is a massive study not 

a good approach? I mean all 

you have is time, right? 

This is not a good approach because 

in 999 times out of 1000, you will not 

have the data you need to do the analysis, 

and even if you did, the best bang 

for the buck is usually to get your people 

trained and then out there doing 

inspections rather than performing a 

big analysis. 

What you will end up with when 

you do a complicated analysis without 

accurate data is a guess based on 

many small guesses that will take A 

LOT of work. So let’s not do the complicated 

analysis with poor data and 

instead do an educated guess using 

our experience and cut out 99.9% of 

the work. 

Example 

Let’s look at some typical problems 

with an AC Motor. Note! this example 

does not include all failure modes, 

for example, if you look at bearing 

manufacturer manual, a bearing has 

over 50 failure modes. So instead 

we need to look at most common and 

most likely problems. 

Example: AC Motor, 125 HP, 80% load, 24/7 operation, dusty environment. 

COMMON PROBLEM 

Temp increase center of motor due to overload or 

damaged winding 

GUESSTIMATED 

FDP 

Weeks 

INSPECTION & FREQUENCY 

Temp gun weekly 

Vibration in bearings 4 -12 weeks Vibration analysis every 2 weeks 

Dirt buildup on motor 1 Month Check/clean bi- weekly 

Bolts loose 1 Month Inspect bolts bi – weekly 

Frame & foundation for corrosion 

Temperature increase inboard bearing (can’t get 

good temperature reading on outboard bearing) 

1 Year 

2 Weeks 

Visual detailed inspection semiannually 

Inspect IB bearing with IR gun weekly 

(don’t exceed 170 F 77C) 

Electrical Junction box and cables 1 Month Bi-weekly 

Noise from bearings, winding, overload, etc. 

Immediate damage such as forklift run in, 

something falling on motor 

1 Week 

Instant 

Other tools above will pick up source 

of noise earlier, recommend weekly. 

Can’t catch problems early without 

a FDP. 

Increase in load (A) 2-4 weeks Weekly Current (A) reading 

As mentioned previously there are many more failure modes, I have picked some 

common problems to illustrate my point. 

Notice in the right-hand column there are many different inspection frequencies 

even when we do a simplified analysis. Our estimates are just guesswork and will 

vary depending on who is doing the inspection, the type of tool, and environment, 

so we should not take the numbers too seriously, they are estimates. 

Instead, you should look at some of the shorter inspection intervals and then 

add some of the longer interval inspections to those since you may as well do the 

longer ones when you’re there. They don’t take too long time to do and we are just 

guessing the intervals. 



In our AC Motor example, we could group them as follows in a typical process plant environment: 

Weekly 

Monthly 

INSPECTION & FREQUENCY 

Temp IB Bearing 

Temp center Motor 

Vibration pen at painted spot 

Check Cleanliness of Motor 

Look at condition of junction box and cables 

Visually look for water on motor 

Check fan with stroboscope 

Listen for unusual noise 

Measure Amps 

Vibration analysis with Analyzer (different than pen above) 

6 months Carefully check the base (steel) and foundation (Concrete) 

THE REAL PROBLEM IS THAT WE 

DON’T KNOW WHAT THE FDP 

IS. THERE IS NO STANDARD, NO 

DOCUMENTATION AND MOST 

PLANTS DO NOT HAVE ANY 

HISTORY ON FDP. 

Other Inspections 

If it is a critical motor perhaps you want 

to do a full motor analysis or a test of 

leakage to ground. 

Common Logical Error 

Preventive Maintenance Inspection 

frequencies are based on FDP, not life of 

component, nor the criticality. 

The life of a component has nothing 

to do with inspection frequency. 

For example, a world class plant had an 

average motor life of 18 years, some mo- 

tors last 8 years some 25. This gives you 

no suggestion as to what the inspection 

frequency is, the failure is random, as it 

is for over 90% of components. 

However, the FDP for the most common 

failure modes for these motors are 

most likely in the 1-4 weeks span, so life 

statistics has nothing to do with inspection 

frequency. There is no logical connection. 

A common erroneous argument is 

“we have inspected this component for 3 

years and have not found any problems”. 

Therefore, the inspection frequency is 

extended from one week to four weeks. 

Just because you have not found a problem 

has nothing to do with the FDP, it 

hasn’t changed just because the component 

is running without any indications 

of a failure. 

Once that component fails, it may be 

after 15 years, the FDP may still be two 

weeks and you need to catch it if it is 

financially viable to do so. If you change 

the inspection period to four weeks, it is 

roughly 50 % + risk that you miss it. 



Criticality does not affect the 

FDP, but it might be a factor 

when we assign inspection 

frequency. 

