Maintworld Magazine 3/2024

3/2024 maintworld.com 

maintenance & asset management 

The Future of 

Sustainability Lies 

in Maintenance 

p 12 

Artificial 

Intelligence as 

a Maintenance 

Monitor and Reduce 

Motor Operating 

Temperature to 

Increase Reliability 

p 20 

Research: Towards 

an Energy-Efficient 

Production Process 

p 40 

Enhancer 

PART 1 OF THE ARTICLE SERIES

EDITORIAL 

AI and Maintenance 

Artificial intelligence is a 

real game changer, most 

probably also in maintenance. 

In this magazine, 

we start a series of articles 

on the use of AI, with the aim to 

bring concreteness to the scene. The 

series consists of three articles. In the 

next episode, we will highlight practical 

solutions and then take a step 

towards the future. 

Unfortunately, at the moment it 

seems that artificial intelligence is 

mainly used to manipulate and distort 

reality. Some time ago, when I 

was wandering around social media, I 

noticed a friend's update from "Italy". 

The picture turned out to be processed, but at first glance the picture did not 

differ from the real thing. Basic consumers have numerous options for easy 

editing of pictures and videos, and all the time, what is on offer is increasing. 

Producing text with the help of artificial intelligence is almost a basic task for 

everyone. While the initial use of AI has not been very encouraging, I remain 

hopeful that we can harness it as a valuable tool. 

We have been evaluating the position of information printed on paper in 

our own operations for several years. The fact is that the share of print media 

has decreased and will continue to decrease. Does print have a place in the 

future? When talking about technology, the basics don't generally get old over 

the years. Facts always find their readers, and a paper book is still the best 

user interface according to many. 

The Maintworld magazine in your hand is still delivered in print. The PDFversion 

of the magazine can also be read fresh online. In the last couple of 

years, the number of clicks online has grown strongly and some of our readers 

have also indicated their wish to receive the magazine only in electronic form. 

In addition to the AI content, this issue also covers many other topical 

issues. For example, the expert article “The Future of Sustainability Lies in 

Maintenance” discusses the important role of maintenance in the transition 

to a sustainable circular economy. In addition, we can read about the results 

of scientific research on the challenges of measuring the benefits of energy 

efficiency. 

I'm still happy to receive your feedback and story ideas. 

Jaakko Tennilä 

Editor-in-Chief, Maintworld magazine 

I remain 

hopeful that we 

can harness AI as 

a valuable tool. 

20 

Understanding 

and 

monitoring your motor’s 

recommended operating 

temperature can drastically 

lengthen its lifespan. 

4 maintworld 3/2024

IN THIS ISSUE 3/2024 

12 

Sustainability, 

closed-loop 

circularity and 

maintenance 

working hand in 

hand. 

In a world where 

sustainability is no longer a 

choice but a necessity, 

the circular economy is 

radically transforming how 

industries operate. 

40 

Verifying 

energy savings 

remains a challenge for the 

growth of the energy services 

sector. 

4 Editorial 

6 News 

12 

20 

Eternal Machines: The Future of 

Sustainability Lies in Maintenance 

Monitor and Reduce Motor 

Operating Temperature to Increase 

Reliability 

PART 1: Artificial Intelligence as 

26 

a Maintenance Enhancer 

The Role of Ultrasound in 

32 

Maintenance 4.0 – Enabling 

Predictive Maintenance Cases & 

Examples 

36 

How to Monitor Critical 

Machines? New software for fault 

monitoring 

40 

44 

48 

RESEARCH: Towards an Energy- 

Efficient Production Process – 

Measuring and Evaluating 

Cost Savings and Environmental 

Benefits 

Should we treat our technicians like 

surgeons? 

Long gone are the days of timebased 

lubrication 

Issued by Finnish Maintenance Society, Promaint, Messuaukio 1, 00520 Helsinki, Finland, tel. +358 40 1959123, Editor-in-chief Jaakko Tennilä, 

jaakko.tennila@kunnossapito.fi. Publisher Avone Oy, Executive producer Vaula Aunola, editor@maintworld.com, vaula.aunola@avone.fi, avone.fi 

Advertisements Kai Portman, Sales Director, tel. +358 358 44 763 2573, kai@maintworld.com. Layout Avone Printed by Savion Kirjapaino Oy 

Frequency 4 issues per year, ISSN L 1798-7024, ISSN 1798-7024 (print), ISSN 1799-8670 (online). 

3/2024 maintworld 5

In Short 

The industrial automation device 

management software market size is 

growing at a CAGR of 6.72% to USD 

1.57 billion between 2023 and 2028. 

Source: Technavio. 

New Guidance for 

Measuring The Carbon 

Footprint of Offshore 

Wind Farms 

A FIRST-OF-A-KIND METHODOLOGY to standardise how the carbon 

footprint of an offshore wind farm is measured has been published 

by the Offshore Wind Sustainability Joint Industry Programme (SUS 

JIP). The collaborative initiative aims to help the global offshore wind 

industry scale up as sustainably as possible to meet global Net Zero 

targets by 2050. 

The methodology will enable more accurate and standardised 

assessments of carbon footprints across the full lifecycle of offshore 

wind developments. It is applicable to fixed-bottom and floating offshore 

wind turbines, whether prospective, operational, or at any other 

stage of their lifecycle. It also has the potential to support carbon 

emission transparency on offshore wind projects, facilitating action 

on non-price criteria following recent changes to many international 

offshore wind auction requirements. 

The Carbon Trust collaborated with twelve SUS JIP industry partners 

— representing around a quarter of installed wind farm capacity 

globally — to devise the methodology. These include bp, EnBW, 

Equinor, Fred Olsen Seawind, Parkwind, Orsted, RWE, ScottishPower 

Renewables, Shell, SSE Renewables, Total Energies, and Vattenfall. The 

methodology also received input from several international research 

bodies, public bodies, industry, and wind trade associations as part of 

a consultation process. 

The methodology is available to any offshore wind industry stakeholder, 

such as supply chain companies, consultants and developers 

involved in measuring the carbon impact of an offshore wind project, 

including the electricity generated and delivered to the grid. It provides 

a breakdown of how the carbon footprint can be calculated for 

all activities related to the material extraction, manufacturing, construction, 

installation, operation, decommissioning and end-of-life of 

the core infrastructure. 

The SUS JIP partners will use this methodology to support the 

standardisation of carbon emissions reporting. All twelve developers 

strongly encourage their networks and supply chains to refer to 

this publicly available methodology and are keen to have continuous 

partner engagement on this topic. 

“Having transparent data on the carbon footprint of offshore wind 

farms is a game-changer. It allows developers to better understand 

and reduce the carbon emissions of their projects, while giving investors 

the ability to make informed, side-by-side comparisons between 

developments," says Mary Harvey, Programme Manager for SUS JIP 

from the Carbon Trust. 

Eu's Goal of 

Increasing Self- 

Sufficiency in 

Mining Operations 

ALL LITHIUM CHEMICALS used in electric car 

batteries and mobile phones are mined and 

processed outside Europe. At the beginning of 

2023, the European Commission took a significant 

step towards self-sufficiency by publishing 

the Critical Raw Materials Act (CRMA). The 

regulation defines clear goals for developing 

the value chain of strategic raw materials and 

focuses, among other things, on lithium processing. 

At the beginning of this year, the EU-funded 

international LITHOS project, for which finnish 

VTT has the leading responsibility, started 

to develop more efficient lithium recovery. 

The project involves close development cooperation 

with projects in France and Portugal, 

for example. The project aims to enable existing 

processes to utilise larger shares of lithium 

resources and brings innovations to processing 

lithium mineral impurities. 

The goal is to develop a concept that allows 

for 90 percent lower water consumption and 

a significant reduction in carbon dioxide emissions 

compared to current operations in Asia, 

thus making the processes that apply LITHOS 

significantly more environmentally friendly 

than current methods. 

The long-term goal is to support Europe’s 

ambitions to improve its self-sufficiency of 

lithium supply by increasing the recovery rate 

of mining and refining operations. 

PHOTO: VTT 

PHOTO: FREEPIK 


142.69 bn. 

The European industrial maintenance 

market is expected to reach 

USD 142.69 billion by 2033. 

Source: Spherical Insights 

European Industrial Maintenance 

Market is Growing 

ACCORDING TO A MARKET REPORT BY 

SPHERICAL INSIGHTS, several European 

companies have adopted integrated service 

concepts to raise the level of equipment 

and services in critical processes. This strategy 

is also helping the industry to reduce 

maintenance and repair costs. This trend 

is expected to boost the industrial maintenance 

market in Europe over the forecast 

period. 

MARKET SEGMENTATION 

The oil segment will dominate the market 

with the largest revenue share over the 

forecast period. On the basis of product 

type, the European industrial maintenance 

market is segmented into greases, lubricants 

and oils. Of these, the oils segment 

dominates the market with the highest revenue 

share over the forecast period. The oil 

and gas industry is increasingly adopting 

cutting-edge technologies such as Internet 

of Things, artificial intelligence and automation. 

Industry sustainment will support the 

integration and optimization of these technologies 

to improve decision-making and 

operational efficiency. To maintain asset 

integrity and avoid unplanned downtime, 

the industry relies primarily on the maintenance 

of advanced machinery and infrastructure. 

Industrial maintenance providers 

offer specialised maintenance solutions to 

ensure the reliability of critical assets. 

The machinery and equipment lubrication 

segment is expected to grow at a significant 

CAGR over the forecast period. 

On the basis of application, the European 

industrial maintenance market is segmented 

into machinery and equipment lubrication, 

bearings and gears, hydraulic systems, and 

engine oils. Of these, the machinery and 

equipment lubrication segment will grow 

significantly over the forecast period. Realtime 

monitoring and advanced monitoring 

algorithms and applications will help 

increase safety and reliability in industrial 

processes. Proper implementation and continuous 

safety-critical systems are enabled 

in part by industrial maintenance. In addition, 

the food and beverage industry has 

large machinery and equipment requiring 

maintenance, which will accelerate market 

growth over the forecast period. 

The aerospace industry dominates the 

market with the largest revenue share over 

the forecast period. On the basis of end-use, 

the European industrial maintenance market 

has been segmented into industrial, automotive, 

aerospace, energy and utilities, marine, 

mining and others. Of these, the aerospace 

segment dominates the market with the largest 

revenue share over the forecast period. 

The aerospace segment includes companies 

125.31 Bn xx.xx xx.xx 

that perform overhauls, refurbishments, 

component replacements, modifications or 

conversions of commercial aircraft. Companies 

in this sector manufacture gliders, helicopters, 

drones, ultralight aircraft, passenger 

aircraft and private aircraft, among others. 

For corporate, commercial and military aircraft, 

the main focus is on the maintenance 

support provided by the industrial aftersales 

service over the forecast period. 

Europe Industrial Maintenance Market 

xx.xx 

xx.xx 

xx.xx 

xx.xx 

xx.xx 

xx.xx 

142.69 Bn 

xx.xx 

2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 

Source: Spherical Insights: Europe Industrial Maintenance Market Insights Forecasts to 2033 


In Short 

Industrial fuel control components 

and systems market forecast to reach 

US$33.94 billion by 2034. 

Source: MR report 

The Market for Industrial Fuel Control 

Components and Systems is Developing 

According to a recent report published 

by Fact.MR, the global market for 

industrial combustion control components 

and systems is expected to reach 

US$19.31 billion by 2024. The market 

is further projected to grow at a CAGR 

of 5.8% from 2024 to 2034. 

The global market for industrial combustion 

control components and systems 

is forecast to reach USD 33.94 

billion by the end of 2034. The global 

demand for industrial combustion control 

systems and components is high 

due to their wide range of applications 

in a variety of industries. These systems 

are essential for maximizing fuel economy, 

reducing emissions and improving 

overall process control in sectors such 

as metallurgy, oil & gas, power generation 

and chemicals. 

North America is expected to reach 

USD 11.57 billion by the end of 2034. 

US market revenues are expected to 

reach USD 9.28 billion by the end of 

2034. Spain's share of the Western 

European market is forecast to reach 

13.8% by 2034. 

The integration of machine 

learning and artificial intelligence 

into combustion 

Order newsletter 

control systems is a 

major step forward. 

These intelligent 

systems adapt to 

changing fuel quality 

and load conditions 

by continuously 

optimising 

combustion 

parameters in real 

time. 

