predictive maintenance digital twin EMERSON EDUARDO RODRIGUES

M.Sc. Thesis
Master of Science in Engineering
Informed Decision-Making: A Systematic Framework
Analysis for Implementing Predictive Maintenance
and Digital Twin
A case study with ProjectBinder
Edvinas Maciulis and
Linda Zviedre
Kongens Lyngby 2023

DTU Elektro
Department of Electrical Engineering
Technical University of Denmark
Ørsteds Plads
Building 343
2800 Kongens Lyngby, Denmark
Phone +45 4525 6352
https://electro.dtu.dk/

Summary
This Master Thesis report will present the two Industry 4.0 topics that are gaining
traction in the industry and academia, namely, Predictive Maintenance and Digital
Twins. A case from an industrial consultancy company, ProjectBinder, was proposed
to investigate the integration of these concepts. This paper is based on a qualitative
approach, analyzing data from interviews, case studies, and research papers. The thesis
presents a systematic literature study focused on the terminology, concepts, and use
cases. Further, the paper will delve into the analysis, uncovering the Life Sciences
market state, as well as presenting two work packages focusing on data-driven and
physics-based maintenance types, considering the integration of existing and available
frameworks of Digital Twins. Moreover, the discussion on implementation challenges
and considerations will be presented. Lastly, recommendations for ProjectBinder will
be proposed, followed by a conclusion on the topic.

Preface
This thesis has been prepared over five months at the Department of Electrical Engineer-
ing at the Technical University of Denmark, DTU, in fulfillment of acquiring the degree
Master of Science in Engineering, MSc Eng in Industrial Engineering and Management.
This thesis was written under the supervision of Dimitrios Papageorgiou (Assistant
Professor, Department of Electrical Engineering, Automation and Control), Seyed Mo-
hammad Asadzadeh (Assistant Professor, Department of Electrical Engineering, Au-
tomation and Control) and Poul Jorch Dehn Kristensen (Head of Digital Twin, Project
Binder A/S).
It is assumed that the reader has a basic knowledge and understanding in the areas
of Machine Learning, Digital Twins, and Predictive Maintenance.
Kongens Lyngby
July 9, 2023
E.Maciulis L.Zviedre
Edvinas Maciulis and
Linda Zviedre

Acknowledgements
We would like to take this opportunity to express our gratitude to all those who have
contributed to the successful completion of this thesis.
We want to thank our DTU supervisors, Dimitrios Papageorgiou and Seyed Mo-
hammad Asadzadeh for their continuous support throughout the Master thesis period,
guidance, and discussions. Your input has greatly impacted this work.
We would also like to thank our advisors from ProjectBinder, Poul Kristensen and
Alberto Martinez without whom this thesis would not be possible. The insight from
the industry has been thought-provoking and led to a lot of interesting discussions. On
top of that, the continuous support and guidance from CEO Martin Petersen has been
greatly appreciated.
To continue, we want to say special thank you to all the companies that have con-
tributed to the analysis of the market. Your input has provided a lot of compelling
insights into the industry and how much will be achieved in the nearest future.
Finally, we would like to express a special gratitude to our families and loved ones
for going the extra mile to be there for us when it was needed the most. Without your
support, this would not have been achievable.

Abbreviations
Abbreviation Full Name
AI Artificial Intelligence
AE Auto Encoder
AKSC Adaptive Kernel Spectral Clustering
CAD Computer-Aided Design
CNN Convolutional Neural Networks
CRA Cumulative Relative Accuracy
DT Digital Twin
DS Digital Shadow
ERP Enterprise Resource Planning
FMEA Failure Mode Effect Analysis
HMI Human Machine Interface
IFP Incipient Fault Point
IME Intrinsic Mode Energies
IoT Internet of Things
ISO International Organization for Standardization
IT Information Technology
KPIS Key Performance Indicator
LSTM Long Short-term Memory
MES Manufacturing Execution System
MSE Mean Squared Error
NASA National Aeronautics and Space Administration
OEE Overall Equipment Eﬀiciency
OEM Original Equipment Manufacturer
OT Operational Technology
PCA Principal Component Analysis
PLC Programmable Logic Circuit
RMSE Root Mean Squared Error
RNN Recurrent Neural Network
RTF Run-to-Failure
RUL Remaining Useful Life
SVM Support Vector Model

Contents
Summary i
Preface ii
Acknowledgements iii
Abbreviations iv
Contents v
1 Introduction 1
1.1 The client and the task . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Problem 4
2.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Motivation and Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Literature Review 6
3.1 Digital Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Maintenance in Industrial Settings . . . . . . . . . . . . . . . . . . . . . 12
3.3 Predictive Maintenance Types . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Digital Patterns for Maintenance . . . . . . . . . . . . . . . . . . . . . . 23
4 Methodology 25
4.1 Research methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5 Analysis 28
5.1 Market analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2 Moving towards Digital Twin* for Predictive Maintenance . . . . . . . . 33
5.3 Work Package I: Data-Driven Predictive Maintenance . . . . . . . . . . . 34
5.4 Work Package II: Physics-based Digital Twin* for Predictive Maintenance 46
5.5 Summary on Predictive Maintenance and Digital Twin* integration . . . 54

Contents vi
5.6 Predictive Maintenance and Digital Twin* cost evaluation . . . . . . . . 55
6 Discussion 58
7 Recommendations 61
8 Conclusion 62
A Framework for interviewing OEMs and clients using DTs/PdM 64
Bibliography 65

CHAPTER 1
Introduction
In today’s world of manufacturing, the advent of Industry 4.0 has changed the way
businesses operate, with advanced technologies being at the center of operations. Pre-
dictive Maintenance has emerged as one of the most important strategies of Industry
4.0, due to its essence of ensuring an uninterrupted production plan [4]. The reliance
on traditional maintenance approaches such as corrective or time-based maintenance
is gradually decreasing amongst companies, paving the way to Predictive Maintenance.
Predictive Maintenance, with the use of data-driven insights, helps operators forecast
failures and optimize the maintenance schedule.
To supplement the Predictive Maintenance capabilities, the concept of Digital Twin
can be investigated. The Digital Twin can provide a way for the operators to be more
informed about their assets and assist in enhancing the operations on the shop floor.
Combining the two concepts can offer to optimize the ways of working and minimize
unplanned downtime, adapting to the increasing customer demands and expectations.
Figure 1.1: Current Maintenance frontier (from [27]).
Figure 1.1 illustrates the current frontier of maintenance, with time-based mainte-
nance being at the forefront. However, with the increase of Industry 4.0 trends, com-
panies should make their efforts into moving towards Predictive Maintenance in order

1.1 The client and the task 2
to meet the increased production rates and avoid disruptions or even major failures of
the equipment. Therefore, it is important to explore the Predictive Maintenance inte-
gration with Digital Twin technologies to decrease the maintenance gap companies face
and facilitate more informed decision-making with better use of maintenance strategies.
This paper will investigate the use of Predictive Maintenance and Digital Twin
through the systematic literature and framework analysis from academia.
1.1 The client and the task
The client for this thesis is ProjectBinder ApS. ProjectBinder is an industrial consultancy
company specializing in the integration of industrial equipment & processes, operational
technology (OT), informational technology (IT), as well as data & the flow of digital
information [43]. The company offers competitive solutions to various customers, from
IT/OT security to digital commissioning for life science companies. With the exponen-
tially growing profit from 1,46 million DKK in 2018 to 18,42 million DKK in 2022 [42],
ProjectBinder has put itself in the market as a strong player, looking to extend its ex-
pertise in technology related to digital twins. The head of the Digital Twin team, Poul
Kristensen, has tasked the thesis team to investigate opportunities when it comes to
combining Predictive Maintenance and Digital Twins, as it is believed to be one of the
core goals many of ProjectBinder’s clients strive for. Furthermore, virtual commission-
ing software Emulate 3D has been provided to evaluate its applicability to the given
topic. Therefore, the task of this thesis will be to investigate the opportunities in com-
bining Predictive Maintenance and Digital Twins, as well as what companies must have
to qualify for the integration of the two aspects in their own industrial setting.
1.2 Objectives
Objectives:
• Analyze the current state of Predictive Maintenance in academia and the industry
• Analyze the current state of Digital Twin in academia and the industry
• Analyze current frameworks existing for different Predictive Maintenance types
and implementation with Digital twins
• Investigate prerequisites for Predictive Maintenance and Digital Twins
• Analyze implementation areas in which Digital Twins can contribute to Predictive
Maintenance modeling
• Investigate the feasibility of the two concept integration in an industrial scenario

1.3 Limitations 3
1.3 Limitations
As the concepts investigated are experiencing a swift development, certain limitations
become apparent during the research. Firstly, the data collection on equipment perfor-
mance is limited as the industry is focused on product quality monitoring rather than
equipment condition. Secondly, As Digital Twin is gaining importance in the industry
and ProjectBinder is believed to be at the forefront of it within Denmark, the main
contacts for industry and market analysis were provided by ProjectBinder. This can
potentially introduce a level of bias during the interview discussions about Digital Twin
and will be considered in the market analysis.
1.4 Thesis outline
The rest of the thesis will be structured as follows: Chapter 2 will present the problem
area and vision with this research, and Chapter 3 will dive into the current state of the art
for Predictive Maintenance and Digital Twins. Chapter 4 will outline the methodology
that has been used throughout the report timeline. Chapter 5 will present an in-depth
analysis of the industry along with the introduction of two Work Packages. Chapter
6 will discuss the two topics through their potential impact on the industry. Chapter
7 will outline the recommendations for the client based on the findings in the analysis
and insights from the discussion points. Lastly, Chapter 8 will present the conclusion,
together with the observations on this topic.