The criticality of the motor is a deciding 

factor when estimating the financial 

aspects of a break down and the precautions 

taken. But, the criticality in itself 

will not help you decide the inspection 

frequency. For example, let’s say you 

have a simple criticality ranking of 

equipment of 1-10 and one equipment 

has the criticality 5, what is the inspection 

frequency? A day, a month, every 

shift? There is no logical connection 

between inspection frequency and criticality. 

But, shouldn’t we inspect critical 

equipment more often? Perhaps, but 

again, you can’t find the time interval by 

knowing the criticality. First, figure out 

the FDP, then if the equipment is critical, 

go conservative, if it’s not critical, be 

liberal. Think of criticality analysis as 

add-on “insurance” to the FDP. 

To sum up this article: 

• Inspection frequencies are based 

on FDP, not criticality or component 

life. 

• The FDP for all failure modes is 

quite unfeasible and impractical to 

predict. However, we can make a 

pretty good guess to what it is. 

• • If you don’t have very good 

historical data as to what the FDP 

is, don’t waste your time making an 

elaborate study, make a reasonable 

guess, it is what you will end up with 

anyway with a study without reliable 

data. 

• If you have the FDP data, ask if it is 

better to spend the effort in training 

people in how to do inspections and 

planning and scheduling of corrective 

actions instead of making an 

outsized study. It is much more cost 

effective to spend the time on making 

the execution of good inspections 

a reality. 

WE INVITE YOU TO CONTACT 

IDCON with comments or questions. 

And check out the rest of our videos on 

our YouTube channel. 

Reveal Your Potential 

Get a Reliability and Maintenance Assessment 

Call us +1 919-847-8764


Maintenance disasters 

caused by unexpected 

events, generally called 

“black swan” events, 

may be prevented 

by the development 

of Industrial AI (IAI), 

given its ability to find 

“invisible” insight in 

data. For centuries, 

swans were considered 

to be white, but in 

1967, a black swan 

(Cygnus Atratus) was 

discovered in Western 

Australia. 

UDAY KUMAR, DIEGO GALAR, 

AND RAMIN KARIM 

Black Swans in Maintenance 

AND INDUSTRIAL AI: 

Predicting the Unpredictable? 

THE TERM “BLACK SWAN” became a metaphor 

for a supposed impossibility that 

was contradicted by new information. 

Black swans are recognized in diverse 

fields, including finance, history, science, 

and also technology. Their common attributes 

across fields are the following: 

a) they have extreme impacts; b) they 

lie outside the realm of regular expectations; 

c) they are unpredictable (with the 

knowledge constraints of each domain) 

and d) they appear stochastically. 

The operation and maintenance community 

also encounters black swans. 

Generally speaking, they deal with the 

impacts of extreme natural degradation 

and man-made accidental or malevolent 

intentional hazards for critical facilities 

by taking a risk-based approach, where 

risk is a function of the likelihood of 

event occurrence and the resulting 

consequences. However, black swans 

are not foreseeable by the usual calculations 

of correlation, regression, standard 

deviation, or reliability estimation, and 

prediction. In addition, expert opinion 

has minimal use, as experience is inevitably 

tainted by bias. The inability to 

estimate the likelihood of a black swan 

precludes the effective application of 

asset management and risk calculation, 

making the development of strategies to 

manage their consequences extremely 

important. In the coming years, Industrial 

AI empowered digitalization may be 

able to cope with the potential or actual 

consequences of these unforeseen, largeimpact, 

and hard-to-predict events. 

Vulnerability of Assets to 

Black Swans 

Complex systems that have artificiallysuppressed 

vulnerability tend to become 

extremely fragile, while at the same time 

exhibiting no visible risks. Although the 

intention of maintenance stakeholders 

is to keep these assets available, reliable 

and non-vulnerable, the result can be the 



opposite. These artificially-constrained 

systems may become prone to unpredictable 

black swans. Indeed, observing 

normality, maintenance engineers tend to 

believe that everything is fine. However, 

environments with “artificial normality” 

eventually experience massive blow-ups, 

catching everyone by surprise, and undoing 

years of failure-free maintenance. The 

longer it takes for the blow-up to occur, 

the greater the resulting harm. If anything 

had indicated the need for protection, 

maintainers would obviously have taken 

preventive or protective actions, stopping 

the black swan or limiting its impact. 

It is unfortunate that we cannot develop 

convincing methods to infer the 

likelihood of a black swan from statisticalinductive 

methods (those based on the 

observation of the past) and combing this 

with statistical deductive methods (based 

on known valid laws and principles) to 

derive the likelihood of a future event 

based on the findings. This is especially 

problematic in maintenance. Arguably, 

Industrial AI has the potential to change 

all this. 