Another innovation 

is the creation 

of advanced sensors 

and analysis tools. Big 

data analytics combined with 

high-precision sensors give users 

a comprehensive understanding of combustion 

processes. Predictive maintenance, 

early detection of faults and predictive 

optimisation techniques are thus 

possible. 

With the introduction 

of IoT-enabled combustion 

control systems, 

operational 

flexibility has 

increased and 

downtime 

has been 

reduced 

thanks to 

remote 

monitoring 

and control. 

These 

developments 

will open up 

advanced combustion 

control to 

more and more people 

worldwide through 

improved user interfaces and 

seamless integration with existing 

industrial automation systems. 

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which will bring the latest news to your 

inbox twice a month. You can 

unsubscribe at any time. 



12.6 bn. 

The global drilling data management systems' market 

size is estimated to grow by USD 12.6 billion from 

2023-2027, according to Technavio. 

Source: Technavio 

AI Redefining Drilling Data 

Management Systems 


The global drilling data 

management systems' 

market size is estimated to 

grow by USD 12.6 billion 

from 2023-2027, according 

to Technavio. 

The market is estimated to grow 

at a CAGR of 9.19% during the forecast 

period. Drilling data management 

system to improve productivity 

and transparency is driving market 

growth, with a trend towards the 

advent of big data analytics. However, 

fluctuations in crude oil prices 

pose a challenge. 

The oil and gas industry faces 

challenges such as the depletion of 

oil wells, the need for accurate information 

on new drilling locations, and 

the requirement to comply with regulations 

regarding pollution and waste 

reduction. To address these issues, 

the industry employs various technologies 

for efficient drilling operations. 

Consequently, vast amounts 

of data are generated which, when 

effectively utilized, can revolutionize 

the sector. Big data analytics, 

initially popularized by tech giants 

like Yahoo, Google, and Facebook, 

is now being adopted in the oil and 

gas, chemical and petrochemical, and 

power industries for process analysis. 

The fusion of SCADA and big 

data analytics is a burgeoning trend, 

enabling prompt decision-making, 

minimizing errors, and identifying 

problem origins. Big data analytics 

2017 : USD 15,001.42 

Market Size Outlook (USD Million) 

enables oil and gas companies to 

analyze historical data and trends to 

make future predictions. Companies 

gather data from various processes, 

including drill tip pressure and oil 

and energy consumption, to predict 

optimal drilling locations and minimize 

expenses. Big data analytics 

provides actionable insights, enhancing 

process reliability and improving 

company efficiency and production. 

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 

Source: Technavio 


In Short 

According to Precedence Research, the global 

smart factory market is projected to grow from 

USD 155.61 billion this year to 

USD 268.46 billion by 2030. 

Source: Precedence Research 

LG Accelerates Smart Factory 

Solutions Business 

THE PRODUCTION ENGINEERING 

RESEARCH INSTITUTE (PRI), which 

has been enhancing production and 

manufacturing competitiveness for LG 

Group affiliates, is now extending its 

expertise to external clients. Services 

offered include production consulting, 

development of equipment and operation 

systems and training for technology 

personnel. 

Major clients include secondary 

battery manufacturers, automotive 

parts manufacturers and logistics 

companies. LG plans to aggressively 

expand into industries with rapidly 

growing factory demand, such as semiconductors, 

pharmaceuticals, biotechnology 

and food and beverage. The 

goal is to develop the smart factory 

solutions business into a multi-trillion 

KRW enterprise by 2030, excluding 

revenue generated within the LG 

Group. 

LG has accumulated vast amounts 

of manufacturing data and know-how 

through 66 years of factory design, 

construction and operation. In the 

past decade alone, the company has 

amassed 770 terabytes of manufacturing 

and production data.The company's 

competitive edge also lies in its 

various core production technologies 

essential for smart factory configuration, 

with PRI filing over 1,000 patents 

related to smart factory solutions. 

Smart factory solutions focus on 

minimizing even the briefest delays 

or minute errors between processes. 

For example, at LG's refrigerator production 

line in Changwon, a refrigerator 

is produced every 13 seconds. A 

10-minute delay in the production line 

would result in a production shortfall 

of 50 refrigerators. Assuming the price 

of one refrigerator is KRW 2 million, a 

10-minute delay translates to a loss of 

KRW 100 million. 

The production system design and 

operation solutions leverage real-time 

simulations using Digital Twin technology. 

Before the factory is built, a 

virtual replica identical to the real factory 

is created, allowing clients to preview 

the production and logistics flow. 

LG Electronics is advancing its smart factory solutions business by integrating artificial 

intelligence (AI) and digital transformation. Photo: LG 

During the operational phase, analyzing 

real-time data helps detect bottlenecks, 

defects and malfunctions in 

the production line in advance, thereby 

contributing to productivity improvement. 

Sensors installed throughout 

the factory detect abnormal signals 

such as vibrations and noise caused 

by equipment aging or lack of lubrication. 

Big data is then used to determine 

the causes and recommend corrective 

actions. 

Generative AI based on large language 

models allows for easy use 

through voice commands. For example, 

saying "abnormal vibration in equipment 

A at 2 p.m." records the abnormal 

signal on the server. A command 

like "show recent abnormal vibrations 

and corrective actions" provides a list 

of defect types and previous corrective 

actions in order of likelihood. 

Additionally, LG has developed a 

real-time detection system powered by 

Vision AI. This system learns the factory's 

normal operating conditions and 

detects anomalies such as temperature 

fluctuations and defects. It also enhances 

factory safety management by identifying 

workers who are not properly 

wearing safety helmets or work vests. 

LG's intelligent autonomous factories 

in Changwon, South Korea, and 

Tennessee, USA, have been recognized 

as Lighthouse Factories by the 

World Economic Forum. Following the 

implementation of smart factory solutions, 

productivity at the Changwon 

plant increased by 17 percent, energy 

efficiency improved by 30 percent, 

and quality costs due to defects were 

reduced by 70 percent. 


Global Key Data 

from the Hydrogen 

Sector 

GIS 

PdM 

Mobile 

AIP 

PPM 

THE HYDROGEN COUNCIL'S latest report highlights 

that the global clean hydrogen project 

pipeline is growing and maturing, with a sevenfold 

increase in committed capital for projects 

reaching final investment decisions over the past 

four years. 

Hydrogen Insights is the Hydrogen Council's 

regularly published perspective on the development 

of hydrogen production. It summarises the 

current state of the global hydrogen sector and 

the actual deployment of hydrogen. The report 

was produced by the Hydrogen Council in collaboration 

with McKinsey & Company and is based 

on a combination of public data and the Hydrogen 

Council members' own data. The Hydrogen 

Council's latest analysis covers more than 1 500 

projects worldwide. 

The Hydrogen Insights 2024 report, published 

in September, finds that the global pipeline of 

projects has grown sevenfold since 2020, from 

228 to 1,572 projects by May 2024. 

The latest data from October 2023 to May 

2024 show a clear shift from project planning to 

implementation. Total reported investment up to 

2030 will have increased by around 20% - from 

$570 billion to $680 billion. The most significant 

increase to date has occurred in the more 

advanced stages of project development: post- 

FID investment has increased by a substantial 

90%, followed by a 30% increase in FEED (frontend 

engineering design) projects. 

The Hydrogen Council is a global initiative 

led by CEOs with a unified vision and long-term 

goals to accelerate the transition from hydrogen 

to clean energy. It brings together a diverse 

group of 140 companies from 20 countries in 

the Americas, Europe, Africa, the Middle East and 

Asia Pacific. 

Next 

Generation 

EAM 

BIM 

AI 

APM 

Many companies use their Enterprise Asset Management 

(EAM) system mainly as an electronic card index or a 

digital work order system, unaware of the possibilities it 

has for Asset Management. EAM Systems like Maximo, 

IFS Ultimo, HxGN EAM and SAP EAM have evolved 

tremendously. They now offer functionalities for Asset 

Investment Planning, Project Portfolio Management, 

Asset Performance Management, Business Intelligence 

and Predictive Maintenance. Major steps have also been 

taken in the field of Mobile, GIS and BIM integration. 

BI 

Are you ready for Next Generation EAM? 

Our VDM XL experts can assist you with further 

professionalisation and automation of your Maintenance 

& Asset Management organisation. 

www.mainnovation.com 


SUSTAINABILITY 

Eternal Machines: 

The Future of 

Sustainability 

Lies in Maintenance 

Text: Prof. DIEGO GALAR / Prof. RAMIN KARIM / Prof. UDAY KUMAR 

Images: ShutterStock, Alamy, Freepik 

Sustainability, closed-loop circularity and maintenance working hand in 

hand. The result? Keeping our devices and machinery alive and well for 

extended periods of time. It is time to change our way of thinking. 

Circular economy and maintenance is the unsung hero of sustainability. 



As industries around the 

world grapple with 

environmental challenges 

and diminishing resources, 

the traditional linear 

economy is disappearing, 

yielding to the circular 

economy. 

As industries around the world grapple with environmental 

challenges and diminishing resources, 

the traditional linear economy—the "take-makedispose" 

model—is disappearing. It is yielding to 

the circular economy (CE), a regenerative system designed 

to keep products, components, and materials in continuous 

circulation, thereby dramatically reducing waste, conserving 

vital resources, and minimizing environmental harm. 

At the heart of this paradigm shift is a powerful but often 

overlooked force: maintenance. Once an operational afterthought, 

maintenance is emerging as an enabler of the 

circular economy of the future. 

CLOSED LOOP CIRCULARITY FOR A LONGER 

PRODUCT LIFESPAN 

It is no longer just about fixing machines; it is about 

breathing new life into assets and extending their useful 

lifecycle. This transformation allows industries to keep 

parts and products in use longer, cutting down on the need 

for raw materials, and regenerating value through proactive 

repair, refurbishment, and remanufacturing. In effect, 

maintenance closes the loop—ensuring resources keep 

circulating within the economy rather than being prematurely 

discarded. As industries adopt circular strategies, the 

focus shifts from replacement and disposal to preservation 

and renewal. Maintenance enables this shift by ensuring 

products and machinery last longer and perform at peak 

efficiency throughout their extended lifecycles. Instead 

of buying new parts or equipment, industries can rely on 

maintenance strategies like refurbishment or remanufacturing 

to breathe new life into existing components. This 

not only reduces waste but also minimizes resource extraction, 

in harmony with the circular economy’s ethos of maximizing 

value at every turn. 



Circular Economy in Motion Maintenance keeps materials flowing, reducing waste and maximizing resource use. 

This article explores how modern 

maintenance strategies—especially predictive 

maintenance (PdM) powered by 

artificial intelligence (AI)—are reshaping 

the industrial landscape. We use real-world 

examples, data-driven insights, 

and cutting-edge strategies to show how 

maintenance is driving the transition 

toward a sustainable, circular future. 

PDM: THE AI-DRIVEN KEY TO 

CIRCULAR ECONOMY SUCCESS 

Imagine a world where machines 

monitor their own health—anticipating 

repairs, predicting malfunctions, 

and requesting maintenance before 

breakdowns occur. No more sudden 

shutdowns, no more wasteful replacements. 

PdM is turning this vision into 

The marriage of AI 

and IoT in PDM 

revolutionizes operational 

efficiency. 

reality. By leveraging real-time data, AI, 

and the Internet of Things (IoT), PdM is 

transforming the way industries manage 

their assets, making it a cornerstone 

of the circular economy. In the linear 

economy, equipment failures lead to 

only one outcome: replacement. But in 

a circular economy, PdM flips the script. 

By foreseeing failures before they happen, 

PdM drastically reduces waste, 

prolonging the life of valuable assets. 

It ensures machinery runs smoothly, 

minimizing environmental impact and 

maximizing efficiency. 

AI OFFERS IMMEDIATE REAL- 

TIME FAULT DETECTION 

PdM doesn’t merely address visible wear 

and tear; it detects subtle, often invisible 

signs of deterioration. Using a network 

of IoT sensors, it continuously tracks the 

health of equipment, while AI analyses 

real-time data to predict failures with 

remarkable precision. These systems 

continuously assess the condition of 

critical components. Take, for instance, 

the maintenance of airplane turbine 

blades. Instead of following a rigid, timebased 

maintenance schedule that risks 

premature or delayed part replacements, 

PdM detects the precise moment when 

a blade begins to wear out. A targeted, 

timely intervention extends the life of 

the component, reducing waste and 

resource consumption—directly supporting 

the circular economy’s goal of 

maximizing asset longevity. 