CHAPTER 2
Problem
With rising needs for better maintenance techniques, increasing data collection, and
growing application of Digital Twin, the lack of common knowledge and frameworks in
the industry creates a gap for the effective application of different concepts. Companies
tend to adopt their own ineffective solutions or trap themselves in limited knowledge
and limited understanding of how the technology should be implemented.
ProjectBinder is investigating possibilities for their Digital Twin team to offer capa-
bilities that would simulate the scenarios and predict the lifetime of a component or a
system. They envision it is possible through the use of Predictive Maintenance mod-
els. Therefore the investigation of Digital Twins and Predictive Maintenance becomes
important to explore.
Problems such as component selection, data collection, model creation, and tuning,
simulation of different operating conditions of the equipment, and accurate measurement
generation for system failure prediction have to be considered in a structured manner.
Since there are no standardized frameworks and use cases from the industry, it is
not clear if Digital Twin can be integrated with Predictive Maintenance models and
what are the functional areas on which one should focus when integrating both concepts.
Therefore a problem statement with additional research questions is proposed to explore
the topic.
2.1 Problem Statement
Can Digital Twin and Predictive Maintenance be integrated
to provide insight for more informed decision-making?
Additionally, three Research Questions are proposed:
RQ1: What is the market state in terms of Predictive Maintenance and Digital
Twin?
RQ2: What concept functional areas have to be considered for Data-driven Predic-
tive Maintenance and Digital Twin integration?
RQ3: What are the current frameworks and use cases for applying Digital Twin for
Physics-based Predictive Maintenance?

2.2 Motivation and Vision 5
2.2 Motivation and Vision
The motivation for exploring Digital Twin and Predictive Maintenance and beyond is
to understand current possibilities of implementing the two concepts, finding respec-
tive frameworks, and utilizing them to achieve more information- and simulation-driven
decision-making in terms of equipment lifetime and maintenance. The research is fo-
cused on familiarising the reader with the current possibilities of carrying out both
concepts, finding gaps of application in the industry and academia as well as identifying
solutions to these problems. The further aim is to quantify the economic benefit of
the concept integration as well as investigation towards change management and actual
implementation in the industry.

CHAPTER 3
Literature Review
The literature review was conducted during the initial phase of the thesis in three sec-
tions:
1. The history, development, and state-of-the-art advances in Digital Twins.
2. The state-of-the-art Predictive Maintenance implementation in industrial settings.
3. The Predictive Maintenance types, and their associated tools for execution.
4. The digital patterns of maintenance, discussing the terminology and implementa-
tion between the two concepts.
The following section of the report will describe each of the literature review topics
in-depth to show the current advances in the topic and present existing methods, ap-
proaches, and observations from various industries, from the railway to automotive and
aircraft applications.
3.1 Digital Twin
The concept of a Digital Twin has emerged, making companies think about how they
can create an environment that can assist their employees with simulation capabilities
and digitization of the manufacturing infrastructure. The concept of a Digital Twin can
be traced back to 2002, when a professor from the University of Michigan, Dr. Michael
Grieves presented the first replica of what we know today as a ”Digital Twin” at the
Society of Manufacturing Engineers conference. Back then it was described through a
Conceptual Ideal for PLM (Product Lifecycle Management - auth.), and consisted of a
real space, virtual space, and some linking mechanisms [18]. Michael Grieves referred to
this model as Mirrored Spaces Model (MSM).
Further, in 2006, Grieves referred to this concept as Information Mirroring Model.
He defined it as ”A model that consists of three elements: real space, virtual space(s) and
a linking mechanism, referred to as data and information/process connection between real
space and virtual space(s)” [17]. It was only in 2010 when NASA considered potential
advances in aerospace, that the term ”Digital Twin” saw the light in its technology
roadmap. It was defined as (...) an integrated multi-physics, multi-scale, probabilistic
simulation of a vehicle or system that uses the best available physical models, sensor
updates, fleet history, etc., to mirror the life of its flying twin [48]. Since then, both

3.1 Digital Twin 7
NASA and the original concept author, Michael Grieves, are using the term Digital Twin,
but the researchers still attempt to define the Digital Twin in the industry.
3.1.1 Definition of a Digital Twin
Many research papers addressing Digital Twin define it differently. It is important to
distinguish between several concepts that might be called the same, however, are cardi-
nally different. In his later research, Grieves defines the digital twin as a set of virtual
information constructs that fully describes a potential or actual physical manufactured
product from the micro atomic level to the macro geometrical level [19]. Such a definition
is quite broad and general, and does not provide a lot of context in terms of the environ-
ment it is applied in, the problem it tackles, or how similar concepts could contribute
to a digital twin in this definition.
Nevertheless, the Digital Twin term has been changing in the state-of-the-art context,
with several authors coming up with their own definitions. For example, Zheng et al.
define the Digital Twin as: (..) an integrated system that can simulate, monitor, calculate,
regulate, and control the system status and process [61]. Yakhni et al. in their research
define Digital Twin as: a virtual representation of a physical system containing all data
on site [57]. Madni et al. describe the Digital Twin as: a virtual instance of a physical
system (twin) that is continually updated with the latter’s performance, maintenance,
and health status data throughout the physical system’s life cycle [34].
The variety of definitions of a Digital Twin shows that there is no standardization
of what a Digital Twin entails. This leads to an observation of the fact that researchers
interpret the term Digital Twin based on the particular area or application type either
for the concept development or a specific case study.
3.1.2 Digital Twin Patterns
Tekinerdogan and Verdouw in their paper attempt to define several design patterns for
Digital Twins, the lifecycle stage a pattern can be implemented in, as well as the problem
it tackles and solution principles for the problem [53]. As the authors point out, a data
flow that is automatically synchronized between a digital and a physical object defines
a Digital Twin [53]. There might be instances when data flow is either fully manual or
partially manual, which can serve as a proof-of-concept in different stages of a product,
even though it is not a Digital Twin. Manual data flow refers to instances where there
is human input required to either tune the parameters on physical or digital entities
or even carry out a decision of maintenance. On the other hand, automatic data flow
would mean that human intervention is not necessary, for example, the data can be sent
from the physical system to the digital environment and vice versa. This type of data
flow distinction can be used to differentiate between Digital Twin patterns.
Therefore, authors have designed patterns for Digital Twin to cover a product’s entire
lifecycle. This means that in certain stages of the lifecycle, not all design patterns by
definition guarantee a complete Digital Twin. In particular, the authors differentiate

3.1 Digital Twin 8
four design patterns (Digital Model, Digital Generator, Digital Shadow, and Digital
Proxy) that are a partial Digital Twin, and three (Digital Monitor, Digital Control, and
Digital Autonomy) that can be considered complete Digital Twins. The definitions for
each have been combined in Table 3.1.
Design Pattern Description
Digital Model A manual dataflow flows both from physical object to digital object
and vice-versa. It can be treated as a first step into developing a
digital twin. Here, a client develops a physical object based on a
digital object.
Digital Generator Here, the digital object has an automated dataflow to the physical
object, yet the dataflow back to the digital object is still manual.
The DT serves as a blueprint for the automatic creation of the
physical object.
Digital Shadow Digital Shadow considers a physical object, based on which a dig-
ital twin can be created. The physical object must be equipped
with the relevant sensors to generate a digital twin of interest. The
dataflow then travels manually from the digital object back to the
physical object.
Digital Proxy As the name suggests, the digital twin serves as a proxy for the
physical object. Digital Twin in this pattern responds in the name
of the physical object [53]. This means that instead of the physical
object, the digital twin is being communicated to instead of the
physical object.
Digital Monitor The first design pattern that can be considered a full digital twin.
The digital twin oversees the physical object, either constantly over
time or over specified time intervals. The relationship between the
physical and digital objects is many-to-many, i.e., several digital
twins can oversee several physical objects.
Digital Control This pattern is a continuity of the Digital Monitor pattern. Apart
from monitoring the physical object, the digital twin can moreover
assign an action to the physical object and collects feedback data
in the digital twin.
Digital Autonomy This pattern is the most advanced one, due to its autonomy. It
can act without human touch, and the digital twin determines and
updates the parameters of interest, which means that it can learn
from the system itself.
Table 3.1: Overview of design patterns (adapted from [53]).
The three most commonly discussed Digital Twin patterns are the Digital Model,
Digital Shadow, and Digital Twin, among various types. It is, however, important, to
emphasize that the first two are not Digital Twin itself, due to its lack of automatic

3.1 Digital Twin 9
data flow. In Digital Model, the data flow is purely manual, and in Digital Shadow, the
automatic data flow is only unidirectional. The distinction between the three common
patterns is depicted in Figure 3.1.
Figure 3.1: Three common Digital Patterns used in research [28].
An example of a Digital Model could be a model of a gearbox, that has geometry,
material characteristics, and dimensions with which one can simulate scenarios. A Dig-
ital Generator produces data from the digital environment to create physical systems,
for example, in [53] it was used to automatically generate greenhouse production systems.
A Digital Shadow is achieved when there is a communication from, for example, a CNC
machine to the virtual object and it analyzes the incoming data to create patterns or
display Remaining Useful Life, however, the RUL is not fed back into the physical sys-
tem. As explained in Table 3.1, Digital Proxy will be the enabler of the physical system,
therefore avoiding the security risks of communicating directly with the physical system.
Lastly, Digital Monitor, Control, and Autonomy, depending on the application, is re-
quiring the least human input, since it could prescribe actions to the physical system,
all while receiving feedback from it.
In the analysis, particularly Digital Model, Digital Shadow, and Digital Twin will be
used as common terms when analyzing the opportunities within Predictive Maintenance.
This is due to the fact that other researchers commonly distinguish between the three
types of digital patterns, and other terms are not as widely adopted in the use cases.
3.1.3 Digital Twin characteristics
In the literature, a systematic review of Digital Twin characteristics has been written
by Jones et al. [24]. Here, the authors through more than 90 papers have generated a
list of terms that describe a Digital Twin. These characteristics are:
• Physical Entity/Twin
• Virtual Entity/Twin
• Physical Environment
• Virtual Environment
• State
• Metrology
• Realisation
• Twinning