Industry 4.0 and Black Swans 

In the technology industry, every new 

mobile App, computer program, algorithm, 

machine learning construct, etc. is 

advertised as revolutionary and destined 

to change the world. However, black swans 

still exist and are highly impactful, especially 

in a connected world. Industry 4.0 

technologies must learn to handle them. 

The knowledge cycle refers to the 

frameworks or models used by organizations 

to develop and implement strategies, 

including in maintenance. The knowledge 

cycle or the knowledge management cycle 

(or knowledge life cycle) is "a process of 

transforming information into knowledge 

within an organization which explains 

how knowledge is captured, processed, and 

distributed in an organization." Today’s 

organizations must deal with increasingly 

complex problems. The rapid changes 

in the economy and a highly competitive 

market lead to uncertainty, making it important 

to predict possible outcomes or 

events to remain operational. In addition, 

there is a need to develop knowledge life 

cycle strategies to recognize the possibility 

of an unlikely critical situation – this, of 

course, is not easy, but organizations have 

access to immense knowledge. This knowledge 

should be managed systematically 

to identify and eliminate unpredictable 

events or reduce the consequences. 

The Industrial AI learning framework 

aimed to turn black swans in maintenance 

to white swans is shown in Figure 1. As 

the figure shows, it is a mixture of various 

conventional knowledge cycle models. 

This integrated knowledge cycle model 

suggests a way to find black swan events, 

create a strategy to prevent them, and to 

incorporate that strategy. The framework 

covers two major areas of knowledge: 

known and unknown. The conventional 

cycle steps of known knowledge include 

knowledge capture and creation, knowledge 

dissemination, knowledge acquisition 

and application, knowledge base 

updating. Black swan events are an example 

of unknown knowledge. The cycles of 

known and unknown knowledge move at 

the same pace and merge at the end with 

the main goal of recognizing a black swan 

and resisting it. The resulting white swan 

or new known knowledge allows the exploration 

of previously unknown areas. 

Black Swan and 

Anomaly Detection 

In statistical terms, a black swan corresponds 

to the disproportionate contribution 

of a few observations to the overall 

picture. In maintenance, a few observations 

can constitute the normality, the 

information provided by outliers may be 

missed, and the resulting reduced data 

set of failure modes will neglect the total. 

Even a simple underestimation of the required 

sample size can cause a black swan. 

Maintenance engineers use stochastic 

processes and such tools as reliability 

estimation to predict the behaviour of assets, 

but the excessive application of the 

“law of large numbers” is not advisable. 

Simply stated, the law of large numbers 

indicates that the properties of a sample 

will converge to a well-known shape after 

a large number of observations. Although 

bigger datasets of faults lead to greater 

accuracy and less uncertainty when predictive 

maintenance is performed, the 

speed of convergence (or lack of it) is not 

known from the outset. 

Outliers are considered by modelers 

in risk management, but they cannot 

capture off-model risks. Unfortunately, 

in maintenance engineering and asset 

management, the largest losses incurred 

or narrowly avoided by maintainers are 

completely outside traditional risk management 

models. 

Predictability of Black Swans 

Given the subjectivity of human decision-making, 

incorporating the use of 

AI modelling as a tool could positively 

impact outcomes and support maintenance 

expertise. Arguably, using datadriven 

approaches increases objectivity, 

equity, and fairness. Machine learning 

can quickly compile historical data and 

create a risk map to assist with decisions. 

In addition, using a predictive model that 

has a learning component can account 

for variations in different subpopulations 



and potentially capture changes in risk 

over time. 

Artificial intelligence has the potential 

to positively influence maintenance 

effectiveness; however, when used inappropriately, 

there is a risk of AI technology 

underperforming due to lack of knowledge. 

There is a fine line between bias and 

prediction, when using past information 

to make decisions on future behaviours. 

It may be impossible to account for all 

unknown factors that could influence the 

model, particularly when future events 

do not follow the historical data, rendering 

the model invalid; prognosis based on 

sensor data and past knowledge might 

therefore be useless, and predictions will 

be affected by long tails (black swans), as 

shown in the figure below. 

Unanticipated events with a major 

impact could weaken the predictability 

of the model. Dataveillance refers to the 

systematic monitoring of assets using data 

systems to regulate asset behaviour in 

maintenance field. It is another concern 

when using a predictive model. In particular, 

using the model to monitor or surveil 

something is highly contested. Therefore, 

it is imperative to understand and account 

for potential biases when using a predictive 

model. For example, biases in favour 

of positive results could impact the interpretation 

of the data, i.e., looking for data 

to justify decisions instead of justifying 

decisions based on the data. 

AI algorithms are not generally biased, 

but the deterministic functionality of the 

AI model is subjected to the tendencies of 

the data; therefore, the corresponding algorithm 

may unintentionally perpetuate 

biases if the data are biased. Biases in AI 

can surface in various ways. For example, 

the data may be insufficiently diverse, 

prompting the software to guess based on 

what it “knows.” 