The marriage of AI and IoT in PdM 

revolutionizes operational efficiency. 

These systems can process vast quantities 

of data, spotting patterns and 

trends that would otherwise go unnoticed. 

But PdM isn’t just about preventing 

breakdowns—it is a key enabler of 

circularity. By identifying components 

that can be repaired, refurbished, or 

upgraded before they fail, PdM ensures 

parts are used to their fullest potential. 

Machines and parts are kept in circulation, 

reducing the need for new raw 

materials. 

As more industries adopt forwardlooking 

maintenance strategies, we 

are witnessing a seismic shift in asset 

management. PdM’s cutting-edge technology 

ensures assets stay in use longer, 

supporting sustainable practices while 

minimizing waste. In this new para- 


Learn the real 'magic' of equipment uptime at: 

www. 

.com


digm, maintenance is no longer seen as 

a burdensome cost—it is a critical, AIdriven 

tool propelling industries toward 

a future of resource optimization and 

circularity. 

In a world where 

sustainability is no longer a 

choice but a necessity, the 

circular economy is 

radically transforming how 

industries operate. 

CIRCULAR ECONOMY BUSINESS 

MODELS: RETHINKING 

MAINTENANCE AS THE 

LIFEBLOOD OF SUSTAINABILITY 

In a world where sustainability is no 

longer a choice but a necessity, the 

circular economy is radically transforming 

how industries operate. The 

once-dominant linear economy is 

giving way to a new paradigm where 

products remain in circulation for as 

long as possible. PdM is at the heart 

of this transformation; it ensures 

products and machinery remain functional 

and valuable through processes 

like repair, remanufacturing, and refurbishment. 

Imagine an industrial world where 

nothing is built to fail. A fitting metaphor 

can be drawn from the 1951 classic 

film “The Man in the White Suit,” 

where the protagonist invents a fabric 

that never wears out. This discovery 

wreaks havoc on the textile industry, as 

planned obsolescence—the practice of 

designing products to fail after a predetermined 

time—suddenly collapses. 

The film’s disruptive premise parallels 

the role PdM plays in industry today. 

Just like that indestructible white suit, 

it thwarts business models rooted in 

planned obsolescence. It extends the 

life of industrial components and assets, 

enabling companies to embrace 

sustainability by reducing waste and the 

need for constant replacements. 

Old electronics get a 

second life through 

smart maintenance and 

remanufacturing. 

PRODUCT-AS-A-SERVICE, 

EVERYONE WINS 

In the circular economy, innovative 

business models thrive by placing maintenance 

at their core. Take Product-asa-Service 

(PaaS) as an example. In this 

model, companies no longer simply 

sell products—they retain ownership 

and provide the product as a service, 

maintaining it throughout its lifecycle. 

Consider aircraft engine manufacturer 

Rolls-Royce, for example. Instead of 

selling engines outright, it offers them 

as a service to airlines, shouldering the 

responsibility of maintenance and ensuring 

optimal performance. Then, by 

using PdM to monitor engine health in 

real time, Rolls-Royce keeps its engines 

running efficiently for longer periods, 

minimizing waste and supporting circular 

economy principles by extending the 

lifecycle of each asset. 

A NEW LIFE FOR OLD 

EQUIPMENT 

Remanufacturing offers another compelling 

business model built on the 

foundation of PdM. The automotive 

industry has embraced remanufacturing 

to reduce resource consumption 

and lower production costs. Companies 

like Renault have perfected the art of 

remanufacturing by refurbishing old 

engines and making them nearly as 

good as new. But the success of remanufacturing 

hinges on maintenance—particularly 

PdM—to monitor the health 

of each engine and intervene before 

catastrophic failures occur. By tracking 

the wear and tear of individual components, 

PdM ensures critical parts reach 

their full potential, supporting remanufacturing 

efforts and enabling the reuse 

of valuable resources. 

CLOSING THE LOOP AND MINI- 

MIZING WASTE 

Perhaps the most significant contribution 

of maintenance to the circular 

economy is its ability to enable closedloop 

supply chains. In these systems, 

products are returned to the manufacturer 

at the end of their lifecycle to be 

remanufactured, recycled, or refurbished. 

PdM optimizes this process by 

providing manufacturers with real-time 

data on the condition of components 

throughout their lifecycle. When a 

product returns for remanufacturing, 

manufacturers know exactly which 



parts can be reused and which need recycling. 

This allows maximum resource 

efficiency, directly aligning with the circular 

economy’s ultimate goal: to keep 

materials in circulation for as long as 

possible while minimizing waste. 

Without maintenance, specifically 

PdM, the circular economy would fail. 

Maintenance is the invisible force that 

holds circular business models together, 

ensuring assets remain in optimal 

condition for as long as possible. In this 

way, maintenance becomes the enabler 

of a new industrial era—one where assets 

are designed to last, resources are 

preserved, and sustainability is the new 

business-as-usual. 

SUSTAINABILITY IN MOTION: 

TRACKING THE IMPACT OF 

MAINTENANCE THROUGH KEY 

INDICATORS 

Industries pivoting toward sustainability 

must evaluate the effectiveness 

of their maintenance strategies—not 

just in terms of operational efficiency 

but also in terms of resource conservation 

and waste reduction. But in the 

fast-evolving world of circular economy 

practices, how do we measure success? 

The answer lies in key performance 

indicators (KPIs), which provide the 

metrics necessary to quantify the environmental 

and economic impact of 

maintenance activities. 

In the circular economy, maintenance 

transcends its traditional role of 

keeping machines running to become 

a tool for optimizing resource use over 

time. KPIs allow companies to track 

this optimization in a tangible way, 

turning abstract sustainability goals into 

actionable, measurable results. Critical 

sustainability-focused KPIs include 

those tracking CO2 emissions avoided 

per repair, waste reduced per intervention, 

and energy saved through predictive 

or preventive maintenance. These 

metrics empower businesses to gauge 

the environmental value of extending 

the lifespan of assets, reducing the need 

for new components and conserving 

resources. 

SEE THE DIFFERENCE 

Every repair and every refurbishing 

effort can now be quantified in terms 

of its contribution to sustainability. 

Take the aviation industry, for example. 

Airlines use KPIs to measure CO2 

emissions saved per flight hour as a 

direct result of PdM. This allows them 

to calculate both financial savings and 

the environmental benefits of reducing 

unnecessary repairs and extending the 

lifespan of key components like turbine 

Industries pivoting 

toward sustainability must 

evaluate the effectiveness of 

their maintenance 

strategies. 

engines. These metrics paint a holistic 

picture of how maintenance practices 

align with sustainability goals, showing 

companies how they are actively contributing 

to a circular economy. 

The power of KPIs lies in their ability 

to offer data-driven insights that help 

refine strategies over time. Imagine a 

fleet of commercial planes, each fitted 

with sensors that constantly monitor 

engine health, fuel efficiency, and component 

wear. Through this continuous 

stream of data, businesses can adjust 

their maintenance strategies in real 

time to maximize both performance 

and sustainability. 

Another crucial set of KPIs focuses 

on component lifecycle management. 

In industries such as automotive manufacturing, 

where components like engines 

or gears can be repaired or remanufactured 

multiple times, KPIs track 

how well maintenance interventions 

extend the useful life of these parts. The 

ability to measure how much longer an 

AI-powered insights catch problems early, keeping operations smooth and sustainable. 



engine can run thanks to timely maintenance 

interventions directly contributes 

to reducing waste and lessening 

the demand for raw materials. 

The EN 15341:2019 standard offers 

a structured framework for maintenance 

performance measurement that 

incorporates both economic and environmental 

outcomes. By adhering to 

these standards, businesses can ensure 

their maintenance strategies are not 

just keeping machines operational but 

are also aligned with their sustainability 

objectives. Every data point tells a 

story—of resources saved, emissions reduced, 

and a world moving ever closer 

to a sustainable future. 

PLANNED OBSOLESCENCE: THE 

VILLAIN THAT PDM CAN DEFEAT 

In the narrative of sustainability, few 

villains loom as large as planned obsolescence. 

A business model designed 

to make products fail prematurely, it 

traps industries and consumers in a vicious 

cycle of consumption, waste, and 

replacement. Planned obsolescence is 

short-term strategy prioritizing profit 

over longevity, thus driving resource 

depletion and filling landfills with products 

that could have been repaired or 

maintained. 

"The Light Bulb Conspiracy", a documentary 

about the infamous Phoebus 

cartel of light bulb manufacturers, gives 

a powerful example of planned obsolescence. 

In the 1920s, the cartel conspired 

to limit the lifespan of light bulbs to 

1,000 hours, even though technology 

existed to make them last far longer. This 

conspiracy ensured consumers were 

forced to purchase new bulbs frequently, 

driving profits at the expense of sustainability. 

In stark contrast, the Centennial 

Light Bulb in Livermore, California, has 

lasted for over a century—an example of 

what is possible when products are designed 

for durability, not disposability. 

BREAKING FREE 

AND LIVING LONG 

PdM offers industries a way to break 

free from planned obsolescence. By using 

AI, IoT sensors, and real-time data, 

PdM systems can detect subtle signs of 

wear and tear before catastrophic failure 

occurs. This proactive approach allows 

companies to maintain and repair 

products at the right time, extending 

their lifespan and conserving resources. 

Take the smartphone industry, where 

devices are often designed with nonreplaceable 

batteries and components 

that become obsolete within just a few 

years. In this model, consumers are 

forced to buy new phones instead of repairing 

or upgrading their old ones. But 

imagine a world where smartphones 

are equipped with sensors that monitor 

battery health and alert users when it is 

In the narrative of 

sustainability, few villains 

loom as large as planned 

obsolescence. 

time to replace a part. Instead of throwing 

the device away, consumers could 

extend its life through a simple repair, 

drastically reducing e-waste. This is 

the promise of PdM—a model where 

products are designed for longevity, not 

failure. 

The automotive industry tells a similar 

story. Planned obsolescence has led 

to the creation of sealed components, 

such as transmissions or electronic 

control units, which are nearly impossible 

to repair. This forces car owners to 

replace entire systems rather than fix 

individual parts, contributing to a massive 

amount of waste. But with PdM, 

the health of these components can be 

monitored throughout their lifecycle. 

By detecting early signs of wear and 

tear, maintenance can be performed 

before the part fails, allowing repairs 

instead of replacements. 

At its core, the battle between 

planned obsolescence and PdM is one 

of philosophies. On the one side, we 

have a system built on disposability 

and frequent replacement—generating 

enormous waste and depleting valuable 

resources. On the other, we have a system 

that values longevity, repairability, 

and sustainability. In this battle, PdM 

emerges as the victor, offering a way to 

escape the wasteful cycle of planned 

obsolescence, extending the life of 

products, reducing waste, and creating 

a future where resources are conserved 

for generations to come. 

REAL-WORLD APPLICATIONS OF 

PDM: FROM FACTORY FLOORS 

TO CIRCULAR FUTURES 

From manufacturing plants to high-tech 

industries, PdM is being used not only 

to keep machines running efficiently 

but also to drive the circular economy. 

Picture a sprawling industrial facility, its 

machinery humming with activity. In the 

traditional linear economy, when one 

critical part of this system breaks down, 

the ripple effect leads to downtime, 

wasted resources, and costly replacements. 

But in today’s world of PdM, 

something entirely different happens. 

Sensors track vibrations, temperatures, 

and energy consumption, analysing the 

data in real time to detect subtle wear 

and tear. Long before a breakdown occurs, 

maintenance systems flag the 

component for repair or replacement, 

keeping the factory running smoothly 

and efficiently, with minimal disruption. 

This means fewer wasted parts, 

less downtime, and significantly lower 

operational costs. In essence, the factory 

becomes a symbol of circularity—doing 

more with less. 

Heavy industry has been quick to 

adopt PdM. Take the massive turbines 

powering energy plants or the complex 

conveyor systems in large-scale 

factories, for example. In the past, 

maintenance was performed according 

to fixed schedules, regardless of actual 

need, often leading to unnecessary part 

replacements or equipment overuse. Today, 

with condition-based maintenance, 

equipment is continuously monitored, 

and maintenance is performed only 

when necessary, saving companies both 

time and resources. The machines continue 

running, producing at full capacity 

while using fewer materials—a perfect 

alignment with the goals of a circular 

economy. 