3.1 Digital Twin 10
• Twinning Rate
• Physical-to-Virtual Connection/Twin-
ning
• Virtual-to-Physical Connection/Twin-
ning
• Physical Processes
• Virtual Processes
The overall theme in defining the Digital Twin’s characteristics is distinguishing
between the virtual and physical components of the twin. This means, that the Digital
Twin should not only have a physical environment and entity, as well as processes but
also a virtual version of it. The connection point between the two worlds happens at
Twinning when both are synchronized at a frequency or also known as Twinning Rate.
Both the virtual and physical entity of the Digital Twin can have their state, and it can
be measured (Metrology) or changed (Realisation).
Another important aspect worth mentioning is fidelity. Fidelity in this context can
be measured in the number of parameters that are being shared between physical and
virtual objects, as well as how accurate these parameters are. The more realistic, precise,
and complex with many parameters the Digital Twin is, the higher its fidelity. This
term should be useful when evaluating a case study and the requirements that need to
be satisfied for the client to have a Digital Twin.
3.1.4 Application of Digital Twin
One of the advantages of the Digital Twin is the flexibility and variety of applications
in which a Digital Twin can be used. The literature mentions several applications in the
manufacturing industry [1, 33, 49], aerospace [59, 48], automotive, and even healthcare
industry [32, 13, 26, 41].
The application purpose for the Digital Twin is to simulate various scenarios. De-
pending on the purpose of a Digital Twin, its fidelity, and what the Digital Twin is
trying to solve, there are many applications based on the lifecycle the pattern is applied
on. Pal et al. [39] have distinguished between three Digital Twin stages, namely, Design,
Manufacturing and Installation, and Service, where a Digital Twin can serve various pur-
poses. In the design stage, a Digital Twin can be useful for developing a product idea for
future production. In Manufacturing and Installation, Digital Twin can help to find the
optimal design of a product through, for example, CAD files on which various testing
and analysis (such as Finite Element Analysis) have been performed. Lastly, for the
service stage, authors emphasize the length of life of such Digital Twin being the longest
due to continuous performance measurement of the machine or process. This means
that the digital twin essentially is measuring how well (or poor) the performance of the
machine is, and how the end product might be affected in terms of quality, mechanical
properties, and structure.
The simulations from a Digital Twin can further create scenarios on the potential
selection of, for example, the design of a product or the optimal manufacturing process
for a product. This is also beneficial when companies have large systems in which

3.1 Digital Twin 11
significant financial investment has been made because it allows for the non-disruption
of existing operations through the simulation in a virtual entity.
3.1.5 Implementation of a Digital Twin
The implementation of a Digital Twin must follow a certain framework or a standard
that is defined for the industry. International Organization for Standardization (ISO) is
one such organization that develops standards for various topics based on market needs
[21]. While searching in the ISO archives for keywords Digital Twin, ISO currently has
three standards related to the topic, namely:
• ISO 23247: Automation systems and integration - Digital twin framework for
manufacturing (4 parts)
• ISO 30172: Digital Twin - Use cases (Under Development)
• ISO 30173: Digital Twin - Concepts and terminology (Under Development)
Since the use cases and terminology of Digital Twin are still under development from
the ISO side, we can only interpret these as seen in the literature. However, ISO 23247
is identifying a wide framework for how a Digital Twin should be represented graphically.
This is depicted in Figure 3.2.
Figure 3.2: ISO 23247 Digital Twin Framework for Manufacturing [22].
Nevertheless, since ISO has not yet defined clear terminology when it comes to Digital
Twin and its use cases, it is evident that researchers interpret the framework feasibility
based on the particular use case they are working on. This means that no standard
framework that could be extended to various use cases exists.

3.2 Maintenance in Industrial Settings 12
3.2 Maintenance in Industrial Settings
In today’s global world, where competition and customer demands are very high, stable
production, product quality, and an eﬀicient supply chain are vital for every company
[56]. To ensure that product quality is within the specifications and that unexpected
production stops and associated costs are prevented, maintenance plays a crucial role
in the manufacturing industry. There are several maintenance policies ranging from
ones that were commonly used for decades and new policies emerging from Industry 4.0
trends. Errandonea [14] has provided a thorough list of five main policy types used to
this day. The most maintenance types considered with regards to measurements taken
when applying them are visualised in Figure 3.3.
Figure 3.3: Maintenance types and measurement parameters [5] .
Firstly, run-to-failure, otherwise known as breakdown policy, is Reactive maintenance.
Companies following this type of policy fix the equipment after its failure. The costs
associated with equipment health monitoring are low as there are no investments required
by this policy, but long-term costs are high due to unexpected production stops, product
quality deviations, and the long time taken for fault detection and mitigation.
The second maintenance type known as preventive maintenance is aimed to prevent

failure before it actually happens. This is usually done at predetermined intervals set by
part or equipment suppliers, regardless of equipment health, thus over-maintaining the
asset. While this maintenance type assumes that the costs are reduced by preventing
downtime and equipment failure, the cost can actually increase due to frequent compo-
nent changes and unnecessary maintenance. Besides the cost, decision-making is based
on asset managers’ experience and gut feeling.
Due to the maturity of technology such as IoT, computation power, and cloud com-
puting, Condition-Based Maintenance could emerge. This type of maintenance antici-
pates a maintenance activity based on signal data of the degradation of an asset. Thresh-
olds are employed to detect anomalies in the asset and inform the asset responsible
regarding decision-making. With regards to cost, it requires companies to establish a
sensor system that is able to provide real-time measurements. Currently, the trend of
companies analyzing Operational Equipment Eﬀiciency (OEE) and utilizing IoT sensors
connected to equipment can be seen.
A recently emerged maintenance type called Predictive Maintenance has caught the
trend of Industry 4.0 [52]. This maintenance type is focused on an approach where
cyber-physical systems and sensors provide big data to monitor equipment health and
performance. This policy aims to provide engineers and operators with real-time ana-
lytics and predictions for equipment failure such as the Remaining Useful Life (RUL) of
an asset. In terms of costs, a high upfront investment is required. This is due to the
fact that the establishment of an equipment monitoring system as well as the hiring of
skilled data analysts is needed to reduce the frequency of preventive maintenance and
save costs on replacement parts. Furthermore, Predictive Maintenance can require a
high level of expertise and competence in identifying critical components and providing
robust machine-learning algorithms for data analysis. Lastly, a change management
approach has to be considered in establishing such systems and fostering a culture of
trusting such systems.
Lastly, as explained by PwC, the most recent maintenance policy is called Prescrip-
tive Maintenance [44]. This type of policy is based on Machine Learning and AI al-
gorithms to not only predict failure but also adjust the production rate and prescribe
maintenance policies. The main objective of this type of maintenance is to optimize
maintenance and increase production eﬀiciency by reducing downtime caused by ma-
chine breakdown and maintenance stops.
3.2.1 Current State of Predictive Maintenance
In 2011, the German government initiated industrial research agenda called Industry 4.0
[11]. Following the research initiative, many companies have initiated various digitiza-
tion projects. This has included automation, sensors, big data, analytics, and machine
learning. As part of machine learning, time-series analysis, and predictive modeling has
highly contributed to Predictive Maintenance method development in the industry.
With regards to Predictive Maintenance, in 2017 PwC performed a market analysis to
estimate the industry’s maturity in terms of maintenance and grouped it into 4 different

levels [44]. As indicated in Figure 3.4, Level 1 maturity includes visual inspections
performed periodically and based on inspectors’ expertise, around 27% of industry is at
this level. Level 2, which is the most common maturity level covering 36% of the industry,
adds experts’ judgment based on instrument read-outs. Level 3 excels by introducing
condition monitoring and alerts given by pre-established rules this way incorporating
experts’ knowledge into the system. Level three covers 22% of the industry. Lastly, only
11% of the industry is at Level 4, performing analytics and prediction based on assets’
historical and monitored data. This situation in the industry indicates that 89% has still
not reached the full potential of Big Data towards Predictive Maintenance. Furthermore,
it is important to note that the most mature industry observed in the market was the
railway followed by manufacturing and aerospace.
Figure 3.4: Maintenance Maturity Levels Towards Predictive Maintenance [44].
3.2.2 Critical Component identification
Before shifting from preventive maintenance and selecting the modeling technique for
Predictive Maintenance, one of the most important considerations that have to be taken
is critical component selection. Due to the fact that machine health monitoring equip-
ment is expensive and Predictive Maintenance algorithms require historical data, mon-
itoring all components in complex machinery is infeasible. This requires the machinery
responsible to evaluate the equipment and select the critical components that would
have a critical effect on the production lines. Furthermore, these components can have
different wearing factors and cause different failures in the machinery, so certain steps
have to be followed to identify what kind of component has to be selected for effective