There are four basic types of bias associated 

with AI. First, interaction bias 

occurs when the user biases the algorithm 

through interactions. For example, the 

user may provide vibration data but not 

data on other failure modes; when a misalignment 

appears, the algorithm may not 

recognize the failure. Second, latent bias 

occurs when the algorithm incorrectly 

correlates parameters and condition indicators. 

Third, selection bias occurs when 

the data used to train the algorithm overrepresents 

one population, making the 

algorithm operate better for that population 

than for other populations. This is 

typical of dominant failure modes which 

may hide the non-dominant modes, even 

though the latter eventually may have a 

higher impact. Fourth, lack of relevant 

data due to changes in the asset configuration, 

which makes the learning loop 

unactualized. 

Asset managers need to cultivate a 

culture of resilience, i.e., the capacity 

to absorb disturbance while retaining a 

basic function and structure. Traditional 

reliability and maintenance decisions 

are often tainted by personal biases and 

based on a limited time span of observations. 

Engineers do not like uncertainty 

and ambiguity, so they focus on specifics 

instead of generalities and look for explicit 

explanations. Such thinking is shaped by 

their training in linear logic. But nearly all 

black swan events involve complex causal 

relationships. 

DATAVEILLANCE REFERS 

TO THE SYSTEMATIC 

MONITORING OF ASSETS 

USING DATA SYSTEMS TO 

REGULATE ASSET BEHAVIOUR 

IN MAINTENANCE FIELD. 

Industrial AI must compensate for the 

human blindness to black swans. First, 

humans tend to categorize, focusing on 

preselected data that reaffirm beliefs and 

ignore contradictions. Second, humans 

construct stories to explain events and see 

patterns in data when none exist, due to 

illusion of understanding. Third, human 

nature is not programmed to imagine 

black swans; humans tend to ignore the 

silent evidence and focus disproportionately 

on either failures or successes. Finally, 

humans overestimate their knowledge 

and focus too narrowly on their field of 

expertise, ignoring other sources of uncertainty 

and mistaking models for reality. 

As black swans are not predictable, and 

humans are both limited and biased; Industrial 

AI must propose a way to manage 

black swans by determining the emerging 

patterns and disrupting undesirable patterns 

while stabilizing desirable ones. A 

host of studies demonstrate the human 

tendency to assign patterns to random 

data and create descriptive narratives, 

resulting in a focus on the mundane and 

missing the extraordinary. Pitfalls of making 

projections from limited data include 

the failure of many assets. 

Maintenance engineers are not wellequipped 

to deal with anticipation, waiting 

for an important event that will occur 

infrequently, if at all. In a few extremely 

conservative sectors, like nuclear energy, 

maintainers are tasked with taking action 

during incidents that may never 

happen. Numerous cases can be cited in 

the airline and marine industries, where 

nothing out of the ordinary is observed 

for long periods, but a deadly combination 

of fatigue and boredom ultimately leads 

to a catastrophic failure. Engineers also 

have a tendency for tunnel vision, focusing 

on the known sources of uncertainty 

and ignoring the complexity of reality. As 

events that have not taken place cannot be 

accounted for, they do not have adequate 

information for prediction, particularly 

since small variation in a variable can 

have a drastic impact. It is not the random 

uncertainty of probabilistic models, often 

called “known unknowns” or “grey swans” 

but the uncertainty due to lack of knowledge, 

i.e., unknowns or black swans, that 

is the main concern. No probabilistic 

model based on in-box thinking can deal 

with out-of-box events. 

REFERENCES 

Galar, Diego, Pasquale Daponte, and Uday Kumar. Handbook of Industry 4.0 and SMART Systems. CRC Press, 2019. 

Galar, Diego. Artificial intelligence tools: decision support systems in condition monitoring and diagnosis. Crc Press, 2015. 

Galar, Diego, Uday Kumar, and Dammika Seneviratne. Robots, Drones, UAVs and UGVs for Operation and Maintenance. CRC Press, 2020. 



JOSEF HASL, JAKUB KLÍMA, 

Aimtec a.s., Pilsen, Czech Republic 

Empower your Maintenance 

with a System 

How maintenance is misunderstood 

Maintenance. This area is often 

underestimated in a huge number 

of companies. Sure, the main point 

is to produce effectively and to 

reduce unnecessary costs, but is 

maintenance really part of the costs 

that are unnecessary? 

THIS TOPIC CAN BE VIEWED from 

many different points; I would like 

to see this from the perspective of 

a person that is keeping a company 

working, and in the end, those are 

the qualified hands of a maintenance 

person. 