SENSORS KNOW 

– AND TELL US SO 

In the automotive sector, PdM has become 

a game changer. Once, engines 

and components were designed to be 

replaced after a fixed number of miles, 

but today’s vehicles are built with sensors 

that monitor every aspect of their 

operation. These sensors provide realtime 

data on everything from oil levels 

to brake wear, ensuring components 

are maintained or replaced only when 

necessary. This extends the life of the 

vehicle and keeps valuable materials in 

use for longer, reducing the need for raw 

resource extraction. 

Even in the consumer electronics 

industry—historically dominated by 

planned obsolescence—PdM is making 

waves. Smartphones and laptops, 



In the 1951 classic film “The Man in the White Suit,” the protaganist invents a fabric that never wears out. Photo: United Archives GmbH / Alamy Stock Photo 

once considered disposable after a few 

years of use, are now being equipped 

with smart maintenance systems. These 

systems can detect when a battery is 

degrading or a processor is under strain, 

permitting a simple repair or part replacement 

instead of a new purchase. 

This shift is crucial in reducing the massive 

amounts of electronic waste that 

end up in landfills each year. PdM ensures 

technology can have a much longer 

and more sustainable lifecycle. 

In every industry, from consumer 

goods to heavy manufacturing, maintenance 

is stepping into a new role as the 

hero of the circular economy. It is no 

longer just about fixing what is broken— 

it is about rethinking how products and 

machinery are designed, maintained, 

and kept in use. Beyond improving sustainability, 

this represents a financial 

boon for companies. Reducing waste, 

conserving resources, and extending the 

life of equipment create significant cost 

savings, blurring the line between profitability 

and sustainability. 

PdM is the key to unlocking the full 

potential of the circular economy. By 

maximizing the use of resources, minimizing 

waste, and ensuring equipment 

and products last as long as possible, 

industries are setting themselves up for 

long-term success, both financially and 

environmentally. PdM is the driving 

force behind a sustainable, circular future—proving 

that what’s good for business 

can also be good for the planet. 

PDM is the key to 

unlocking the full 

potential of the circular 

economy. 

CONCLUSION: THE UNSUNG 

HERO OF SUSTAINABILITY 

– MAINTENANCE IN THE 

CIRCULAR ECONOMY 

The journey from a linear economy to 

a circular one isn’t just about reducing 

waste; it is about fundamentally rethinking 

how we interact with the products 

and machines we rely on. In this new 

reality, industrial assets—from turbines 

and engines to smartphones and consumer 

electronics—are no longer seen 

as disposable, short-lived items. They 

are valuable resources that can be kept 

in use for far longer through PdM, which 

leverages data, AI, and IoT to maximize 

asset longevity and performance. 

Beyond simply making products last 

longer, PdM drives value at every stage of 

the product lifecycle, from initial design 

through reuse, repair, refurbishment, 

and beyond. The wasteful pattern of discarding 

and replacing is giving way to a 

new model where products are continuously 

monitored, maintained, repaired, 

and kept in use for as long as possible. 

Resources are cycled back into the production 

loop instead of ending up in 

landfills, and maintenance becomes the 

keystone in a sustainable development 

strategy. 

Industries that are embracing PdM 

are discovering that it isn’t just an environmental 

responsibility—it is a competitive 

advantage. By reducing costs, 

increasing operational efficiency, and 

optimizing the lifespan of assets, PdM 

supports the core goals of the circular 

economy: resource efficiency, waste reduction, 

and long-term sustainability. As 

industries continue to evolve, those that 

embrace PdM will find themselves at the 

forefront of a new, more sustainable, and 

more profitable industrial paradigm. The 

unsung hero of the circular economy, 

maintenance, is quietly but powerfully 

reshaping the future of industry. 


INDUSTRIAL MECHANIC 

Monitor and Reduce 

Motor Operating 

Temperature to 

Increase Reliability 



Understanding and monitoring your motor’s recommended 

operating temperature can drastically lengthen its lifespan. 

Here we tell you how to do both, and so avoid early and 

unnecessary failure. 

TEXT: THOMAS H. BISHOP, P.E. EASA SENIOR TECHNICAL SUPPORT SPECIALIST 

PHOTOS: EASA AND SHUTTERSTOCK 

It is a striking fact that operating 

a three-phase induction 

motor at just 10°C above its 

rated temperature can shorten 

its life by half. Whether your facility 

has thousands of motors or just a few, 

regularly checking the operating temperature 

of critical motors will help 

extend their life and prevent costly, 

unexpected shutdowns. Here is how 

to go about it. 

First, determine the motor’s insulation 

class (A, B, F or H) from its 

original nameplate or the ratings for 

three-phase induction motors in the 

National Electrical Manufacturers 

Association (NEMA) standard Motors 

and Generators, MG 1-2021 (hereafter 

MG1). The insulation class indicates 

the maximum temperature that 

the motor’s winding can withstand 

without degrading (see Table 1). 

That sounds simple enough. To 

protect the motor, just keep its winding 

temperature below its insulation 

class rating. But there is a little more 

to it. 

The largest component of the 

winding temperature is heat from 

Table 1. Temperature ratings 

by insulation class 

Temperature 

Insulation class 

rating 

A 105°C 

B 130°C 

F 155°C 

H 180°C 

Figure 1. Hot spot temperature versus ambient and rise for Class B insulation system. Note that 

at 40°C ambient (horizontal axis), the rise is 90°C (vertical axis). The sum of the ambient and 

temperature rise will always be 130°C for a Class B insulation system. 

120 

SHUT DOWN AND ALARM RANGE BASED ON INSULATION SYSTEM 

Motor winding temperature (¡C) 

110 

100 

90 

80 

70 

60 

Thermal load capacity 

Ambient limit 

50 

40 

0 

20 30 40 50 60 70 80 

Ambient temperature (¡C) 



Operating a threephase 

induction motor at 

just 10°C above its rated 

temperature can shorten its 

life by half. 

motor operation (called temperature 

rise), which is load-dependent. The 

rest is attributable to the ambient 

(room) temperature. Identifying 

both components of the winding temperature 

makes it possible to protect 

the motor winding under different 

operating conditions (e.g., a lower 

ambient temperature may permit a 

higher temperature rise). NEMA also 

incorporates a safety factor, but more 

on that later. 

As with insulation class, every 

motor built to NEMA standards will 

have an ambient temperature rating 

(normally 40°C for three-phase 

motors). This is the maximum temperature 

for the air (or other cooling 

medium) surrounding the motor. Like 

the insulation class rating, you can 

find this rating on the motor nameplate 

or in MG1. 

DETERMINE THE “HOT” 

TEMPERATURE 

The next step is to measure the 

overall (“hot”) temperature of the 

winding with the motor operating at 

full load–either directly using embedded 

sensors or an infrared temperature 

detector, or indirectly using the 

resistance method explained below. 

The difference between the winding 

temperature and the ambient temperature 

is the temperature rise. 

Put another way, the sum of the ambient 

temperature and the temperature 

rise equals the overall (or “hot”) temperature 

of the motor winding or a 

component. 

Ambient temp. + Temp. rise = 

Hot temp. 

To avoid degrading the motor’s insulation 

system, the hot temperature 

must not exceed the motor’s insulation 

class temperature rating. 

Given that MG1’s maximum ambient 

temperature is normally 40°C, 

you would expect the temperature 

rise limit for a Class B 130°C insulation 

system to be 90°C (130° - 40°C), 

not 80°C as shown in Tables 2 and 3. 

But as mentioned earlier, MG1 also 

includes a safety factor, primarily to 

account for parts of the motor winding 

that may be hotter than where the 

temperature is measured, or that may 

not be reflected in the “average” temperature 

obtained by the resistance 

method. 

Table 2 shows the temperature rise 

limits for MG1 medium electric motors, 

based on a maximum ambient of 

40°C. In the most common speed ratings, 

the MG1 designation of medium 

motors includes ratings of 1.5 to 500 

hp for 2- and 4-pole machines, and up 

to 350 hp for 6-pole machines. 

Temperature rise limits for large 

motors–i.e., those above medium 

motor ratings–differ based on the 

service factor (SF). Table 3 lists the 

temperature rise for motors with a 

1.0 SF; Table 4 applies to motors with 

1.15 SF. 

RESISTANCE METHOD 

OF DETERMINING 

TEMPERATURE RISE 

The resistance method is useful for determining 

the temperature rise of motors 

that do not have embedded detectors–e.g., 

thermocouples or resistance 

temperature detectors (RTDs). Note 

that temperature rise limits for medium 

motors in Table 2 are based on resistance. 

The temperature rise of large motors 

can be measured by the resistance 

method or by detectors embedded in 

the windings as shown in Tables 3 and 4. 

To find the temperature rise using 

the resistance method, first measure 

and record the lead-to-lead resistance 

of the line leads with the motor “cold”– 

i.e., at ambient temperature. To ensure 

accuracy, use a milli-ohmmeter for 

resistance values of less than 5 ohms, 

and be sure to record the ambient temperature. 

Operate the motor at rated 

load until the temperature stabilizes 

(possibly up to 8 hours) and then deenergize 

it. After safely locking out the 

motor, measure the “hot” lead-to-lead 

resistance as described above. 

Find the hot temperature by inserting 

the cold and hot resistance measurements 

into Equation 1. Then subtract 

the ambient temperature from the hot 

temperature to obtain the temperature 

rise. 

Table 2. Temperature rise by resistance method for medium induction motors based on a maximum ambient of 40°C 

1 

Motor type 

Electric motors with 1.0 service factor (SF) 

other than those in 3 or 4. 

Insulation class and 

temperature rise °C 

A B F H 

60 80 105 125 

2 All electric motors with 1.15 or higher SF 70 90 115 – 

3 

4 

Totally-enclosed non-ventilated electric motors 

with 1.0 SF 

Electric motors with encapsulated windings and 

with 1.0 SF, all enclosures 

65 85 110 130 

65 85 110 – 

Ref. MG 1, 12.43 



Table 3. Temperature rise for large motors with 1.0 service factor at rated load 

Motor rating 

Method of determination 

Insulation class and temperature rise °C 

A B F H 

1 All horsepower (or kW) ratings Resistance 60 80 105 125 

2 1500 hp (1120 kW) and less Embedded detector 70 90 115 140 

3 

4 

Ref.: MG 1, 20.8.1. 

Over 1500 hp (1120 kW) 

and 7000 volts or less 

Over 1500 hp (1120 kW) 

and over 7000 volts 

Embedded detector 65 85 110 130 


Table 4. Temperature rise for large motors with 1.15 service factor at rated load 

Motor rating 

Method of determination 

Insulation class and temperature rise °C 

A B F H 

1 All horsepower (or kW) ratings Resistance 70 90 115 135 

2 1500 hp (1120 kW) and less Embedded detector 80 100 125 150 

3 

4 

Ref.: MG 1, 20.8.2. 

Over 1500 hp (1120 kW) 

and 7000 volts or less 

Over 1500 hp (1120 kW) 

and over 7000 volts 



To avoid degrading the motor’s 

insulation system, the hot temperature 

must not exceed the motor’s insulation class 

temperature rating. 

Install thermocouple on center line of 

laminations through outlet box opening 

Equation 1. Hot winding temperature 

Th = [ (Rh/Rc) x (K + Tc) ] - K 

Where: 

Th = hot temperature 

Tc = cold temperature 

Rh = hot resistance 

Rc = cold resistance 

K = 234.5 (a constant for copper) 

Example. To calculate the hot winding temperature for 

an un-encapsulated, open drip-proof medium motor with 

a Class F winding, 1.0 SF, lead-to-lead resistance of 1.21 

ohms at an ambient temperature of 20°C, and hot resistance 

of 1.71 ohms, proceed as follows: 

Th = [ (1.71/1.21) x (234.5 + 20) ] - 234.5 = 125.2°C 

(round to 125°C) 

The temperature rise equals the hot winding temperature 

minus the ambient temperature, or in this case: 

Temp. rise = 125°C - 20°C = 105°C 

As Table 2 shows, the calculated temperature rise of 

105°C in this example equals the limit for a Class F in- 

Center line 

of motor 

Figure 2. It may be possible to determine the approximate 

temperature of the winding with a thermocouple. 