Predictive Maintenance. For thorough critical component selection, [29] suggests using
Failure Mode Effect Analysis (FMEA) and the 4-quadrant method based on two axes:
failure frequency and failure consequence. The 4-quadrant method is presented in Figure
3.5.
Figure 3.5: 4-quadrant model for Critical Component selection. Remodeled from [29]
.
For the components identified by FMEA with a low consequence (downtime) and fre-
quency, a preventive maintenance policy should be applied by following Original Equip-
ment Manufacturer (OEM) suggestions. For high-frequency, low-consequence parts stor-
ing of suﬀicient amount of parts has to be ensured. For parts in high frequency and
consequence or only very high consequence quadrant, failures should be prevented at
all costs without taking any risks, and potential remodeling of the system should be
considered no matter the maintenance type. Lastly, the parts with moderate-to-high
frequencies and consequences can be considered potential candidates for Predictive Main-
tenance. There is potential to reduce maintenance costs by predicting the failure and
reducing the frequency of maintenance or minimizing the high impact on machinery
when the part fails.
Nevertheless, critical component selection must also reflect if the critical component
fits with other maintenance policies. As Tinga illustrates, in the maritime scenario where
components are changed on the shore together, they share a maintenance cost, and
replacing critical components at other time slots would dramatically increase its price

3.3 Predictive Maintenance Types 16
as it should share the single cost of maintenance [55]. Furthermore, when maintenance
is done only at certain time slots, the Predictive Maintenance model would not be
applicable.
As stated by Tiddens: Using prognostic methods to extend the component’s life-
time is only useful when the failure prediction actually enables reducing or extending
the maintenance intervals [54]. Therefore, after identifying the components by the 4-
quadrant method, the author proposes to consider factors to evaluate potential show-
stoppers. These showstoppers are critical components that do not qualify for Predictive
maintenance policy according to factors such as technical, economic, and organizational
feasibility. Examples can include poor failure monitoring systems, insuﬀicient financial
resources, or lack of trust in monitoring systems. Therefore, the components fit for the
Predictive Maintenance policy are the ones that have a critical impact on machinery but
are feasible to be monitored by current technology and expertise in the industry.
3.3 Predictive Maintenance Types
Once critical components are identified, three types of Predictive Maintenance can be
developed, these cover Data-driven, Physics-based, and Hybrid predictive maintenance
models [23]. This chapter will describe each maintenance type in detail.
3.3.1 Data-driven Predictive Maintenance
Research in data-driven predictive maintenance has resulted in constantly evolving ma-
chine learning algorithms and methods for Predictive Maintenance modeling. This type
of Predictive Maintenance modeling heavily relies on historical operational machinery
data and its failure symptoms with maintenance actions logged in the past. Due to
the fact that companies deploy preventive maintenance strategies, failure data is not
often an easy resource to access. Therefore, the advanced deep learning methods are
focused on the ability to abstract the complex problem and provide more accurate infor-
mation with existing input of failure data. As indicated in the taxonomy done by [45] for
most commonly used Predictive Maintenance approaches there are two main branches,
traditional Machine Learning, which includes:
1. Artificial Neural Network (ANN)
2. SVM (Support Vector Machine)
3. Decision trees (DT)
4. k-NN (k Nearest Neighbours)
and Deep Learning models including:
1. Auto Encoder (AE)

2. Convolutional Neural Networks (CNN)
3. Recurrent Neural Network (RNN)
4. Long Short-term Memory (LSTM)
Some of these machine learning models will be explained in more detail in Section
3.3.1.1.
In terms of frameworks applied to develop data-driven Predictive Maintenance mod-
els, it has been outlined by [46] that there is a lack of frameworks providing general
procedures. Therefore, as of now, the framework that different authors follow is best de-
picted by a reference model for data mining projects, CRISP-DM [20]. This framework
includes six steps that can be iterated and start with Business Understanding, followed
by Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment.
The framework with the steps is shown in Figure 3.6 below.
Figure 3.6: CRISP-DM Process [9].
Examples that followed the CRISP-DM framework for data-driven Predictive Main-
tenance include the development of AutoEncoder [60] and LSTM methods [15]. Nev-
ertheless, as outlined by Nunes, the data-driven approach is reliant on collected data

which can limit the model to predict the RUL based on unknown operating conditions
[37]. Therefore, the collected data can be used to create and verify physics-based models
which will be discussed in the further sections.
3.3.1.1 Machine Learning models for data-driven Predictive
Maintenance
As indicated in the taxonomy, each of the aforementioned machine learning models
can be applied for Predictive Maintenance purposes. The models selected below are
presented in more detail as they will be discussed in the analysis sections. The machine
learning models for Predictive Maintenance further discussed in this report will be:
1. CNN
2. SVM
3. LSTM
4. AE
3.3.1.2 Convolutional Neural Network
Convolutional Neural Network (CNN) is a supervised deep learning model known for
shared weights and the ability of local field representation. CNN is used in image
analysis as well as effective audio, time series, and signal data classification. CNN works
on pattern recognition and local feature extraction of the input data and combines them
layer by layer resulting in high-level features. As shown in 3.7, CNN structure consists
of an input layer, convolution layer, pooling layer, and fully connected layer followed by
classification or regression layer. The layers are explained in more detail below.
Figure 3.7: Schematic diagram of a basic convolutional neural network (CNN) archi-
tecture [40].

Input layer: The input layer can be exhibited either in a one-dimensional manner
such as time-series data or a two-dimensional manner such as time-frequency spectrum.
The input data is the data acquired from machinery, for example, vibration data.
Convolution layer: In the convolution layer, the input data is convoluted through
the convolution filter through a set of weights and composes a feature output, generally
called a feature map.
Pooling layer: The main operation of the pooling layer is sampling, which is used
to keep the effective information while the model parameters are reduced. In this case,
training speed can be increased while the risk of overfitting can be reduced.
Fully connected layer: Once several combination forms of convolution layer and
pooling layer are made, fully-connected layers follow. These layers can convert the
matrix in a filter to a column or a row.
Output layer: Final layer in which regression or classification values are derived. In
the field of Predictive Maintenance, CNN has shown dramatic capability in extracting
useful and robust features from monitoring data. Nevertheless, CNN is often combined
with different signal processing algorithms for higher accuracy in fault diagnosis. Al-
though 1D CNN requires a limited amount of data for effectively training applications,
it has shown a low feature extraction capability for different sensor data in 1D format.
To address this issue, different authors have investigated ways to automatically convert
the sensor data into a 2D matrix, such as a time-frequency matrix. This has resulted in
better classification performance compared to some other machine learning algorithms
[45].
3.3.1.3 Auto-Encoder
Auto-Encoder (AE) is an unsupervised neural network model that maps input data into
a compressed layer which is subsequently decoded into the most representative version
of the input data. This way, the AE is able to extract the most important features
needed to represent the input data. As illustrated in Figure 3.8, AE consists of three
main types of layers, the input layer, one or more hidden layers also called bottleneck,
and the output layer.

Figure 3.8: Structure of a typical Auto-Encoder Network [6].
As illustrated in the AE structure, the input data is transformed by the encoder to a
hidden representation by a non-linear activation function. Then, the decoder is used to
map the representation back to the original form. The AE parameters are optimized to
result in minimized reconstruction error between the output and the input. In case the
input data is highly nonlinear, more hidden layers have to be used in order to construct
the deep AE.
In terms of Predictive maintenance, AE and its deep models have been shown to
learn well the high-level representation from raw data. Researchers have applied AE in
terms of automatic feature extraction from raw and highly dimensional bearing vibration
signals. Nevertheless, as with any machine learning model, high dimensional raw data
can lead to heavy computation costs and overfitting. Therefore, multi-domain features
can be extracted first from raw data and then fed to AE-based models. The research
in this area has focused on the frequency spectrum of the vibration signals as well as
multi-features extracted by time domain, frequency domain, and time-frequency domain
analysis.
With limited historical failure data, AE-based models work well with the degradation
process estimation. As seen from academia, these approaches are able to measure the
distance between the states and distinguish healthy operation and degradation as well as
different degrees of fault severity. For instance, by the use of kernel density estimation.
The labeled failure data in this case is not heavily needed due to Auto Encoders’ ability to
learn on operational data and learn the underlying patterns themselves. Furthermore,
it has been observed that AE-based models can be combined with various regression
models for RUL prediction of machinery equipment [45].

3.3.1.4 Support Vector Machine
Support Vector Machine (SVM) is a supervised machine learning modeling technique
that is deployed when the process of the system is unknown. SVM can also be used
when mathematical relation, such as degradation, is too computationally and practically
expensive to be obtained, mostly caused by the high number of affecting factors. SVM is
usually deployed for classification tasks where the data is assumed to be divided into two
classes namely positive and negative. In this case, SVM builds a model that classifies new
data to any of the two categories, making it a non-probabilistic binary linear classifier.
For this purpose, Hyperplane is created with the aim to create a maximum margin
between the two classes. The SVM working principle is indicated in Figure 3.9.
Figure 3.9: Structure of a Support Vector Machine [51].
Because of its high classification accuracy, including non-linear problems, SVM has
shown good accuracy in different applications ranging through image recognition, verifi-
cation, and machine fault diagnosis. For regression problems, Support Vector Regression
(SVR) is used in approaches like fault prognosis. Support Vector Regression (SVR) is
based on the same work as SVM, but instead of increasing the margins between the
classes, SVR is aimed to find a hyperplane that results in the least amount of error.
In contrast to some other deep learning models, SVM/SVR requires a selection of the
relevant features that could contribute to failure and abnormalities.
3.3.1.5 Recurrent Neural Networks and Long Short-Term Memory)
Recurrent Neural Networks (RNNs) are a group of supervised neural networks deployed
for processing sequential data. RNN can be used to build cycle connections among

its hidden units and use its previous inputs to influence the new data coming into the
network.
Figure 3.10: Structure of a Long Short-Term Memory [7].
As shown in Figure 3.10, the network consists of an Input layer, Recurrent layer,
and Output layer, where the recurrent layer functions as a main part of the network.
In this layer, the sequential data is processed and the hidden state is updated with the
trends seen from past data. Therefore, the main advantage of RNNs is the possibility
of capturing long-term dependencies and patterns seen in the input data. Nevertheless,
RNNs can be affected by the vanishing or exploding gradient problem, which limits the
capture of long-term dependencies effectively. This limitation can be overcome by using
more advanced RNN architectures, like LSTM (Long Short-Term Memory).
LSTM has introduced a new structure of RNN with a memory cell and three main
parts: the input gate, the forget gate, and the output gate.
Memory Cell: The memory cell serves as a storage unit that can retain or forget
information over time. This cell is used for maintaining long-term dependencies in the
sequence.
Input Gate: The input gate is for data input and selection of how much new data
is added to the memory cell at the current time step. For new input, it considers the
previous state and the current input and by the use of the sigmoid activation function
passes ranges them to values between 0 and 1. This gate manages the update of the
previously mentioned memory cell.
Forget Gate: As the name suggests, the forget gate is used to decide which infor-
mation should be removed from the memory cell. As the input gate, it uses the current
input along with the previous hidden state and applies a sigmoid activation to determine
how much of the information of the previous memory cell should be forgotten.
Output Gate: Based on the previous gates, the output gate controls the information
that is generated from the memory cell, resulting in the final hidden state of the LSTM
cell.