Our specialist is working in an 

automotive company, and his area of 

responsibility is to watch over the mechanical 

part of multiple production 



lines. An effective system has become 

indispensable over the past few 

decades in the maintenance area. 

Since our example company is trying 

to keep up to date with the evolution 

not only in production, but also using 

relevant computer systems, the 

implementation of an ERP system 

became a priority already almost 20 

years ago. 

MAINTENANCE. 

THIS AREA IS OFTEN 

UNDERESTIMATED IN 

A HUGE NUMBER OF 

COMPANIES. 

Life was simpler in the past 

Since then, there have been a huge number 

of process changes, implementations 

in additional areas. And one of them 

was the plant maintenance. In the past, 

life was simple. The technician came to 

work, had his desk, and every day had a 

simple task. Check the production line, 

parts that were supposed to be maintained 

had in fact been maintained, that 

maintenance was documented directly 

in the line to a paper sheet, so that everyone 

could easily see when the maintenance 

was performed, and what were the 

performed activities. You only had to get 

to the line, and check sheet by sheet, if 

and what problems have been solved. 

Of course, Ralph was used to describing 

the solved problems in his own way, 

then Michael and Karl could misunderstand 

the description completely, but the 

system was working. When parts needed 

to be changed, the technician went to his 

storehouse, and he got the parts from 

the shelf. He knew exactly what he was 

searching for, small parts in the drawers, 

bigger parts in the cabinets. Once you 

got used to the system, it was a matter 

of minutes to find the required part. It 

almost never happened that the parts 

were not purchased in time or ran out, 

because there was a simple rule. Every 

time, you took the last part, you had to 

inform the purchasing department and 

they would order a new part. A system 

that no one could miss. So there have 

been almost no downtimes in production 

due to a part being missing, and 

maintenance didn’t take so long to carry 

out. 

One technician was responsible for 

one line, and life was easy. But then it 

was decided that the system is not up to 

date, and maintenance would be supported 

using the existing ERP system. 

It seems that this was one of the decisions 

in the beginning that was not so 

easy, because there was resistance from 

maintenance personnel in the company, 

they didn’t like the idea. Demos of maintenance-oriented 

systems were found, 

that would be perfect for maintenance 

planning and tracking of performed 

work. Once the personal got used to it, 

it would significantly reduce the administrative 

workload. There would be a 

separate system to run the purchasing 

and storing of spare parts, that would 

be run by a second team. The system 

would be almost the same as it was before. 

The working personal would just 



need to learn to work with a different 

system and everything would be solved. 

But the reason this was not chosen was 

simple. There was already an ERP system 

implemented, and why not use the 

maintenance part of that current system 

to cover the relevant processes in plant 

maintenance. The already implemented 

purchasing and warehouse system can 

be used to track the movement of spare 

parts in the company and the system can 

already be operated by the personnel in 

that company. The second point was that 

from time to time, it was required to perform 

maintenance activities for external 

partners that needed to be billed, and the 

ERP system was already also set up to 

allow this functionality. And the last, but 

not least point, is that all other systems 

were designed to support maintenance, 

but they could not precisely track the 

cost part of the planned and performed 

maintenance activities. And this is one 

of the main benefits of a well set up ERP 

system – a process cost evaluation with 

minimal effort. So, the decision was to 

use the existing ERP system. 

Change is life and life is change 

After some struggle to get the relevant 

process information of the performed 

maintenances from the past, the system 

implementation started. The struggle I 

mentioned was caused by the decision 

not only to implement a system, but 

to also have a look at the maintenance 

process effectivity. This was not only 

from the maintenance point of view, but 

also to to try to decrease the duration of 

the performed maintenance activities 

directly in production, and to reduce production 

downtimes. A simple implementation 

of a system would not be such an 

improvement. At the start of the project 

it was necessary to decide on a relevant 

implementation partner that had knowledge 

on how to implement maintenance 

processes in the ERP system, and what 

are the current technical possibilities 

supporting the maintenance personnel. 

A few companies were evaluated, and 

the decision was made on a partner that 

already had experience in this area, not 

just from previous projects, but also from 

the user point of view. In the first project 

phase, it was required to collect a lot data 

about the currently-performed maintenance 

activities. A lot of interesting 

issues have already been found there. But 

the most important thing that the maintenance 

people realized was that life 

really could get easier, if they started to 

use a system that supported their work. 

Everyone would have the same information 

and sharing would become simple, 

because the data is tracked down, and 

can be viewed historically in seconds, no 

need to search in the old records. 

The implementation itself was 

expected to be the main part, but the 

chosen consulting company already had 

a lot of experience from previous implementations. 