Don’t let excessive 

heat kill your motors 

before their time. 

ABOUT THE AUTHOR 

Thomas Bishop is a senior technical support specialist at EASA, 

St. Louis, MO; 314-993-2220; 314-993-1269 (fax); www.easa.com. 

EASA is an international trade association of over 1800 electromechanical 

sales, service and repair firms in nearly 80 countries. 

sulation system. Although that is acceptable, 

it is important to note that 

any increase in load would result in 

above-rated temperature rise and 

seriously degrade the motor’s insulation 

system. Further, if the ambient 

temperature at the motor installation 

were to rise above 20°C, the motor 

load would have to be reduced to 

avoid exceeding the machine’s total 

temperature (hot winding) capability. 

DETERMINING TEMPERATURE 

RISE USING DETECTORS 

Motors with temperature detectors 

embedded in the windings are usually 

monitored directly with appropriate 

instrumentation. Typically, 

the motor control centre has panel 

meters that indicate the hot winding 

temperature at the sensor. If the 

panel meters were to read 125°C as 

in the example above, the same concerns 

about the overall temperature 

would apply. 

What if you want to directly 

measure the operating temperature 

of a motor winding that does not 

have embedded detectors? For motors 

rated 600 volts or less, it may 

be possible to open the terminal 

box (following all applicable safety 

rules) with the motor de-energized 

and access the outside diameter of 

the stator core iron laminations 

with a thermocouple (see Figure 2). 

The stator lamination temperature 

will not be the same as winding temperature, 

but it will be nearer to it 

than the temperature of any other 

readily accessible part of the motor. 

If the stator lamination temperature 

minus the ambient exceeds the 

rated temperature rise, it is reasonable 

to assume the winding is also 

operating beyond its rated temperature. 

For instance, had the stator 

core temperature in the above example 

measured 132°C, the temperature 

rise for the stator would have 

been (132°C - 20°C), or 112°C. That 

significantly exceeds MG1's limit of 

105°C for the winding, which can be 

expected to be hotter than the laminations. 

The critical limit for the winding 

is the overall or hot temperature. 

Again, that is the sum of ambient 

temperature plus the temperature 

rise. The load largely determines 

the temperature rise because the 

winding current increases with load. 

A large percentage of motor losses 

and heating (typically 35 - 40%) is 

due to the winding I 2 R losses. The 

“I” in I 2 R is winding current (amps), 

and the “R” is winding resistance 

(ohms). Thus, the winding losses 

increase at a rate that varies as the 

square of the winding current. 

ADJUSTING FOR AMBIENT 

If the ambient temperature exceeds 

the usual MG1 limit of 40°C, you must 

derate the motor to keep its total 

temperature within the overall or hot 

winding limit. To do so, reduce the 

temperature rise limit by the amount 

that the ambient exceeds 40°C. 

For instance, if the ambient is 

48°C and the temperature rise limit 

in Table 2 is 105°C, decrease the 

temperature rise limit by 8°C (48°C 

- 40°C ambient difference) to 97°C. 

This limits the total temperature 

to the same amount in both cases: 

105°C plus 40°C equals 145°C, as 

does 97°C plus 48°C. 

Regardless of the method used to 

detect winding temperature, the total, 

or hot spot, temperature is the 

real limit; and the lower it is, the 

better. Each 10°C increase in operating 

temperature shortens motor 

life by about half, so check your 

motors under load regularly. Don’t 

let excessive heat kill your motors 

before their time. 



ARTIFICIAL INTELLIGENCE IN MAINTENANCE | PART 1 

80 percent of 

companies say they 

are planning or have 

started AI-related 

projects. 

Artificial 

Intelligence as 

a Maintenance 

Enhancer 



In three articles published in this issue of Maintworld magazine, we will 

look at how AI can improve maintenance efficiency. This first article 

describes the concepts and issues related to AI in general. The next will 

go into more detail on its use in maintenance. 

TEXT: HANNU NIITTYMAA, SOLUTIONS ENGINEER, IBM SUSTAINABILITY SOFTWARE 

PHOTOS: IBM, SHUTTERSTOCK, FREEPIK 

is already part of our everyday lives, but in the 

future, it is expected to fundamentally change 

our society. Eighty percent of companies say 

AI they are planning or have already started AIrelated 

projects. Overall, global GDP is expected to grow by 

up to 7% over the next ten years thanks to AI. 

AI has also been used in maintenance and in the coming 

year, the potential of new generic AI models will be increasingly 

brought to maintenance. In the future, this will 

also strongly change the operating models and practices of 

maintenance. 



- Despite the undoubted benefits of AI, 

there are many aspects of generative AI 

in particular that those using it need to 

consider, says the author of the article, 

Hannu Niittymaa, Solutions Engineer, IBM 

Sustainability Software. 

WHAT IS AI? 

AI is a broad and multi-dimensional 

concept. It is also used loosely in very 

different contexts. In simple terms, 

AI refers to the ability of a machine to 

use skills traditionally associated with 

human intelligence, such as reasoning, 

learning, designing or creating. Even 

the definition of human intelligence is 

not unambiguous but, in some ways, a 

philosophical conundrum. AI is therefore 

not a single technology or solution, 

but a set of different technologies, applications, 

methods and research directions, 

depending 

on the point of 

view. 

At a high level, 

AI is often divided 

into weak and 

strong AI. 

WEAK AI 

Weak AI is also referred to as applied, 

narrow and traditional AI. Weak AI 

refers to systems that are designed to 

solve a specific task. Examples of weak 

AI solutions include Apple's Siri assistant 

and Netflix’s recommendation 

engine. Unlike traditional programming, 

where all rules must be defined 

individually, weak AI models are 

trained for a specific task using training 

data. The trained models can then find 

regularities, anomalies, relationships, 

etc. in the data. Most current AI solutions 

are based on weak AI. 

Weak AI technologies include machine 

learning, deep learning, neural 

networks, speech recognition and 

machine vision. In particular, in deep 

learning, complex model structures can 

lead to a loss of generalisability of the 

solution. 

Solutions based 

on weak artificial 

intelligence are 

already in use in 

maintenance. 

STRONG ARTIFICIAL 

INTELLIGENCE 

Strong AI, also referred to as general, 

creative or human-level, aims to 

mimic human cognitive ability in the 

most general way and is capable of 

performing tasks that go beyond human 

intelligence. 

Such an AI 

would be able to 

learn and understand 

complex 

concepts, apply 

knowledge to different 

situations, 

and demonstrate 

creativity and abstract thinking in a 

wide range of tasks. This requires a 

deeper understanding of the meanings 

of data sets. However, solutions based 

on strong AI are not yet in practice. 

GENERATIVE AI 

Currently, AI is often referred to as 

generative AI, but this is not the same 

as strong AI. Generative AI refers to 

a subset of machine learning that focuses 

on creating new content, such as 

images, music or text. It is often based 

on deep learning and neural networks 

that are trained to create complex 

models and generate new content. 

While generative AI can be highly 

advanced and capable of producing 

impressive content, it has not yet 

reached the level of strong AI, where 

a machine is capable of independent 

FORECAST OF INVESTMENT IN AI IN THE COMING YEARS 

2021-22 2023 (est.) 

2024-25 

(est.) 

IT expenditure as a share of 

annual revenue 

Traditional AI as a share of 

IT expenditure 

Share of traditional AI in 

the amount of money spent 

on generative AI 

5.4% 6.6% 7.9% 

6.4% 7.7% 9.2% 

1.5% 2.2% 2.8% 

More investment in traditional 

AI or non-AI can be expected as 

ecosystem partners (e.g. SAP, Salesforce, 

Microsoft) incorporate 

generative AI into their products. 

The low level of direct spending may 

reflect a "wait and see" approach 

to competitive dynamics before 

committing to larger investments in 

an uncertain environment. 

Source: 2023 IBM IBV Generative AI global survey 



thought and understanding in the 

same way as a human. 

Generative AI is suitable for illustrating 

solutions and creating something 

new. For example, it can be used 

to generate entirely new text, speech, 

images, sound or code. These can be 

varied based on previous implementations. 

Humans do this quite commonly. 

Creating something new is demanding 

for both AI and humans. 

Generative AI has been made 

possible by new advanced AI foundation 

models developed by corporate 

research and development teams 

using massive computational and data 

resources. 

One example of an advanced AI 

foundation model is the Large Language 

Model. These are the basis for 

AI solutions for text analysis and research. 

These models can also be used 

to summarise and translate previously 

written texts. 

For example, ChatGPT, developed 

by Open AI, is based on a large language 

model. A Chatbox-like interface 

allows you to ask questions. These 

questions are answered based on the 

information that is used by the model 

- in practice almost all public information 

on the Internet – and the answer 

is given in the way the model sees fit. 

In addition to the broad language 

models, there are a large number of 

other advanced AI-based models for 

a variety of uses. Advanced AI models 

have been developed by Amazon, 

Google, IBM, Microsoft, NVIDIA and 

several smaller players. 

WHAT CAN AI BE USED FOR? 

Solutions based on weak AI are 

already in use in maintenance today. 

For example, equipment condition 

monitoring and solutions that predict 

future equipment failures are based on 

weak AI technologies such as machine 

learning. The next part of this series of 

articles will explore these solutions in 

more detail. 

Typical applications of generative 

AI include automation of various customer 

service situations, automation 

of IT support services, automation of 

repetitive routines such as job application 

reviews, etc. The interactive use 

of experience by varying questions is 

the basis for this growing set of applications. 

Despite the huge attention that 

From a security point of view, information uploaded to the ChatGPT service is public 

information for everyone. 

generative AI has received, the use of 

generative AI in general is still limited 

and has not yet been widely exploited 

in maintenance. However, various 

maintenance system vendors are heavily 

developing their systems in such a 

way that generative AI can be used in 

those use cases where it makes sense. 

The final part of this series of articles 

will explore these in more detail. 

It is of course an intriguing idea 

that AI could easily and autonomously 

handle all of the above. However, there 

is a huge amount of research, algorithms, 

models and technology behind 

the various AI models. Deep human 

expertise is still needed to make use of 

all this. 

THE CHALLENGES OF 

HARNESSING AI 

Despite the undeniable benefits of AI, 

there are many issues, especially with 

generative AI, that those using it need 

to consider. Some of these issues are 

very practical, while others may involve 

larger ethical issues. 

An AI model is trained against a set 

of data, and the quality and representativeness 

of this set of data will determine 

to a large extent, the usefulness of 

the solution for a specific application. 

There are many ways to improve the 

quality of the data. For example, signal 

analysis and feature extraction can be 

used to build indicators that describe 

performance better than mere measurements. 

Data analysis would provide 

a more meaningful basis for cognitive 

analysis, i.e. AI proper. So how much 

data should and should not be manipulated? 

In using AI, it is also important that 

the problem to be solved is of the right 

size. AI can be used to try to find solutions 

to large-scale problems, but this 

should be done with great caution. The 

result may look good, but is it reliable? 

From a security point of view, it 

is important to note that the data 

The umbrella term AI 

covers a wide range of 

technologies, applications, 

methodologies and research 

directions. 



exported to public intelligence services, 

such as ChatGPT, is public data for 

everyone from a security point of view. 

AI models can be trained on data 

sets whose quality and origin are not 

known. This can lead to offensive, 

biased or outright wrong answers. 

Not knowing how the AI arrived at 

its answer also calls into question the 

reliability and usefulness of the answer. 

Verification is needed to identify 

biases and errors. 

There may also be copyright restrictions 

associated with AI solutions and 

the data sets used, which need to be 

taken into account. 

For these reasons, there must be 

governance mechanisms in place to 

ensure that the risks associated with 

AI are properly assessed. Risks may be 

related to the reputation of the company 

and regulatory or operational 

issues. Ultimately, it is always up to 

the user to evaluate the quality of the 

answers provided by AI. 

DATA FOR DECISION-MAKING 

According to a wide range of studies, 

companies consider the use of AI to 

be one of their top priorities for the 

future. AI provides additional data for 

decision-making. It can deliver significant 

business benefits and the payback 

period for investing in AI can be very 

Expertise and AI are 

mutually supportive. 

short. It would therefore be good for 

companies to explore the potential of 

AI while taking into account the challenges 

and risks associated with AI for 

decision-making. Expertise and AI are 

mutually supportive. 