3.4 Digital Patterns for Maintenance 23
Due to its unique structure with different gates, the LSTM model shows one of the
best capabilities for Predictive Maintenance purposes [45]. Firstly, due to the memory
cell implementation in its structure, the model is capable of memorizing the long-term
dependency and modeling it with given data. This makes LSTM one of the most com-
monly used models when dealing with time-dependent data. The popularity of LSTM
models also drives research in this area at a very fast pace. Furthermore, LSTM mod-
els can deal with sequential data with varying sizes, this allows the model to capture
the whole sequence and adapt its memory cell. Lastly, in terms of real-time prediction,
LSTM models can make predictions in real-time, due to the fact that LSTM models can
update their internal state, the predictions can be adjusted if there are changes in the
environment.
3.3.2 Physics-based Predictive Maintenance
As the naming suggests, Physics-based Predictive Maintenance is derived from modeling
the behavior and operational conditions of the selected system in terms of mathematical
and physical processes. Furthermore, the degradation phenomenon is included by deriv-
ing it from collected test and operational data. This way different operating profiles can
be selected to estimate the RUL for the selected system.
In terms of frameworks, it has been identified that no general framework for Physics-
based Predictive Maintenance has been established. As confirmed by Aivaliotis et al., no
easily adaptable procedure exists for the creation of physics-based models for different
assets of equipment [3]. Therefore, authors doing research in this field had to develop
their own frameworks for physics-based Predictive Maintenance. The most common
examples of it include Robot arm modeling in OpenModelica [1], Filter clogging in
Matlab [12], and fan/motor system in Simulink [57].
3.3.3 Hybrid models for Predictive maintenance
Lastly, researchers working on Predictive Maintenance hybrid models have observed
that results given by Data-driven Predictive Maintenance models are not realistic in
the early stages of component wear and that certain tuning is needed. This tuning has
been introduced by merging the two concepts above and delivering Hybrid Predictive
Maintenance Models. The advantages of this modeling technique have been shown by
[33], where a framework combined both data-driven and physics-based models to achieve
timely and more accurate results of the CNC machine tool. In order to verify that such a
combination of models always delivers better results, more research has to be performed.
3.4 Digital Patterns for Maintenance
As Digital Twins offer real-time connection and data gathering of the physical asset,
research towards Digital patterns of physical systems for Predictive Maintenance inte-

3.4 Digital Patterns for Maintenance 24
gration has been apparent in both industry and academia. Nevertheless, the lack of
use cases and structured terminology for these Digital patterns has resulted in a mis-
conception of the term Digital Twin and its application in different industries, causing
the understanding of Digital patterns and Digital Twin to be vague. Therefore, the
terminology and concepts of Digital Twin have to be covered in a maintenance context
as well.
Firstly, as Kritzinger et al. proposed, Digital Model, Shadow, and Twin concepts
can be used to distinguish between the maturity of the Digital pattern and the data
flow [28]. Therefore, certain trends of Digital Twin being used as an umbrella term can
appear. These were clearly indicated by [28], where most of the Digital Twins named in
the articles were only mature to the level of Digital Shadow or a Model.
In the context of maintenance, Errandonea et al. have followed the same classification
as Kritzinger for maintenance types [14].
As mentioned in the definition, Digital Twin requires an automated data flow, how-
ever, Predictive Maintenance is able to provide a prediction but still requires the operator
to take manual decisions affecting the physical entity. Therefore the two concepts are
not to be combined according to the definition. This means that Digital Shadow has to
be considered in the context of Predictive Maintenance as the automatic data flow from
physical to virtual entity is enabled but not vice versa. This definition of Digital Shadow
is also fit for Condition-Based monitoring, but it has to be noted that the fidelity of this
Shadow is lower since the amount of data provided is less. Lastly, since Reactive main-
tenance is based on no monitoring of the asset and Preventive maintenance is based on
expertise and OEM-defined limits, there is no automatic data flow in these maintenance
types. This means that preventive and reactive maintenance can be represented at best
by the Digital Model.
Since maintenance is done during the service lifecycle of an asset, the Digital Twin
considered falls in this life cycle as well. To address the terminology used in academia,
where concepts such as Digital Model or Digital Shadow are sometimes referred to as
Digital Twin, the term Digital Twin* as an umbrella term will be used in the rest of this
report. This notation will bridge the conceptual gap between these terms throughout
the report. When referring to Digital Twin (DT) as a term, a full Digital Twin with
automated data flow will be considered.

CHAPTER 4
Methodology
The methodology section will discuss the methods applied in this research and outline the
procedure to perform an analysis of the topic of the thesis. It was chosen to apply various
methods throughout the different stages of the thesis, in order to ensure a structured
approach and generate comprehensive results in analysis.
The following subsections provide an overview of the research methods, data collec-
tion, and data analysis.
4.1 Research methods
The goal of the literature research strategy was to identify relevant state-of-the-art lit-
erature on Digital Twins, Predictive Maintenance, as well as research whether there
are existing frameworks for combining the two concepts. A comprehensive search of re-
search papers, online databases, and websites was conducted, using two sources: Library
Services Platform, DTU Findit, and Google Scholar. The keywords included:
• Digital Twin
• Predictive Maintenance
• Digital Twin for Predictive Maintenance
• Remaining Useful Life (RUL) prediction
• Condition-Based Monitoring
• and others
Some of the keywords were combined using Boolean operators (AND, OR) to increase
the accuracy of the search.
First, papers on Predictive Maintenance and other maintenance types within the
last 10 years were selected for analysis. Then, Digital Twin-related papers were selected
for further understanding of the concepts. Lastly, understanding relevant terms of the
two, such as RUL prediction, Machine Learning (ML) algorithms, were searched for.
The papers that qualified for most relevance and modernity were further analyzed and
structured, using software, such as Microsoft Word and Mendeley. Microsoft Word
served as a tool to structure the notes of all papers in one place, where the title, keywords,

4.2 Data collection 26
and most relevant information was extracted, whereas Mendeley software was used to
combine all bibliography-related information for further referencing in the thesis.
Apart from the literature review, several other methods had to be used for further
confirmation of our assumptions. That includes searching for publicly available data sets
for supplementing the literature review and reinforcing our assumptions that shaped the
direction of the thesis.
4.2 Data collection
For further support of the thesis topic, a combination of primary and secondary data
collection methods was used. The primary data collection involved conducting interviews
with the clients of ProjectBinder as well as companies that are interested in pursuing
Industry 4.0 technologies as part of their business strategy. The secondary data collection
consisted of gathering information from existing literature as well as publicly available
data from various online sources.
The primary data collection process involved conducting interviews with key stake-
holders coming from the industry with many years of experience in engineering, software
development as well as sales and marketing. The interviews were conducted through an
online platform MS Teams. To ensure that it was possible to gather the most relevant
information for the research and comparison between the sources, but also to allow the
interviewee to express their opinions in a relaxed environment, semi-structured inter-
views were created. The framework for interviewing ProjectBinder clients can be found
in Appendix A. This allowed us to stay on track within the allocated time, as well as
gather firsthand information, while at the same time allowing the interviewee to provide
their own input on the topic. While conducting the interviews, it was the responsibility
of one person to keep the conversation, ask relevant questions as well as use the answers
to further comment on the topic. The other person was responsible for transcribing
the interview in real-time and making sure all the relevant information was noted down.
The most relevant information was further noted down into categories relevant to main-
tenance and Digital Twins, as well as business perspective. The results were discussed
between the authors and a conclusion was made based on reading all the transcriptions.
The publicly available data from online sources was used to gain insights into the
current research being done on Predictive Maintenance and Digital Twin. Platforms
such as Kaggle and GitHub were accessed to look into the relevant datasets and algo-
rithms used for developing a predictive model. On the other hand, for exploring the
Digital pattern representation of a system, COMSOL online documentation and Mat-
Lab/MATHWORKS online repositories were used to gain a better understanding of
balancing the model fidelity and computational time for it with the mathematical mod-
els involved. This secondary data allowed to capture a broader perspective while not
being entirely biased toward ProjectBinder’s clients’ opinions.

4.3 Data analysis 27
4.3 Data analysis
Once the data collection was completed, an analysis process could be initiated. The
overall goal for the data analysis was to identify similarities between the responses of
stakeholders as well as to see the relationship between the observations of ProjectBinder
and the research done by academia. Moreover, the pain points within the industry
were attempted to identify for further investigation purposes. This way the trends
throughout the interviews with clients and academic insights could be combined and
further explored. It was also important to see whether the argumentation is supported
in academia, therefore the previously mentioned literature notes were brought back. The
aim of the data analysis was to arrive at a conclusion on the current state of Predictive
Maintenance and Digital Twin, which further would help in proposing the next actions
for ProjectBinder.