The recommendation was 

to support the process also using mobile 

AN EFFECTIVE SYSTEM HAS 

BECOME INDISPENSABLE 

OVER THE PAST FEW 

DECADES IN THE 

MAINTENANCE AREA. 

devices, so the technicians could have 

use of the ERP system directly during the 

performed maintenance activity, no need 

to write down any relevant information 

and have them typed up later on a PC. 

The company was also offering its own 

solution. The implemented solution was 

of a simple design, so the only operator 

is able to see relevant data for the 

performed activity. Using a simple user 

Fig.1. – Maintenance process BEFORE. 

interface also enabled people who are not 

used to operating complicated programs 

on a PC, to start using the program more 

quickly. A second benefit was that the 

system was designed in the same way, as 

already implemented solutions directly 

in production. The production was already 

using an MES system, to track the 

production process. So, this was slightly 

extended to also track down downtimes, 

evaluate them and trigger maintenance 

actions directly. 

In the end, the work of the maintenance 

person has not changed in the 

relevant areas. The person is still performing 

maintenance actions where required. 

What has changed significantly is 

the way the maintenance is planned and 

evaluated. And this has also influenced a 

significant reduction of downtime caused 

by maintenance in the production. Due to 

the collected data, it was found that multiple 

parts of the production line do not 

require daily maintenance, because no 

maintenance activities were performed 

at all. Additionally, because the maintenance 

is performed in the used ERP 

system, the produced quantities are also 

known directly in the maintenance par; 

the main part of maintenance is triggered 

based on the produced quantity and not 

just by looking at the calendar. Using one 

system there was no additional effort at 

all to achieve this, because the production 

quantities have been already collected. 


The Uptimization Experts. 

BROKEN PROCESSES ARE NOT 

“JUST A FLESH WOUND” 

OUR PROCESS GUIDE LIBRARY 

IS THE HOLY GRAIL OF 

MAINTENANCE DOCUMENTATION 

Includes MRO storerooms and maintenance work management 

process maps, procedure details, KPI’s, RACI charts, and process 

guides templates, ready for you to customize. 

Based on Best Practice 

Use our best practice documentation 

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their performance, and your overall 

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You don’t have to start from scratch. 

Our procedures are meant to be 

customized to fit your environment and 

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Fig.2. NEW maintenance process 

Benefits of an integrated ERP 

system process 

What has gone through a significant 

change is the administration of the 

maintenance and the in-time reporting 

of the performed maintenance activity 

directly in the line. The entire maintenance 

department is using one system 

together with the rest of the company. 

Using this, the maintenance of the relevant 

master data is kept in one place, so 

no additional effort is required to maintain 

the master data for other departments. 

The purchase of relevant spare 

parts is triggered based on the planned 

maintenance activities, for more frequently 

needed parts additional evaluations 

are offered, to optimize the spare 

parts stock. Procurement of new production 

resources is first evaluated, based on 

defined KPI indicators in the systems, so 

it is also evaluated if it is more effective 

from the cost and productivity point of 

view, to modernize parts in the production 

line. The focus here also became to 

replace only those parts where the effectivity 

can be significantly improved. This 

became possible since the maintenance 

personnel is tracking each performed 

activity in the system. The tracked activities 

are evaluated and categorized. The 

spent downtime is measured and gives 

an immediate input to the OEE calculation 

of the production line. Such improvements 

were unthinkable without 

using an easy-to-handle system. 

An additional topic is that the process 

improvement also results in relevant 

spare parts being delivered directly to 

the place, where they are needed. By using 

the mobile solution that is directly 

connected with the warehouse, the 

technician just orders the relevant spare 

part; if he is not sure, he can also view the 

specification, the drawing and pictures 

of the part, to order the correct part. 

While the technician is disassembling 

the tool, the spare part is already on the 

way to the correct production site. There 

is no need to search for the correct parts 

because the spare parts are also managed 

in an automated warehouse system, 

which is also connected to the ERP system. 

In case a bigger problem is found 

during the maintenance, the system also 

offers the possibility to store media for 

later evaluation of the problems found. 

By simply taking a picture and storing 

this during the maintenance process, 

it was found that spare parts from one 

supplier often do not fit correctly and 

wear our sooner. Collecting data directly 

during the process of maintenance has 

in the end improved the efficiency of the 

entire process, in most areas using the 

already existing principles. 

As a final point, the relevant cost of 

maintenance can be now also planned, 

because during the past years, the required 

maintenance time for each activity 

based on the evaluations of the collected 

data was collected. So, in the end, 

the maintenance effectivity is also evaluated 

from the cost point of view, and all 

data is transparent in one system. 


EDUCATION AND TRAINING 

Industry as well as 

academy put emphasis 

on other competences 

than the core engineering 

competencies of today. 