In the following parts of this article 

series, we will discuss in more detail 

how AI can be used specifically in the 

development of maintenance. 

CONCERNS RELATED TO THE USE OF AI 

Explainability Ethics Prejudices Trust 

48% 46% 46% 42% 

Leaders believe that the 

decisions made by generative 

AI are not sufficiently 

understandable. 

Leaders are concerned about 

the security and ethical aspects 

of generative AI. 

Leaders believe that generative 

AI will spread prevailing 

prejudices. 

Leaders believe that generative 

AI cannot be trusted. 

Source: 2023 IBM IBV Generative AI global survey 


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Text and photos: UE Systems 

The Role of Ultrasound 

in Maintenance 4.0 

– Enabling Predictive 

Maintenance 

Industry 4.0, the ongoing digital transformation of manufacturing, 

emphasizes intelligent machines, data-driven decision-making, 

and interconnected operations. 

Maintenance 4.0 will be a 

fundamental part of this 

revolution and maintenance 

teams will need 

to shift from reactive maintenance 

(fixing problems after they occur) to 

predictive maintenance (anticipating 

and preventing failures). We will take 

a close look at how ultrasound sensors 

and data-collection instruments will 

play a key role in this shift to Maintenance 

4.0. 

By capturing these 

early warnings, 

ultrasound empowers 

proactive maintenance 

strategies. 

THE ROLE OF ULTRASOUND 

In the era of Maintenance 4.0, ultrasound 

technology emerges as a critical 

tool for pinpointing equipment health. 

Ultrasound excels at detecting subtle 

changes, like increased bearing friction 

- a telltale sign of bearing wear, bearing 

damage or even lubrication deficiencies. 

By capturing these early warnings, 

ultrasound empowers proactive maintenance 

strategies. Technicians can 

address lubrication issues before they 

escalate, preventing costly breakdowns 

and maximizing equipment lifespan. 

ULTRASOUND'S APPLICATIONS 

AND ADVANTAGES: SHINING A 

LIGHT ON HIDDEN ISSUES 

Ultrasound proves particularly valuable 

in several key areas of Maintenance 

4.0 and has some unique 

strenghts: 

Bearing Condition Monitoring 

– Earliest Warning of Failure: 

Ultrasound effectively identifies bearing 

wear, lubrication deficiencies and 

other issues by simply monitoring any 

increase in friction. This allows for 

corrective actions before breakdowns 

occur or even before failure itself. 

Optimizing Lubrication: Did you 

know that 60% to 80% of bearing failures 

are related to poor lubrication? 

Ultrasound helps assess lubrication 

effectiveness, ensuring optimal performance 

and extending equipment 

life. Ultrasound instruments will also 

help maintenance technicians to be 

sure they apply only the right amount 

of lubricant, avoiding the very common 

problem of over-lubrication. 

Monitoring Slow Speed Bearings: 

Traditional vibration analysis 

often struggles with slow-speed machinery. 

Since ultrasound will monitor 

any increase in friction, it is very 

effective in monitoring the health of 

these critical components. 



CASES & EXAMPLES 

Using Ultrasound-Based 

Solutions for Early Failure 

Detection 

CASE 1: Bearing Fatigue Detected in a Flour Milling Plant 

A flour milling facility invested in 

placing ultrasound sensors on multiple 

critical bearings. The sensors 

are connected to a data processing 

unit – called the 4Cast – which then 

connects to a data management software. 

This setup makes it possible 

for the maintenance team to be notified 

via email or SMS as soon as the 

4Cast detects that a certain bearing 

has reached an alarm level – it is possible 

to setup low level and high level 

alarms. 

Because the idea was to monitor 

critical bearings, the maintenance 

team decided to let the system warn 

them as soon as a low alarm level is 

achieved. We can see in this image 

how the dB readings from this bearing, 

belonging to a sifter, kept hitting 

the low alarm and would not stay 

below the baseline level. The team 

decided to take a better look into the 

sound file from this bearing, and this 

is how it looked like. 

Sound recording of the defective bearing. 

Sound recording of a bearing whose readings were according to baseline. 

When compared to a sound recording 

from a bearing whose readings 

were according to baseline, it 

became very clear that the bearing 

had some sort of defect. Notice the 

difference in amplitude between 

the spectrum of these sound files. 

The team decided to tear the 35 cm. 

bearing apart to confirm the damage, 

finding clear signs of bearing fatigue, 

observed in both the outer race and 

the rolling elements. 

Usually, in pulp and paper plants we 

will find a wash floor or wash area, 

Bearing showing outer race damage. 



CASE 2: Critical bearing failure at a pulp and paper plant 

where the paper comes through to 

be thoroughly cleaned / bleached. 

That job is done by a machine called 

a bleach decker, which is considered 

a critical and fundamental piece of 

equipment for production operations. 

In this particular plant, which has 

a predictive maintenance program in 

place, it was decided to invest in online 

monitoring for these machines, 

using ultrasound sensors. This machine 

has 4 bearings, of about 120 cm 

diameter, rotating at 3 RPM. 

The online monitoring system 

(4Cast) received an unusual decibel 

reading from one of the ultrasonic 

sensors. The NDE (non-drive-end) 

bearing of this bleach decker was 

registering 17dB when, normally, a 

bearing rotating at such slow speeds 

like 3RPM should simply show a 0dB 

reading. 

This triggered the system to immediately 

alert the maintenance 

team. In this case, and even though 

the machine was apparently working 

as expected, the sound file spectrum 

showed a very different story. 

The peaks shown in this sound 

sample clearly indicate a problem 

with the bearing. Also, when reproducing 

the sound file, we could 

very clearly hear the impact noises. 

The failure was even more obvious 

when the sound file was compared 

to a sound recording from one of the 

other bearings. 

When the bearing was dismantled, 

the damage was clearly visible. The 

signs of impact are obvious. Also, 

metal fragments were found in the 

shaft, plus spalling, with some pitting, 

and slight abrasion were present 

in the outer race. 

Sound recording: the peaks clearly indicate bearing damage. 

Sound recording of a similar bearing in good condition. Notice 

how the spectrum is uniform. 

When the bearing was 

dismantled, the signs of impact 

were quite obvious. 


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Text: Eva Gerda, Adash Ltd. | Photos: Adash 

How to Monitor 

Critical Machines? 

Adash has launched new software for fault monitoring. In the development phase, 

AI did not bring significant benefits. The analytical software is based on industry 

experience. 

Some machines need to be monitored 

24/7. For such situations 

remote online monitoring 

systems are standard solutions 

these days. The word ‘remote’ can have 

many meanings, but in all of them you do 

not physically stand next to the machine. 

The machine is equipped with sensors 

and the values are measured by a unit 

connected to the sensors. 

In one case, the values can be displayed 

in the control room. However, not 

all the monitored data is easily readable 

by the maintenance staff in the control 

room. For example, temperature and 

pressure are easily understood by everyone, 

but what about vibration values? 

What is 6 mm/s? Is it a low or a high value? 

Should the machine be stopped? Do 

I call an expert? What do I do with all the 

numbers I have in the control room? Do 

I need to hire a vibration expert to check 

these values? If the vibration expert is 

going to analyse the data occasionally, 

it's not really 24/7 monitoring, is it? 

Another option for remote monitoring 

is to store the values in the cloud, 

where an analyst checks the data on a 

regular basis. In this case, the vibration 

expert is involved in the scenario, but 

that's not 24/7 monitoring either. It also 

needs to be decided which vibration 

expert to hire. You can hire a vibration 

expert who can go out in the field and get 

to know the machines first, which is certainly 

the right choice. Or you can hire a 

team of diagnosticians who are remote 

and may be cheaper, but they may be 

so far away that they can't visit the field 

(if they've ever been to any machinery 

park). 

The ideal situation would be for a 

vibration expert to monitor the measured 

values continuously, but that is not 

possible in the real world. At Adash, we 

The current severity of machine faults can be displayed in a control room 24/7. 



Eva Gerda, eva.gerda@adash.cz, Adash Ltd. 

have been trying to solve this problem 

for years. We have been looking for a way 

to help maintenance staff find machine 

faults without the constant presence of a 

vibration diagnostics expert. 

WE TALK DIRECTLY TO OUR 

CUSTOMERS 

New features in Adash products are developed 

according to customer requirements. 

We are in direct contact with our 

customers all the time. You can call our 

office and speak directly with an expert. 

There is no administrative wall between 

our customers and the Adash office. As a 

result, we always have a list of customer 

requirements of what they lack in the 

field of vibration diagnostics. One of the 

recurring requests was for an online 

monitoring system with a feature that 

automatically detects machine faults. 

10 years ago, we developed an expert 

system called FASIT (Fault Source 

Identification Tool) that shows machine 

faults and their severity. The FASIT 

system is designed exclusively for Adash 

portable vibration meter and analyzers. 

It means you have to physically stand 

next to the machine to find faults. However, 

the big step was to enable maintenance 

staff who have no knowledge of 

vibration diagnostics to do so. Without 

any knowledge of spectra, frequency 

bands or other terms, maintenance personnel 

could see directly from the screen 

of the portable analyser, for example: an 

unbalance has been detected, its severity 

is high and balancing is necessary. This 

was the first step towards something bigger. 

A few months ago we launched our 

new Online Monitoring Expert Guard 

Application (Omega for short). 

Simply put, Omega is an expert system 

that gives you information about the 

major problems with your machines and 

their severity. If you don't have a vibration 

diagnostics department, Omega 

gives you a great way to get information 

about the condition of your machines. 

It can also be a great help for your diagnostic 

teams. It really does monitor your 

machines 24/7. 

OMEGA IS ANALYTICAL 

SOFTWARE, NOT ARTIFICIAL 

INTELLIGENCE! 

Omega is analytical software based on 

industry experience. The creators of the 

software have many years of experience 

in vibration measurement and analysis. 

Omega software is not an artificial intelligence 

system. We have tried to use AI 

several times in the past, but we have 

found and empirically verified that it is 

not possible. There is a very simple reason 

for this. There are thousands of different 

machines with dozens of possible faults. 

There is no way to get sufficient training 

data. To put it simply, it can be compared 

to medicine. There are AI attempts to 

diagnose patients medically, and they 

are still not reliable enough. In this case, 

there is only one type of organism - the 

human body (in terms of machines - only 

one type of machine). And now let's imagine 

that there is an AI that reliably diagnoses 

humans and all animals. You can 

see that this is not possible. 

Omega software shows you a severity of machine faults, 

in this case looseness is the problem 

We have carried out 

the first beta project in 

several companies and 

the results look 

promising. 

HOW DOES IT WORK AND WHAT 

IS NEEDED TO USE IT? 

How exactly does the Omega software 

work and what does it take to use it? It 

has 2 parts. The first part is the software 

in the online monitoring unit (Adash 

A3716 or Adash A3800). You need to install 

the accelerometers on the machine. 

In the basic machine setup, there is one 

acceleration sensor mounted radially 

per bearing and one acceleration sensor 

mounted axially on the machine. 

If the speed is variable, you also need 

a speed sensor. Faults and their severity 

are identified directly in the A3716/ 

A3800 unit. The information is placed 

on Adash's OPC server, where it can be 

displayed, for example, on the control 

room screen. 

The second part is the software installed 

on the computer. You set up a 

measument tree with its measurement 

points and displays the measurement results 

and the Omega analysis. The Omega 

software automatically determines 

the measurements to be taken. The 



result is a traffic light-coloured graph 

showing the machine's faults and their 

degree of severity. You can use Adash 

Omega's graphical user interface for this, 

but it can also be displayed using thirdparty 

software you already use (in which 

case the data must be taken from Adash's 

OPC server). 

WHICH FAULTS CAN BE 

DETECTED IN THIS WAY? 

As we run the system, the trend curves 

start to fill in. The trend curves show 

the severity of the faults. The severities 

are basically calculated according to the 

thresholds defined in ISO 20816 part 3. 

The user can change the thresholds if necessary. 

The graphs are displayed in traffic 

light colours, which makes them easy to 

read for the user. There are no numbers to 

confuse the user, only the machine faults 

and a colour bar indicating their status. 