CHAPTER 5
Analysis
This section will present the analysis of current Predictive Maintenance frameworks of
data-driven and physics-based maintenance types. The analysis will focus on Digital
Twin∗
integration with the Maintenance concepts. Additionally, market readiness anal-
ysis is presented as well as Predictive Maintenance and Digital Twin integration cost
analysis.
5.1 Market analysis
As part of this report, market analysis is presented to introduce the reader to the current
analysed market state in terms of Predictive Maintenance and Digital Twin*. During the
thesis period, 14 relevant industrial companies were reached out to, and out of them, 2
Component Manufacturers, 2 OEMs, 2 End Users, 1 IoT hardware and software provider
as well as 1 physics-based modeling software provider were interviewed. As explained in
the Data analysis section, a set of questions was prepared to evaluate and later compare
the market state in terms of Digital Twin and Predictive maintenance. The analysis
below will deep-dive into each interviewed market sector.
5.1.1 Component Manufacturers
As the component manufacturers are the ones manufacturing the critical components
and running tests to set the lifetime of the components and their warranty, they were
interviewed to see if any of the knowledge gained during the development and manufac-
turing of the component is used to develop Predictive Maintenance and Digital Twin*
models. Two companies interviewed were bearing manufacturer SKF and gearbox man-
ufacturer Wittenstein.
5.1.1.1 SKF
SKF is one of the biggest bearing suppliers in the world. Therefore, it was important
to reach out to the company in order to understand more about the currently available
models of SKF bearing lifetime estimation as SKF has a bearing lifetime calculator
∗
Umbrella term

5.1 Market analysis 29
available on its homepage [50]. SKF Sales Representative Carsten Harreby has been
interviewed for this purpose.
As explained by Carsten, the current state in SKF Bearing lifetime estimation is
based only on a few Run-to-Failure tests, and some assumptions of physics are incor-
porated in the models. A few factors such as Load, Speed, Bearing Size, Lubrication,
and Temperature are available in the calculator for RUL prediction. Nevertheless, each
factor has a different impact on the bearing lifetime, and the influence of the factors
is not clearly defined. Furthermore, static factors such as seal and oil type had a big-
ger influence than temperature, which, out of all factors, had the lowest impact on the
bearing lifetime. The modeling technique, whether machine learning or physics-based,
was not disclosed. Moreover, the calculator provides an accurate RUL prediction for
approximately 10% of the cases, meaning that with 90% certainty, the bearing might
have a longer RUL than denoted in the SKF calculator. In the context of Predictive
Maintenance this is not certain enough as the real RUL of most common cases cannot
be estimated accurately. As it was later discussed with Carsten, bearings can have
various defects affecting their inner or outer components. Creating models for accurate
failure classification and prediction requires a huge amount of data collected on bearing
conditions and different operating conditions. This is currently not feasible nor in the
long-term plans by SKF to execute, therefore research in this area can be expected to
be rather slow.
5.1.1.2 Wittenstein
The second component manufacturer that was interviewed was Wittenstein. Wittenstein
is working in the industry of gearing and servo motors and recently has investigated the
possibility of collecting Run-to-Failure data on some of its gearboxes to develop models
for anomaly detection and ideally - Predictive Maintenance. In total, the company has
performed 8 Run-to-Failure tests, setting the motors to run at constant speeds forward
and backwards, measuring the vibration signal every 10 seconds. After running the
motors for 2 weeks, this resulted in 500.000 data points and 730 features requiring file
storage of several terabytes. During the testing, the temperature increase in the inner
motor was classified as a failure, while the motor was still running.
As discussed with Wittenstein, after the Run-to-Failure tests were executed, a few
things became apparent. Firstly, the test has generated a substantial amount of data,
making the storage and computation of the data a challenge. Secondly, the tests were run
on static operating conditions, which led to the realization that data-driven Predictive
Maintenance models cannot be developed as the gearboxes are run in different conditions
and environments in the industry. Lastly, gearboxes in the industry are maintained with
strict intervals, limiting data collection on failure mechanisms, meaning that Wittenstein
can collect data only from Run-to-Failure testing.
As disclosed by Wittenstein, these Run-to-Failure tests have shown the company
that deployment of Predictive Maintenance models is still in the far future and that the
end users will always prioritize stable and well-run machinery. Therefore, the current

plans of Wittenstein are to continue exploring the anomaly detection area for its motor
performance.
5.1.2 OEM
Original Equipment Manufacturers (OEMs) were the next industry players to interview
as they were the ones manufacturing the equipment and assembling the components for
the end users. Two companies interviewed were Qubiqa and PJM.
5.1.2.1 Qubiqa
Qubiqa is a handling machinery provider for different industry areas. The company has
been in the industry for more than 75 years, offering innovative automation solutions.
The head of automation, Thomas Jørgensen, has been able to participate in the market
study on behalf of Qubiqa.
In terms of Predictive Maintenance, the maintenance concept was well known to
Qubiqa, but no actual use cases have been seen in-house. As indicated by Thomas, this
is due to the fact that the concept is sold by the sales department and the final result
is usually very complex and costly. Therefore, Qubiqa has focused on the principles of
Predictive Maintenance and developed a solution for Condition-based monitoring for one
of its customers. The solution included sensor equipment that was able to store the data
in the cloud. Once the data was available for the customer, certain condition monitoring
algorithms and thresholds were established to notify the customer of equipment status.
In terms of Qubiqa machinery maintenance, a Preventive maintenance policy is ap-
plied. This is due to a few main factors. Firstly, the next step in maintenance, like
Condition Based Monitoring requires organizational change, the costs of introducing
and maintaining the sensor equipment overweight the component breakage costs and
the competence in terms of data analysis by end users is missing. Therefore, considera-
tions towards offering services of data analysis are currently in process by Qubiqa, since
the manufacturers of the equipment have more knowledge about machinery mechanics
and working principles. Another important factor noted from the interview was that
no data is collected during machinery development and testing. The machinery goes
through Factory Accelerated Testing (FAT), but only data such as alarms from PLC
can be harvested, as no real sensors are used for machinery condition monitoring.
In terms of Digital Twin*, virtual commissioning has been performed on the machin-
ery to save time on PLC code evaluation. A real Digital Twin with bi-directional data
flow is in consideration but a few challenges are still apparent. Firstly, Qubiqa needs to
know what information is needed for the end-user to monitor constantly. This leads to
the second challenge, having the sensors and capabilities to monitor such data. Lastly,
a lot of maintenance practice is gained through experience and that experience has to
be reflected in Digital Twin.
Finally, in terms of Digital Twin* and Predictive Maintenance future, Qubiqa indi-
cated that a full solution of those concepts has to be offered if the supplier wants the

solution to work in the industry. Due to a lack of expertise and large organizational
change needed, end users prefer simple solutions that would require low maintenance
thus requiring the supplier to consider how such OT/IT solutions can be integrated,
simplified, and made affordable.
5.1.2.2 PJM
PJM is a specialized machinery and advanced automation solution provider for different
industry areas. The company has been in the industry for more than 60 years offering
its services. Innovation Manager Jakob Nors has been able to participate in the market
study on behalf of PJM.
When it comes to maintenance, PJM relies on its technical department experience
and defines a maintenance plan for the end user. This maintenance plan follows set
intervals and is fully preventive. A service department is established for consultation
and immediate maintenance decisions in case of earlier or unexpected component breaks.
With regards to Predictive Maintenance, PJM has not investigated this possibility as
they are not able to generalize the data that would be harvested from different machinery.
Collaboration with other companies in terms of data analysis is done with a focus on
condition monitoring and early alarm system.
In terms of Digital Twin*, only virtual commissioning and graphical representation
are utilized. Interest in industrial physics is apparent but due to a lack of skill in the
company, this, as well as the proper establishment of Digital Twin, is not being realized.
Lastly, as indicated by PJM, use cases and pilot projects are needed to evaluate the
costs and feasibility of Digital Twin and Predictive Maintenance. This is due to the fact
that sensors and data logging as well as analysis require resources and critical component
breakage has to overcome the cost of these resources.
5.1.3 End Users
To understand the investigations done by the End Users, two companies were interviewed
as part of the market analysis. The companies interviewed were major performers from
the pharmaceutical manufacturing and shipping industries.
In terms of maintenance, both companies have started their investigation on Predic-
tive Maintenance. Pharmaceutical manufacturer initiated the Predictive Maintenance
project 2 years ago for rubber sealing degradation monitoring, but due to a lack of
knowledge on critical component selection and sensor mounting, high complexity, and
costs, the project did not reach its full potential and was terminated prematurely. As
indicated by the company representative, experience in handling the machine was not
enough to properly establish the sensor equipment and perform data analytics. There-
fore, the company has postponed Predictive Maintenance by 5 years and initiated a
Condition-Based Monitoring project with Siemens Senseye solution for monitoring the
sealing conditions and creating different thresholds for the operating conditions.