This is reflected in 

international engineering 

education standardization 

initiatives such as CDIO 

(www.cdio.org) or The 

Washington accord. 

COMPETENCY 

Requirements for 

Future Engineers 

MIRKA KANS, PhD, Linnaeus University, Department of Mechanical Engineering 

WHEN REFERRING TO CORE ENGINEER- 

ING competences we typically mean 

knowledge in mathematics, natural science 

and generic engineering science, 

as well as experimental and analytical 

abilities. These are tightly connected 

to the students’ own learning, i.e., abilities 

that the individual student possess 

(disciplinary knowledge and reasoning, 

and personal and professional skills 

and attributes), and that reflects the 

traditional way of viewing the engineering 

student; someone that is an 

expert within a narrow subject area and 

that works alone. 

In contrast to this, the interpersonal 

skills as well as the holistic and systemic 

perspectives are, and will become, 

more important in the future. 

Interpersonal skills are for instance 

the ability to work efficiently in teams, as 

well as leading and managing interdisciplinary 

work. Communication skills are 

also important; the engineer should be 

able to speak and write and adapt communication 

method and style depending 

on the context and the audience. This includes 

the ability to listen, to discuss, to 

argue, and to work with negotiation and 

conflict solving. Communication skills 

in the native language is important, but 

also in other languages (mainly English), 

as engineers often work in an international 

context today. 

The holistic understanding covers 

understanding the full life cycle of the 

product and system, i.e., knowledge of 

how to conceive (conceptualize), design, 

implement, and operate products and 

systems. In addition, the understanding 

of enterprise, societal, and environmental 

contexts is also important. The 

engineer will affect, and is affected by 

aspects such as economy, environment, 

or health, and must be able to create sustainable 

products and systems. 

The longest life cycle phase, as well 

as the phase showing up highest relative 

costs, is the operational phase. Therefore, 

from my perspective, this phase 

should be emphasized more. Efficient 



maintenance management would for instance 

prolong the useful lifetime, which 

fits well with the sustainability trend 

we can see. Maintaining, modifying and 

reusing products and materials, are all 

aspects of the circular economy, which is 

a very important topic today. 

Why have the competence 

requirements changed? 

What is the influence of new 

technologies? 

Requirements change due to changes 

in society. We can see a clear change 

towards globalization, sustainability, 

and knowledge as an asset in today’s 

world. Engineers will be affected by, 

and have an effect on this by conceiving, 

designing, implementing and operating 

new solutions. Many of these solutions 

are technological. One can say that the 

technology is an enabler for the changes, 

but also that new demands require new 

types of technology solutions. It works 

both ways! 

Are Nordic countries in 

general ready for the changes? 

What is the current situation 

in Sweden? 

In general, I would say yes. The 

Nordic countries have already 

made many changes in the 

engineering curricula. Other countries, 

especially in Asia and Africa, have not 

1 

Conceiving, 

Technical knowledge 

and reasoning (core 

engineering) 

4designing, 

implementing and operating 

systems in the enterprise 

and societal context 

come so far, but many countries are also 

making a huge change. For some countries 

it is a full paradigm shift, going from one 

way of educating to another, and that is 

often hard, especially if resources are lacking. 

For the Nordic countries the change 

has been ongoing for quite some time and 

is easier to manage. 

We can see that the engineering 

education has changed over the past 

decades in Sweden. Interpersonal skills 

in particular have been emphasized a 

lot – nowadays engineering students 

often have experience of working in 

teams (mainly disciplinary teams), and 

they have trained their communication 

skills during the education as well. The 

holistic context, in which sustainability 

aspects are connected to engineering 

has also developed. Today engineering 

students take courses within industrial 

engineering, environmental aspects, or 

sustainability. Some programs even include 

full courses in engineering ethics. 

Nevertheless, it is hard to change the 

education system, and sometimes one 

can hear that people are afraid that the 

core competences and knowledge are 

weakened when other competences 

should be added. In a worse case scenario 

this could become true – only emphasizing 

teamwork, interdisciplinary work, 

communication, or putting engineering 

in a societal context would be devastating; 

we would get engineers that can 

write and talk but not solve engineering 

problems! Engineers of tomorrow really 

need the core competences in the future 

as well. They are the basis for other 

competences, see figure below. They 

also need the personal and interpersonal 

competences… The trick is to integrate 

the interpersonal and systemic understanding 

in regular courses, I believe 

( just as the CDIO initiative). 

In the industrial maintenance 

field specifically - what are 

the key competencies required 

today and in the coming years? 