The following faults are displayed: 

unbalance, misalignment, looseness and 

bearing condition. You can see if these 

faults have been detected on the machine 

by using the traffic light colours. Green 

means that the fault is insignificant, orange 

means that you should be alert to the 

Simply put, Omega 

is an expert system that 

gives you information 

about the major 

problems with your 

machines and their 

severity. 

The basic machine measurement setup is 5 accelerometers, 4 in radial and 1 in axial 

direction. 

possible development of the fault, and red 

means that the severity of the fault is high. 

The trend graphs show the evolution of 

the severity of the fault. You can see the 

current value, but also the average value 

of the last hour, last day, last week and last 

month. Each bar also has a vertical black 

line showing the maximum and minimum 

values measured over that period. 

This shows whether the measured values 

have been stable or not. 

VIBRATION EXPERTS ALWAYS 

HAVE THEIR PLACE 

There is also one graph with a question 

mark icon. This graph is for situations 

where there is something wrong 

with the machine, but Omega doesn't 

know exactly what it is. In this case, you 

should call a vibration expert. You may 

be wondering, do I still need a vibration 

expert at the end of the day? Yes, of 

course! You always do! But with Omega 

software, the need for a vibration expert 

is greatly reduced. The importance of 

a human vibration expert can never be 

beaten by even the best analysis software. 

But the software must be developed 

to greatly assist the vibration expert 

and maintenance personnel, which 

was Adash's goal. 

The Omega software has been on the 

market for a short time and we are now 

eagerly awaiting feedback from users 

around the world. We have carried out 

the first beta project in several companies 

and the results look promising. If 

you too are joining the world of Omega 

users, we look forward to your comments. 

Your comments are of immeasurable 

value to us. 

What is the result of this? Physical 

presence can never be fully replaced by 

remote analysis. The software cannot 

see splashing oil or hear strange noises 

coming from a machine. However, remote 

diagnostics plays an important 

role in today's maintenance. 


INDUSTRY | ENERGY | INFRASTRUCTURE 

Reach Professionals 

in a Powerful Way 

Asset lifecycle management | Continuous improvement of production efficiency and 

maintenance | Operational reliability and risk management | Responsibility and sustainable 

development | Utilization of data, information and knowledge | Industrial information 

systems, data collection, digitalization, artificial intelligence | Organizations 

and competence development 

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which is available on 

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RESEARCH 

Towards an Energy-Efficient 

Production Process 

– Measuring and 

Evaluating Cost Savings 

and Environmental 

Benefits 

TEXT: VTT LTD, MINNA RÄIKKÖNEN, SAARA HÄNNINEN, AND TEUVO UUSITALO 

IMAGES: SHUTTERSTOCK, VTT 


RESEARCH 

Improving energy efficiency is crucial for reducing energy 

consumption and emissions in the production process 

while achieving cost savings. Comprehensive maintenance 

and energy efficiency go hand in hand, although reliably 

measuring these benefits can be challenging. 

The environmental and economic 

pressures to manufacture 

products in the most sustainable, 

energy-efficient and 

resource-efficient way are constantly 

increasing. Enhancing energy efficiency 

in manufacturing is a key strategy 

for meeting sustainability goals. It 

reduces both energy consumption, cost 

and harmful environmental impacts. 

Regular maintenance of machinery and 

equipment helps extend their lifetime 

and reduces the need for repairs, further 

supporting 

sustainability objectives. 

Moreover, 

investments in 

energy efficiency 

optimise energy 

consumption and 

generate long-term 

cost savings. 

THE ROLE OF 

LIFE CYCLE 

THINKING 

Life cycle thinking allows companies 

to assess the environmental, cost, and 

resource impacts of their production 

processes and equipment throughout 

their life cycle. Tools like Life Cycle 

Assessment (LCA), Life Cycle Costing 

(LCC) and Social Life Cycle Assessment 

(SLCA) provide frameworks for 

evaluating sustainability. 

LIFE CYCLE COSTING (LCC) 

Life Cycle Costing (LCC) focuses on 

minimising the life cycle costs of production 

processes, machinery and 

equipment at different life cycle stages, 

following the principles of IEC 60300- 

3-3 and ISO 15663. LCC can be used to 

plan and improve the efficiency of production 

processes and maintenance, 

compare different processes, select 

the most suitable alternatives, and reduce 

adverse environmental impacts. 

LCC assesses cost across all life cycle 

phases - design, procurement, installation, 

operation, maintenance, and 

LCC focuses on 

minimising the life cycle 

cost of machinery and 

equipment in the 

production process 

throughout their 

lifetime. 

end-of-life – optimising the total cost of 

ownership, including energy efficiency 

considerations. 

LIFE CYCLE ASSESSMENT (LCA) 

Life Cycle Assessment (LCA) evaluates 

the environmental impacts of a product 

or process throughout its life cycle. 

LCA is standardised by ISO, with ISO 

14040 outlining the main principles 

and framework, and ISO 14044 specifying 

the requirements and guidelines for 

conducting the assessment. Like LCC, 

LCA helps identify 

and energy-related 

challenges, offering 

insights to improve 

energy efficiency 

and reduce harmful 

environmental 

impacts. It also 

enables systematic 

comparison 

of alternatives, 

supporting more 

sustainable decision-making. 

CHALLENGES IN ASSESSING 

BENEFITS 

Real-time measurement of energy 

consumption, alongside the evaluation 

of associated economic and environmental 

benefits, is a central challenge 

in industrial energy efficiency projects. 

Verifying energy savings remains a 

challenge for the growth of the energy 

services sector. 

Both LCA and LCC require detailed 

data on energy consumption, waste, 

and maintenance measures to assess 

the environmental and cost savings 

from energy efficiency improvements 

accurately. Insufficient or poor quality 

economic and technical process data 

can affect the reliability of the analysis 

results, leading to misleading conclusions. 

Integrating and harmonising data for 

both environmental and economic 

impact assessment can be complex. Additionally, 

life cycle cost assessments 

Minna Räikkönen, 

Senior Scientist, VTT 

Teuvo Uusitalo, 


Saara Hänninen, 



RESEARCH 

Verifying energy 

savings remains a 

challenge for the growth 

of the energy services 

sector. 

often focus only on direct costs, often 

ignoring indirect cost impacts. There is 

also limited empirical evidence on the 

links between energy efficiency investments, 

long-term savings, and sustainability. 

Standardised methodologies 

are needed to address these challenges 

effectively. 

THE DENIM DIGITAL PLATFORM 

The European Commission's H2020 

research programme project, Digital 

Intelligence for collaborative ENergy 

management in Manufacturing 

(DENiM), developed a digital platform 

to integrate smart energy solutions 

into production systems and business 

processes. 

The project expanded LCC analysis 

by incorporating new elements such 

as productivity and material loss cost 

estimation. 

The development process regarding 

sustainability involved collaboration 

between researchers and four European 

manufacturing companies at different 

stages of advancing sustainable 

development and energy efficiency. 

This collaboration facilitated complementary 

and comparable reviews and 

sustainability assessments. 

SYSTEMATIC COST BREAKDOWN 

STRUCTURE 

As part of the project, a life cycle cost 

structure was developed to systematically 

assess the economic benefits of 

energy efficiency (see Figure 1). The 

structure divides costs into distinct categories, 

which are further broken down 

into cost parameters and functions. 

The main cost categories in the cost 

structure are capital expenditure 

(CAPEX), direct operating and maintenance 

costs (direct OPEX) and productivity 

and material loss cost. 

CAPEX covers all pre-start-up expenses, 

including capital expenditure on 

Figure 1: Life cycle cost structure to assess the economic benefits of energy efficiency. 

production assets, as well as significant 

enhancements during the machines’ 

lifetime. 

OPEX includes energy, labour, maintenance 

and waste treatment costs. 

Productivity and material loss costs account 

for material losses, rework, and 

machine unavailability, such as lost 

production due to downtime. 

ENVIRONMENTAL IMPACT 

ASSESSMENT 

Environmental impact assessment 

and the integration of LCA and LCC 

analyses, conducted as part of the 

DENiM project, were performed in 

collaboration with the University of 

Applied Sciences and Arts of Southern 

Switzerland (SUPSI). The environmental 

impact indicators applied align with 

the Global Reporting Initiative (GRI) 

standards, covering key dimensions 

such as materials, energy, water, emissions, 

and waste. The environmental 

impact assessment focuses on the use 

of production resources and production 

emissions. 

Seven impact categories from the Product 

Environmental Footprint (PEF) 

and the Organisation Environmental 

Footprint (OEF) were selected as the 

most relevant for assessing the energy 

efficiency of production processes. 

These are: 

‐ Climate change 

- Photochemical ozone formation 

(impact on human health) 

- Acidification 

- Eutrophication (freshwater) 

- Water use 

- Resource use (minerals and metals) 

- Resource use (fossil fuels). 

The project also developed tools for the 

integrated assessment of life cycle costs 

and environmental impacts, including 

the LCC tool developed by VTT, described 

below. 

A NEW TOOL FOR ASSESSING 

LIFE CYCLE COST AND 

VISUALISING RESULTS 

The web-based DENiM LCC tool enables 

users to identify and evaluate the 

life cycle cost and cost savings associated 

with energy efficiency improvements 

in production processes and 

equipment. The tool supports companies 

in optimising energy efficiency 

from an economic perspective. The 

analysis includes several steps, integrated 

into the tool, such as the cost 

structure presented in Figure 1. The 

tool comprises the following modules: 


RESEARCH 

Life cycle cost (€, discounted / machine lifetime) 

‐ Management: Start a new LCC analysis 

or update an existing one. 

‐ Estimation basics: Define the production 

process, production assets 

and equipment, and LCC calculation 

parameters. 

- Costs and cost functions: Enter data 

for CAPEX, direct OPEX, productivity 

and material loss cost. 

- Outputs: View performance indicators 

and graphs, including life cycle 

cost, energy cost, annual cost and 

cost per machine hour. 

- Sensitivity analysis: Monte Carlobased 

simulation to assess the impact 

of uncertainty on costs and cost 

savings. 

Figure 2 presents an example of a graph 

comparing the life cycle costs of different 

production assets across three cost 

categories: CAPEX, OPEX, and productivity 

and material loss costs. 

PATHWAYS TO SUSTAINABILITY 

Innovative approaches to life cycle 

costing at both process and production 

asset levels, with an increased focus on 

material and productivity loss costs, are 

helping companies combine economic 

and environmental considerations 

more effectively. Enhancements in energy 

and material efficiency translate 

to cost savings and emission reductions. 

Energy efficiency measures can 

bring environmental benefits and cost 

savings through reduced energy consumption, 

reduced amount of waste, 

enhanced production efficiency, less 

rejected material and components and 

fewer defects. 

Figure 2. Life cycle costs by production resource. 

The new digital platform will enable the 

integration of smart energy solutions into 

production systems and business processes. 

For more information on the DENiM project, 

visit denim.fof.eu. The project has received 

funding from the European Union's H2020 

research and innovation programme under 

contract No 958339. 

VTT Headquarters in Espoo, Finland. 

VTT Technical Research Centre of Finland Ltd is a Finnish, fully state-owned limited 

liability company. The special duty of VTT as an independent and impartial research 

centre is to promote the wide-ranging utilisation and commercialisation of research 

and technology in commerce and society. 



In practice, about thirty 

to fifty percent of a 

technician's time is lost on 

tasks that have no added 

value or that could be 

performed by non (or less) 

technically-trained 

personnel. 



Should we treat our 

technicians like surgeons? 

And how can the concept of 

"Blue Boxing" help us out? 

It really is a challenge to find good technicians, hold on to them and expand the technical 

talent. This is the case today, and it will probably be the same in the years to come. 

Therefore you would expect that we properly support our technicians and provide an 

environment in which they can develop their technical talent to the fullest. But our 

actions often tell a different story. 

Text: Peter Decaigny, Partner, Mainnovation 

Photos: Mainnovation, Freepik 

In practice about thirty to fifty 

percent of a technician's time is 

lost on tasks that have no added 

value or that could be performed 

by non (or less) technically trained 

personnel. This varies from arranging 

the right spare parts, preparing the 

necessary tools, waiting for permits, 

collecting and guiding external contractors, 

obtaining the appropriate 

Personal Protective Equipment and 

performing tasks in pairs or threesomes 

when this is not really necessary. 

A SURGEONS TASK 

What is the deal here? The added 

value of professional Planning & 

Scheduling is often still misunderstood. 