The shipping company, on the other hand, has taken a different approach by laying
the foundation for Predictive Maintenance. Experience-sharing and condition-tracking
platforms have been established by automation engineers. The aim of the platforms is to
gather the failure symptoms of different equipment to later focus on what kind of data
has to be collected for condition-monitoring as well as tracking the status of currently
used parts in three vessels. The estimation of component working hours comes from
a combination of experience and manufacturing data, and the thresholds are created
based on the logistics and availability of spare parts.
In terms of Digital Twin*, both companies experience growing interest in the topic
and many different departments can be seen to introduce this term into their daily lives.
Nevertheless, currently, no real Digital Twin has been established as the companies
are carefully investing their resources in this topic. It can also be observed that the
management of the companies wants to see the financial performance of such a solution
and the financial benefits it might bring to the table, therefore investigation between
technical and business aspects is necessary.
5.1.4 IoT - Software/Data analysts
To get more insight into companies that are providing IoT and data harvesting solutions,
Anders Meister from CIM.AS - IoT for Pharma solutions was interviewed.
From an IoT software and data harvesting perspective, Anders has indicated multi-
ple factors affecting the data availability for Predictive Maintenance and Digital Twin*
enabling. Firstly, due to the fact that legacy systems are still being used in production
facilities, data extraction is very limited, and only now, with the help of IoT solutions,
the data is becoming more accessible. This has shown that end users are lacking the ca-
pabilities to investigate further into the data from production. Secondly, as the software
and equipment provided by CIM.AS is used for process and machinery condition moni-
toring, only descriptive analytics and to some degree diagnostic analytics are currently
available. This means that predictive and prescriptive analytics are still not available
due to limitations in know-how and data analytics. Currently, with this type of solution
implemented in the industry, the companies can answer questions of ”what happened”
and sometimes ”why did it happen”. Nevertheless, questions of ”what will happen” are
still far from being answered. Lastly, for successful Digital Twin implementation, change
management from top management has to be initiated to fully enable the benefits of
Digital Twin. This is due to the fact that Digital Twin requires data from PLC levels
up to MES and ERP levels.
5.1.5 Software providers - Ansys and EDR Medeso
As physics-based Predictive Maintenance modeling has been investigated, an interview
with physics-based modeling software Ansys and EDR Medeso representative Frode
Halvorsen has been carried out regarding Ansys Twin Builder.

5.2 Moving towards Digital Twin* for Predictive Maintenance 33
As indicated by Frode, all companies face the issue of having insuﬀicient failure data
while those doing only simulation-based modeling often miss simulation accuracy, as the
field insights are not calibrated with the field data. Furthermore, companies focus on
simple critical components as modeling competencies are not yet high. Therefore, the
solution provided by Ansys is focused on combining physics, sensor data, and engineering
knowledge with an aim to provide informed decisions further in the maintenance. To
reduce the issue of missing failure data, a partner of Ansys, EDR Medeso has gathered
data of bearings and shafts to supply Ansys with RUL modeling. As indicated by Frode,
95% of machine failures come from bearings and shafts, therefore, the company was able
to collect a suﬀicient amount of data to supply the software with valuable insights.
Nevertheless, as discussed in the interview, cases of applying the solutions are small
and competence building, as well as interest from the End Users, is still low. This
indicates that the industry is still not fully mature to take the steps towards Predictive
Maintenance models.
5.1.6 Market analysis - summary
From market analysis few things become apparent. First of all, even though academia
has shown some cases of Predictive Maintenance and Digital Twin* applications, the
industry has not managed to successfully apply those principles. Cases of SKF and
CIM.AS signals that the research done in this area is slow and far from the current
goals. Secondly, the current state of the market revolves around Preventive Maintenance
and some applications of Condition-based Monitoring practices. One of the reasons for
the low application of Digital Twin* and Predictive Maintenance is the fact that end
users are not investing until they see the use cases to evaluate actual benefits and costs.
On the other hand, the lack of failure data is caused by efforts put to maximize the
lifetime of the production system. This leads to a discussion on who dedicates a system
for testing and data capturing purposes. Furthermore, it has been seen that with no
clear use cases, the understanding and definition of the concepts differ in each area of
the industry. Therefore, one thing becomes clear, in the scope of this market research,
no one has taken the lead in introducing Predictive Maintenance and to some degree
Digital Twins. This means that the process and steps taken for this matter have to be
thoroughly analyzed and presented.
5.2 Moving towards Digital Twin* for
Predictive Maintenance
After the literature review, it was observed that some cases of Predictive Maintenance
and Digital Twin* application are shown, however, the market has not managed to
apply these cases due to lack of standardization. It takes many smaller steps than the
outlined procedures to achieve the feasibility of the maintenance strategy, depending on

5.3 Work Package I: Data-Driven Predictive Maintenance 34
the complexity of the system and the level of maintenance that can be implemented.
Moreover, it appeared unclear how the two concepts could be applied in a setting that is
different from the use cases presented in academia. This means that the prerequisites on
how to apply different Predictive Maintenance types in the context of digital patterns
are not clearly discussed.
The further analysis sections aim to provide a structured and transparent overview
of the requirements for advancing to various Predictive Maintenance types through the
use of digital patterns, particularly, Digital Shadow. It is proposed to discuss two types
of Predictive Maintenance: Data-Driven, and Physics-based. The two types will be
presented and further referred to as so-called Work Packages, where each Work Package
will in detail describe the prerequisites, available frameworks, and use cases. Ultimately,
this should guide ProjectBinder into adopting more advanced technology and help them
make guided decisions when investigating Predictive Maintenance as a service. With
that, a higher understanding of the concepts as well as standardization when it comes
to digital patterns is achieved.
The next two sections will in-depth describe work packages for Data-driven Pre-
dictive Maintenance, and Physics-Based Predictive Maintenance using Digital Twin*,
and the steps that should be carried out when a company lays the foundation for such
maintenance strategy.
5.3 Work Package I: Data-Driven Predictive
Maintenance
This work package is aimed to analyze a data-driven Predictive Maintenance method.
The use of Machine Learning models for Predictive Maintenance will be discussed to-
gether with the challenges, prerequisites, and current steps taken when developing such
models.
5.3.1 Description
As outlined in the literature, Predictive Maintenance is currently an evolving mainte-
nance technique that, as investigated, has no defined steps and frameworks for thorough
establishment in terms of costs, culture, and technology. Therefore, by exploring the
different use-cases, this analysis of Predictive Maintenance will present the reader with
technological challenges, prerequisites, current takes on frameworks, and expected ben-
efits followed by further recommendations. The use cases will cover following machine
learning models [31, 10, 8, 25]:
1. Convolutional Neural Networks (CNN)
2. Support Vector Model (SVM)

3. Long Short Term Memory (LSTM)
4. Auto Encoder (AE)
These models were used for RUL estimation, Anomaly Detection, and fault classification
modeling.
Lastly, this work package will also analyze the steps and considerations needed to
take when implementing data-driven Predictive Maintenance with Digital Twin∗
and its
patterns according to the literature.
5.3.2 Challenges
As indicated by Nunes et al., there are a number of challenges that have to be considered
for Predictive Maintenance modeling applications [37]. These cover:
1. Interaction between parts is not covered in data-driven component degradation
models.
2. Current research covers single component wear and datasets with synthetically
produced data are not capable of resembling the heterogeneity present in real
data.
3. Large amounts of datasets for each different failure mode are required which are
not currently available.
4. Anomaly detection is only used for detecting critical events while the potential to
detect the important events in the data and generalize them to features used in
Data-driven models is not realized.
5. Current RUL models tend to provide inaccurate prediction in the early stage of
component life, converging to ground truth RUL at the end life of the component
resulting in a short time for maintenance scheduling.
Given these challenges, it is clear that the currently accessible data is only on the
component level leading to limited machine learning model creation. Furthermore, due
to preventive maintenance deployment in the industry, there is a limited amount of failure
data making it harder to speed the development of higher-scope Predictive Maintenance
models. Lastly, RUL accuracy and its measurement techniques have to be investigated
as well as the potential combination of methods for more accurate model creation.
5.3.3 Prerequisites
As data-driven naming suggests, the very first requirements for Predictive Maintenance
enabling is data collection and equipment designed for this purpose. As seen from the
∗
Umbrella term

research, the most contributing factors to Predictive Maintenance have been vibration,
temperature, velocity, load, and acoustical measurements [37]. This means that sensor
equipment such as accelerometers and temperature sensors have to be mounted on the
critical component for healthy data harvest. Besides the operational data, the failure
data has to be collected as well for data labeling purposes. Failure data can come in
the form of operating condition measurements, failure reports, and maintenance logs.
Depending on the purpose of the Predictive Maintenance model, whether it is anomaly
detection, RUL estimation, or classification problem, data has to be labeled with fail-
ure modes, and when the failure has appeared. Considerations towards data recording
frequency as well as cloud storage have to be made regarding the collected data. This
means that communication between sensors, measurement equipment, and servers has
to be established. As later will be shown, data analysis and modeling knowledge are
needed. This implies that besides expert knowledge of system behavior and compo-
nent degradation, data analysts must have the expertise on how to prepare Predictive
Maintenance models in terms of data cleaning, feature selection, model tuning, accuracy
estimation, and model deployment.
5.3.4 Framework analysis
Given that data-driven Predictive Maintenance modeling is still a part of machine learn-
ing model building, the CRISP-DM framework in the Literature section was presented.
As seen from the use cases, following the CRISP-DM framework could deem beneficial,
but specific Predictive Maintenance context modeling questions would not be considered
thoroughly. Therefore, during use-case evaluation a certain flow of steps and considera-
tions became apparent, these steps are visualized in Figure 5.1.
Figure 5.1: Data Driven Predictive Maintenance Framework Outline.
As seen from Figure 5.1, Business Understanding has to be the first step towards
a Predictive Maintenance application. This step has to include goal setting, aforemen-
tioned critical component identification, and selection of Predictive Maintenance mod-