The key competencies are of course a 

basic understanding in maintenance 

and reliability engineering, even in the 

future. But something that I think is 

emphasized more and more are competences 

in maintenance planning and 

management, and especially performance 

monitoring. This is especially 

true for companies that outsource 

maintenance: Instead of being excellent 

in executing maintenance actions, 

the capability to procure, plan and fol- 

2 

Personal and 

professional skills 

and attributes 

3 

Interpersonal skills: 

teamwork and 

communication 



low up maintenance activities are becoming 

the core competence. Even for 

other companies, the ability to connect 

maintenance with strategic business 

goals is important. 

Another key competence is handling 

maintenance-related information. This 

spans the ability to identify relevant 

information (including condition information), 

to apply relevant analysis 

methods, and the ability to interpret 

results for decision making. 

How is the education system 

in Sweden ready to face 

the competency needs of 

industrial maintenance in the 

future? 

Not too good at the moment. In fact, 

most engineering students do not even 

take any maintenance related course at 

all. This is unfortunate, as maintenance 

is one of the areas that could be made 

more efficient, and thus enhance productivity, 

efficiency as well as effectiveness. 

Maintenance is also a means to 

impact other sustainability issues such 

as safety and health. 

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The demand for high 

communications skills and 

team working ability - is 

the demand for these skills 

growing also in industrial 

maintenance? How is the 

education system prepared for 

this side? 

Being able to work in team settings and 

in cross functional projects, as well as 

to communicate, is a necessity for any 

kind of engineer, including those within 

maintenance and asset management. 

Maintenance is not an isolated function 

within a company – maintenance 

is carried out in order to reach production 

and business goals, and therefore 

planning, management and follow up, 

and continuous improvement is made 

in cross functional teams rather than in 

isolated isles. 

Take the standard EN 15628 for 

example, where key competencies are 

described. A maintenance engineer has 

to be able 

1. to ensure the implementation of 

maintenance strategies and policies 

2. to plan the maintenance tasks 

within their area of responsibility, 

defining and organizing the necessary 

resources 

3. to organize, manage and develop 

the maintenance resources: personnel, 

materials and equipment 

4. to ensure compliance with regulations 

and procedures related to 

safety, health and environment 

5. to ensure technical and economic 

efficiency and effectiveness of 

maintenance tasks based on current 

state of technology 

6. to participate in the technical aspects 

of contracts and procurement 

process and manage the performance 

of the contractors 

7. to communicate to all necessary 

partners such as staff, contractors, 

customers and suppliers 

8. to use their technical/engineering 

knowledge and the organizational 

tools to improve maintenance tasks 

and plant efficiency in terms of 

availability and reliability 

9. to fulfil organizational and economical 

obligations in the field of 

his undertaken tasks 

Project management skills are connected 

at least with competencies 1, 3, 

5, 8 and 9, and communication skills 

with 1, 2, 7 and 8. 

Engineering students do get basic 

knowledge within project management 

and communication, but specific 

knowledge within the specific issues regarding 

maintenance to a lower extent. 

From this perspective, the education 

system is not at all prepared. 

Maybe this is not a big problem after 

all; an engineering program of 3-5 years 

cannot cover everything. Instead, we 

should maybe promote specializations 

or at least offer courses that are not only 

teaching basic reliability and maintenance 

engineering. If individual universities 

do not have the competence 

THE COMPETENCIES OF THE FUTURE 

and resources to do so, a viable solution 

is to “share” courses, for instance 

through distance-based alternatives. 

Other options are to collaborate 

more between higher education and 

vocal education, for instance by mixed 

courses, where the student after successful 

participation also can achieve 

higher education credit points. There 

are several such initiatives in Sweden 

within many topics (e.g. Expertkompetens, 

founded by the Knowledge 

foundation), but not just focusing on 

maintenance, reliability and asset management 

at the moment. 

IN FINLAND, THERE HAS BEEN DISCUSSION ABOUT STUDENTS' maths skills not being 

as strong as some years ago (Pisa tests). What is the situation in Sweden – Is there a 

risk that weakening math skills will reflect in future technical skills? 

Sweden had a downward trend in the Pisa tests until 2012, but now the trend in moving 

up again. It is a bit too early to say whether the upward trend will continue I guess, 

but the discussion about math and language skills has been ongoing in Sweden as well. 

The typical discussion on university level is that students have poorer math competencies 

today compared to 30-40 years ago. It seems like the skills are more diverse 

today; they differ depending on which school the student has gone to. Also, the Swedish 

students are poorer at formal maths, and therefore the gap between upper secondary 

school and university is quite large. 

If the deficiencies on basic school level are corrected (and that is happening today), 

then the risk would not be too high. If not, then it becomes a pedagogic problem at university 

level, i.e., to bridge the gaps and deficiencies, or the drop off from engineering 

education programs will continue being high as the students will not reach the objectives 

of the curriculum. The drop off is quite high today already, and that would not be 

a good thing. 


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Maintworld Magazine 4/2020

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