Some people are convinced 

that only the technician can do the 

work preparation themselves. Others 

think that we are going to impoverish 

the job of a technician if all they do is 

tinker. 

Here the comparison with a surgeon 

in an operating room comes 

to mind. Do we ask the surgeon 

to prepare all the tools by him- or 

herself? Does he or she prepare the 

patient and, when the operation is 

Do we ask the 

surgeon to prepare all 

the tools by him- or 

herself. 

over, clean the operating room afterwards? 

I admit that the technician’s 

tasks versus those of a surgeon are 

a mile apart, but still there are comparisons 

to be made. A surgeon has 

to diagnose, operate and monitor the 

healing. He or she is most of the time 

relieved of all other tasks. You could 

say the surgeon is taking care of a 

special kind of asset: the human body. 

The technician on the other hand, 

is taking care of other complicated 

assets. Their focus must be on the 

correct technical analysis, the implementation 

of the preventive or 

corrective actions and the associated 

aftercare. Shouldn’t he or she be relieved 

of all other tasks? 

BLUE BOXING 

The concept of ‘Blue Boxing’ or ‘kitting’ 

consists of preparing containers 

for all plannable maintenance tasks 

that will be carried out in the coming 

period. These bins contain the spare 

parts, the work order, the adjustment 

instruction, the assembly sequence, 

the risk analysis and all other necessary 

agreements. 

We can also prepare specific tools 

that are not part of the standard 



Blue Boxing will not 

solve the scarcity of 

technical talent, but on the 

other hand, it can 

dramatically boost the 

efficiency of the 

maintenance executive 

team. 

equipment of every technician in a 

separate box. The boxes can be filled 

by the warehouse manager, the work 

preparer or by supporting staff. This 

can be executed during the ‘cheaper’ 

hours of the day, or this might be a 

smart way to use eventual idle time. 

For larger jobs and accompanying 

pieces, you can opt for palettes, but 

the concept remains the same. When 

all boxes or palettes are filled, a final 

check for completeness can take place 

and everything is ready for executing 

the engineering task. In this way, the 

technicians can take the box that corresponds 

to their assignment at the 

start of their shift and get to work immediately. 

Peter Decaigny, Partner, Mainnovation 

OPPORTUNITY MAINTENANCE 

This way of working also offers a 

chance for ‘opportunity maintenance’. 

What is it? Imagine we have 

prepared the boxes for a scheduled 

stop next week. A few days before the 

stop, there suddenly is a logistical 

problem with the supply of a crucial 

raw material. The logistics department 

is doing everything it can to 

speed up the delivery, but is already 

saying that we will be idle in a certain 

department for at least four hours. At 

that moment we can use the opportunity 

and start performing a number 

of maintenance tasks with a lead time 

of less than four hours. All boxes are 

ready and everyone can get started in 

a very short term. 

In this way we can usefully fill 

in the loss time, due to the logistics 

problem and we may be able to shorten 

or cancel the next planned stop for 

this department. By being maximally 

prepared, we can use these opportunities 

and this is why we call this ‘opportunity 

maintenance’. 

CONDITIONS 

This probably sounds like roses 

and moonshine. Like ‘too good to 

be true’. And indeed, it takes some 

time to arrange this and get used 

to this new way of working. Blue 

Boxing must be done thoughtfully 

and a number of clear agreements 

are needed. The boxes or pallets 

may under no circumstances be 

regarded as grab stock for (urgent) 

interventions. If boxes are looted, this 

immediately causes great frustration 

during execution of the prescripted 

task. Confidence in the approach 

disappears and we go back to the oldfashioned 

method. 

It is also important to make good 

agreements for the reintegration 

of unused parts and the return of 

parts that need to be repaired. When 

preparing the box, we often start 

from a ‘worst case scenario’. This 

means that we are preparing for the 

situation where we have to replace 

a complete kit of parts. In reality we 

may not need all the components and 

it is useful to reintegrate the unused 

parts back into the warehouse. 

Technically, there are various 

solutions to properly arrange this 

with the supporting EAM system. 

There are companies that regard 

the boxes or pallets as a virtual 

warehouse and only book the items 

when the actual consumption is 

known. But here too there are various 

possibilities. 

EFFICIENCY 

Blue Boxing will not solve the scarcity 

of technical talent, but on the other 

hand, it can dramatically boost the efficiency 

of the maintenance executive 

team. This allows more work to be 

done with the available team of technicians. 

Another positive side effect: 

it increases the satisfaction of the 

technicians. Searching for the right 

spare parts, finding the necessary 

tools, waiting for permits, looking for 

the right adjustment instruction et 

cetera are all sources of frustration. 

Perhaps the metaphor will help us 

see our technicians as surgeons, but 

without the green skirts of course. 


Be Vigilant over 

your critical assets 

24/7 ONLINE 

CONDITION 

MONITORING 

• ULTRASOUND 

• VIBRATION 

• TEMPERATURE 

• TACHOMETER 

• PROCESS


Long gone are the 

days of time-based 

lubrication 

Lubrication management is a cornerstone of industrial maintenance, encompassing a range of tasks 

far beyond the simple application of oil or grease to machines. It involves meticulous selection of 

lubricants, proper storage and filtration, vigilant monitoring of bearing health, and prevention of 

over- and under-lubrication. 

TEXT AND PHOTOS : SDT 

LUBExpert ON-GUARD installed on a machine to continuously check lubrication levels. 



In the pursuit of operational excellence, 

the importance of effective 

lubrication supervision cannot 

be overstated. Yet, despite its essential 

role in ensuring the longevity 

and optimum performance of machinery, 

lubrication practices often 

lack focus, leading to costly downtime 

and equipment failure. Fortunately, 

thanks to technological advances, 

new lubrication management tools 

have emerged, promising greater efficiency 

and reliability. In this regard, 

the LUBExpert ON-GUARD is a revolutionary 

solution that is about to improve 

automatic bearing lubrication 

and set new standards in reliability, 

simplicity and safety. 

UNDERSTANDING THE 

LUBRICATION CHALLENGE 

Inadequate lubrication can wreak 

havoc on machines, with incorrect replenishment 

rates and the wrong type 

of lubricant posing significant risks. 

Choosing the right lubricant isn't 

enough, you also need to ensure that 

the replenishment rate is correct. Too 

little grease accelerates wear, while 

too much leads to overheating and 

potential bearing failure. Striking a 

delicate balance is essential to maintaining 

optimum performance and 

extending equipment life. 

One of the most pressing challenges 

in lubrication management is 

over-lubrication, recognized by many 

experts as a ubiquitous problem in industrial 

plants worldwide. The consequences 

of excessive grease application 

are disastrous: heat generation, 

agitation and, ultimately, solidification, 

leading to clogging of the fresh 

lubricant and, with no surprise, bearing 

failure. Bearing failures, mainly 

due to lubrication problems, lead to 

unplanned downtime, hampering 

production and incurring significant 

costs. Clearly, meticulous lubrication 

practices are essential to the smooth 

running of industrial operations. 

Inadequate lubrication 

can wreak havoc on 

machines, with incorrect 

replenishment rates and the 

wrong type of lubricant 

posing significant risks. 

The LUBExpert ON-GUARD ensures that each bearing receives the right amount of grease 

at the right interval. 

Conventional approaches to lubrication, 

while seemingly logical, are 

often unreliable. Many technicians 

still adhere to time-based, preventive 

lubrication methods, administering 

grease at regular intervals. While this 

strategy aims to limit under-lubrication 

and the resulting malfunctions, 

it often overlooks the risks associated 

with over-lubrication, which can accelerate 

bearing deterioration. 

THE ROLE OF ULTRASOUND 

IN LUBRICATION 

Lubricating bearings using ultrasound 

has long been considered good practice, 

as it provides valuable information on 

friction levels and the quantities of 

grease required. Ultrasound is a reliable 

indicator of bearing health, enabling 

maintenance professionals to accurately 

assess the effectiveness of lubrication 

efforts. By assessing friction levels, 

ultrasound helps determine the precise 

amount of grease required, mitigating 

the risks associated with both over- and 

under-lubrication. 

INTRODUCING THE LUBEXPERT 

ON-GUARD 

The LUBExpert ON-GUARD represents 

a new step in maintenance 

technology, harnessing the power of 

ultrasound measurement to deliver 

targeted lubrication solutions with 

unrivalled precision. 

Long gone are the days of relying 

solely on time-based calculations, 

guesswork or manual intervention. 

With the LUBExpert ON-GUARD, the 

lubrication process is automated, ensuring 

that the right amount of grease 

is applied at precisely the right time. 

This device functions as a virtual "nutritionist" 

for machines, developing 

personalized, autonomous, data-driven 

maintenance plans that preventively 

address potential problems. 



Long gone are the days 

of relying solely on timebased 

calculations, 

guesswork or manual 

intervention. 

Imagine the peace of mind knowing 

that you no longer need to constantly 

visit each machine to ensure 

proper lubrication. With the LUBExpert 

ON-GUARD, the intricate task 

of bearing grease replenishment is 

seamlessly managed, eliminating the 

need for manual intervention. This 

not only reduces workload and minimizes 

grease waste but also guarantees 

optimal machine performance. 

But what makes this device a 

real added value that can change the 

whole of a plant's practices, is that its 

benefits are not limited to individual 

machines. It's actually a versatile 

solution capable of transforming entire 

plants and lubrication programs, 

instilling a culture of reliability and 

efficiency. 

Indeed, the LUBExpert ON- 

GUARD serves as a comprehensive 

solution for your entire plant and lubrication 

program. By automating lubrication 

tasks with precision, thanks 

Installing the LUBExpert ON-GUARD on all your machines means transforming 

your entire lubrication strategy. 

to the SDT LUBrain’s advanced algorithm, 

the device ensures that every 

machine receives the right amount of 

grease precisely when needed. 

The idea is not to improve the 

performance of individual machines. 

Rather, it's about revolutionizing 

your entire approach to maintenance. 

Implementing the LUBExpert ON- 

GUARD solution means a change of 

culture and methodology. No longer 

bound by traditional time-based 

practices, you'll be able to take condition 

monitoring and lubrication 

strategies to the next level. 

In other words, it's not just another 

set of components assembled and 

installed on machines. The LUBExpert 

ON-GUARD represents a complete 

solution to combat poor lubrication 

practices and promote reliability 

in your operations. It's an all-in-one 

package designed to establish new 

quality practices, where precision, efficiency 

and reliability come together 

to deliver unrivalled performance. 

KEY FEATURES AND BENEFITS 

• Reliability Through Condition-Based Lubrication. The LUBExpert ON-GUARD addresses the vulnerability 

of 80% of machine bearings prone to failure due to improper lubrication practices, ensuring consistent 

peak performance and eliminating downtime concerns. 

• Simple, Smart, and Automatic Operation. With its built-in web server and intuitive interface, the 

LUBExpert ON-GUARD simplifies maintenance tasks, allowing users to connect and control the device 

from anywhere. The SDT LUBrain algorithm automates lubrication, optimizing machine performance 

while minimizing workload and grease waste. 

• Safety and Flexibility. Prioritizing safety, the LUBExpert ON-GUARD offers remote grease replenishment 

capabilities, mitigating risks in hazardous environments. Its all-inclusive options empower users to 

tailor lubrication strategies to their specific needs, ensuring sustainable operations and data security. 

• Sustainability. The LUBExpert ON-GUARD offers significant advantages in two key areas: lubricant consumption 

and the use of electrical energy. By reducing over-lubrication, it cuts costs and environmental 

impact. Moreover, it extends bearing life, reducing the need for frequent replacement. On the energy front, 

it improves efficiency by reducing friction, resulting in significant electrical energy savings, particularly 

beneficial for large-scale operations. 



VA3 PRO EX 

INTRINSICALLY SAFE 

3-CHANNEL ANALYZER, DATA COLLECTOR, 

BALANCER, AND MUCH MORE ... 

MASTER THE LANGUAGE OF YOUR MACHINERY 


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 

YOUR 

PARTNER IN 

ULTRASOUND 

Free support & license-free software 

Online Courses 

Free access to our Learning Center 

(webinars, articles, tutorials) 

CONTACT US 

FOR AN ONSITE 

DEMONSTRATION 

UE SYSTEMS 

+31-546 725 125 

www.uesystems.eu 

info@uesystems.eu

Maintworld Magazine 3/2024

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