eling type. Following by Data Selection step, available operational and failure data
has to be selected and uploaded to the modeling software. In the case of live data
analysis, automatic data flow has to be considered. This step must also include the
evaluation of sensor data, data cleaning, and preparation for modeling. The next three
steps of the framework are modeling method dependent. This means that some of the
machine learning approaches of Predictive maintenance do not require time, frequency,
and time-frequency domain extractions as well as feature extraction and selection due
to it being done by the deep learning models themselves. Nevertheless, for other deep
learning models, these steps are needed and they have to be based on expert knowledge
in terms of data analysis and machinery understanding. In these cases, data signals
like vibrations can be the most useful. For Predictive Maintenance modeling, vibration
signals can contain hidden information in time and frequency domains that can improve
the model accuracy once the features are extracted. However, computing and using all
possible features of vibration signals can become costly and time-consuming. Therefore,
feature selection has to be performed to evaluate which features are the most contribut-
ing to model accuracy. Once the features are selected, model training and tuning take
place. This step is aimed at training the model with given data and tuning the model
parameters. In this step, considerations towards test/train data split and the number of
training epochs have to be made. Lastly, once the model is trained it has to be tested
and evaluated by different metrics.
These steps will be further analyzed in the context of selected articles.
5.3.5 Use Case
For the use case analysis, four data-driven use cases were selected. The first three
were different machine learning approaches of RUL prediction and were based on NASA
prognostics bearing dataset [38]. The dataset contains bearing Run-to-Failure test data
where 4 bearings of the same type were mounted on a rotating shaft with a constant speed
of 2000 rpm and a radial load of 2721 kg. Three Run-to-Failure tests were performed
with inner race failure in bearing 3 of Test 1 (Test 1-Bearing 3), outer race failure in
bearing 1 of Test 2 (Test 2-Bearing 1) and bearing 3 of Test 3 (Test 3-Bearing 3).
The fourth use case of Anomaly Detection was analyzed in terms of Auto Encoder
deployment with FEMTO Bearing dataset [36]. In this dataset, different loading and
speed operating conditions were used, these are indicated in Table 5.1.
Even though Anomaly detection does not provide RUL, as indicated in section 5.3.2,
it can be used to detect important factors for Predictive Maintenance and gain valuable
insights on critical component degradation, and different operating condition impacts.
Furthermore, an insight into considerations needed to be taken into account when con-
sidering the integration of Data-driven models with Digital Twin* can be made.
The bearing vibrations data is chosen since bearings are a vital part of various ro-
tating equipment found in production machinery. Furthermore, as indicated earlier,
vibration data is resourceful, common, and a more complex measurement used for Pre-
dictive Maintenance modeling.

Condition Speed Load
Number of Bearings
in
# rpm N train test
1 1,800 rpm 4,000 N 2 5
2 1,650 rpm 4,200 N 2 5
3 1,500 rpm 5,000 N 2 1
Table 5.1: Test and Train data for Anomaly Detection.
5.3.5.1 Business understanding and Data Selection
As the first step of Predictive Maintenance modeling, all of the use cases considered
expected benefits from data-driven models for Predictive Maintenance and indicated
these immediate benefits as business beneficial:
1. Higher reliability and productivity of machinery.
2. Availability of Condition monitoring information to make informed decisions for
maintenance planning.
3. Cost reduction by reducing the frequency of unexpected failures.
It is important to note that more benefits can be expected when considering the
overall impact the Predictive Maintenance implementation can have.
In terms of data selection, since the datasets considered for the use case are Run-to-
Failure tests, no live connection of data was established and the whole dataset was loaded
into modeling software. Nevertheless, in real industrial cases, the data might have to be
analyzed continuously, therefore, requiring a live connection, particularly in cases where
the prediction horizon is short. Companies, such as CIM are in the beginning process
of developing a data capture system that can monitor the data from the production line
through sensors and provide constant data flow. Lastly, operating conditions change
and information on the changing environment has to be reflected, this could be done
through continuous data flow to the model.
Besides the data connection, degradation identification was another important aspect
during data selection. Degradation identification is needed to avoid later training the
model on unhealthy data. Two of the analyzed studies performed degradation start
identification. The study utilizing CNN has introduced Incipient Fault Point (IFP) as
a measurement for determining when the bearing starts to degrade. This measurement
is computed as a mean value of RMS (Root Mean Square) ± (6 × σ) and is used to
distinguish from healthy state and degrading state of the bearing for further feature
selection by the model. The IFP identification graph is visualized in Figure 5.2.

Figure 5.2: Incipient Fault Point Identification in two different datasets [31].
In the study utilizing the LSTM model, the Adaptive Kernel Spectral Clustering
(AKSC)-based anomaly detection method is used to perceive the anomaly behaviors of
Test 2-Bearing 1 and Test 1-Bearing 3. This approach was used to determine signals
different from healthy running state signals of a bearing and to determine the start of
bearing degradation. In this case, the train data was loaded into the AKSC model, then
the parameters of the model were automatically updated to increase the identification
accuracy and match the future data. Lastly, in the detection stage, the outlined iden-
tificator was defined to identify the start of the anomalies. The anomaly detection of
bearing degradation can be seen in Figure 5.3. Once the degradation starting point

was identified, the LSTM model could be used for further data analysis and prediction
modeling.
Figure 5.3: Detection of Bearing Degradation in LSTM study [8].

5.3.5.2 Feature extraction and selection
After the data selection and degradation identification, for some of the models, feature
extraction is the next step.
Therefore, Time, Frequency, and Time-frequency domains can be extracted with
features such as Peak Value, Kurtosis, Mean, Min, Max, and Crest Factor. These
features are extracted from vibration signals by the use of formulas and provide more
insights on critical component degradation [35]. The features such as Kurtosis provide
information on how often the outliers occur in the signal, while Peak value or Crest
factor provides information on the highest measurement in the signal and early warning
of the component degradation, respectively. The formulas used for feature extraction as
well as the plotted Features are provided in Figures 5.4 and 5.5 below.
Figure 5.4: Time Domain feature extraction [8].
An overview of Machine Learning models based on feature extraction is presented in
Table 5.2. As it can be seen, CNN does not require feature extraction as the model is
able to extract the features itself, while the rest of compared analysis performed feature
extraction from the vibration signal.
Starting with the SVM model, all three feature domains were extracted. Then, due
to the fact that the SVM model performs better with lower dimensionality, Principle
Component Analysis was used to merge the features and reduce the dimensionality. As
a result, only the first principal component was created and later used to train and test
the model.
In the case of the LSTM model, all three domains were deployed for feature selection.
In terms of the time-frequency domain, Complete ensemble empirical mode decomposi-

Figure 5.5: Example of Extracted Feature plots of NASA Bearing Dataset.
Feature Extraction
ML Model
Time Domain Frequency Domain Time-Frequency Domain No Feature Extraction
CNN
SVM
LSTM
AE
Table 5.2: Feature extraction in selected use cases.
tion with adaptive noise (CEEMDAN) was deployed to extract Intrinsic Mode Energies
(IME) of the signal. The IME is computed to provide a more accurate signal when
machine damage occurs. Lastly, Euclidean distance was used to select relevant features
for modeling. As a result, one Time-frequency, three frequency, and four IME features
were selected.
In the context of AutoEncoder deployment for anomaly detection, Time Domain
features were extracted manually while other features were extracted by inputting the
data into the neural network itself. Once the features were generated by the neural
network, they were concatenated with previously extracted time domain features.
As seen from Table 5.2 and explained above, CNN as a deep learning model can
extract the features itself, and no manual feature extraction was performed in that case.
5.3.5.3 Model training and Performance Evaluation
Once the features are extracted, common steps between all models are model training,
testing, and performance evaluation. When selecting the features and amount of data
for model training, a few considerations have to be made.
Firstly, it has to be noted that models cannot be tested on the data that it was trained
on as it would result in inaccurate measures since the model would be familiarised with

the data beforehand. Therefore, data selected for the training has to be only used for
the training purpose.
Secondly, the data that is selected for training has to reflect the data that the model
will be exposed to. In the context of Predictive Maintenance, it means that model
trained only on healthy data will not be able to generalize well when exposed to data
indicating a failure. That is one of the reasons why degradation phenomena have to be
identified when selecting the data.
Lastly, it is important to note that there exist different training and test techniques
for ML models. For instance, the training methods for CNNs rely on backpropagation
to learn hierarchical representations and update the weights. Backpropagation is used
as a common training algorithm for LSTMs as well. SVMs use optimization algorithms
for margin maximization to find the optimal hyperplane that separates the data. Lastly,
AutoEncoders use gradient descent to minimize reconstruction error. Incorrect choice
of training can affect the model’s accuracy and performance.
In the use cases, starting with the CNN model, IFP was used to find the start of
degradation and label healthy data as 1 (healthy), while data of degradation was linearly
mapped from 1 to 0 (failure). Then, 70% of all data was used to train the model while
the remaining 30% of data was left for testing.
In terms of the SVM model, only degradation data is used as the model is trained
on 190 points of degradation and tested on the last remaining 50 points of data.
For the LSTM model, backpropagation is used as a method to train the model. In
this case, there is no need to split the data into train and test splits. Nevertheless, only
degradation data is used for backpropagation which requires the understanding and skill
to find the start of component degradation.
Lastly, the AE model has been trained on two bearing data sets for each operating
condition, resulting in a total of 6 bearing sets used for training. Validation of the
models was performed on 11 bearings (5 for the first two operating conditions and one
for the last operating condition). The last 20% of the bearing data was not used.
In terms of performance evaluation of the models, an overview is presented in Table
5.3 below.
ML Type/Accuracy RMSE MSE AC CRA AE
LSTM X X X
CNN X
SVM X
AE X
Table 5.3: Model Accuracy Measurement Metrics.
Most authors relied on one performance measurement mainly based on Mean Squared
Error. An important observation suggested by [30] is that for Predictive Maintenance
purposes, reflective performance measurement should be selected. Therefore, since ma-
chinery components are covered by warranty and suggested OEM or part supplier life-

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