The document discusses using digital twins and Industry 4.0 concepts to synchronize maintenance and operations decisions in real-time. It outlines how digital twins of maintenance systems and production processes could interact to determine the optimal time for maintenance based on current degradation levels, future operational loads, and costs. While the models presented are theoretical, they demonstrate the need for digital replicas that are updated continuously from sensor data and can provide real-time feedback on scenarios. The goal is to minimize total costs by optimizing the tradeoff between maintenance costs, failure costs from downtime, and costs of relaxing production schedules.

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Industry 4.0 and real-time synchronization
of operation and maintenance
Jørn Vatn
Department of Mechanical and Industrial Engineering
NTNU - Norwegian University of Science and Technology, Norway

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Background
• Maintenance decisions needs to take into account
– Current state of the component, system etc (“real-time”)
– Cost of maintenance and failures taking current operational
context into account (“real time”)
– Future loads affecting the probability of failure
• These loads may be influenced by changing operational profiles
Can we use Industry 4.0 concepts to approach the challenges?

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Objectives
• Elaborate on basic elements of Industry 4.0
– Digital twin, stochastic digital twin
– Real-time
– Digital twins interacting
• Present elements of a case study: towards a set of
interacting digital twins

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Industry 4.0 (Forth industrial revolution)
• Industry 4.0 is a collective term particularly used in manufacturing to emphasize
technologies and concepts of value chain organizations
• Related terms
– Cyber-Physical Systems
– the Internet of Things
– Cloud computing
– Digital Twin
• Although the term originates from the manufacturing industry, the elements of
Industry 4.0 are relevant for most businesses (Maintenance 4.0, Safety 4.0,
Ship 4.0,…)
• The current usage of the term Industry 4.0 has been criticized as essentially
meaningless
Focus on the elements rather than the term Industry 4.0 as such !

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IoT -Internet of Things
• The Internet of Things (IoT) is the network of items
embedded with electronics, software, sensors,
actuators, and network connectivity
• which enable these objects to connect and exchange
data
IoT is what we need to connect

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Cloud computing
• Cloud computing is an information technology paradigm
that enables access to shared pools of configurable
system resources
• In some presentations the term Internet of Services (IoS)
rather than cloud computing
With cloud computing we do not need to think about
platforms, how to connect etc

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Digital twin
• The digital twin refers to a digital replica of physical assets,
processes and systems that can be used in real-time for
control and decision purposes
– Computerized mathematical model (what we have done over years)
– Real-time, thanks to IoT
• In contrast to a physical asset, the digital twin can
immediately respond to what-if inquiries

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Garnter TOP 10 (2019):
• The notion of a digital representation of real-world entities or
systems is not new. Its heritage goes back to computer-aided
design representations of physical assets or profiles of individual
customers
• The difference in the latest iteration of digital twins is:
– The robustness of the models with a focus on how they support specific
business outcomes such that high reliability and efficient maintenance
– Digital twins’ link to the real world, potentially in real-time for
monitoring, and control
– The application of advanced big data analytics and AI/ML to drive new
business opportunities
– The ability to interact with them and evaluate “what-if” scenarios

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Draft DNVGL-RP-A204 Qualification and
assurance of digital twins
• The capability of DTs can be ranked on a scale from 0 – 5:
0-standalone
1-descriptive
2-diagnostic
3-predictive
4-prescriptive
5-autonomy

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Off-line digital models
• Remaining useful lifetime (RUL) models have been
available
– First principle approaches (white box)
– Probabilistic models (gray-box)
– Data driven models / ML / AI (black box)
• These models exists, but are mainly used as off-line
models

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The need for on-line models
• For predictive maintenance
1. Anomaly detection
2. Diagnostic
3. Prognostics / RUL

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The need for on-line models
• For predictive maintenance
1. Anomaly detection
1. Machine learning
2. First principles
3. Signal processing (FFT)
2. Diagnostic
1. Signal processing (FFT)
2. Machine learning
3. Prognostics / RUL
1. Not much

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The need for decision support
• For anomaly detection
– False positives and false negatives
• Sensor drifts
• Long term drifts in the process, which is not related to physical
degradation
• Diagnostic
– Is treatment required?
• Prognostics
– What and when to do “hard maintenance”
– Scheduling, taking opportunity windows into account
– How, will changes in operational loads affect RUL, and what to do?

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Time, t
Current time and current state
Mean RUL
Deterioration level, Y(t)
L
Prognostics models: RUL

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Time, t
Deterioration level, Y(t)
L
m
TL
= Lead time
Decisions: m = Maintenance level

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Time, t
Deterioration level, Y(t)
L
m
TL
= Lead time
Decisions requiring DT-inquiries
• What is the current
production demand?
• Will there be a
maintenance opportunity in
the near future?
• Can I relax on production to
reduce degradation rate
• Is it a weather window?
• How to group maintenance
activities, remote
operation?

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Digital Twin vs Stochastic Digital Twin

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Stochastic digital twin
• A stochastic digital twin is a computerized model of the
stochastic behavior of a system where
– the model is updated in real-time
• based on sensor information and other information
• accessed via the internet and the use of cloud computing resources
• What-if inquiries result in pdf’s rather than single values

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Real time model vs test model
“Google maps – Traffic” vs “Google maps Bicycle-friendly routes»

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Real-time model
• A real-time model is a model where it is possible to
obtain values of system performance and system states
in real-time
• With real-time we mean that data referring to a system is
analysed and updated at the rate at which it is
received

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Test model
• A test model is a mathematical model describing relations between
future and current values of the variables of interest, but where we
are not able to monitor system performance and system sates in
real-time
• Such a model is often referred to as an off-line model or a
sandbox model
• A test model is still valid in order to establish decision rules to be
used in real-time
Claim: 99.9% of all models presented at ESREL since 1991 are test
models because they have no ambitions to connect in real time

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Anomaly detection
• Highlights from:
– A2.1 MonitorX framework
– Review of analytics methods supporting Anomaly detection and
Condition Based Maintenance
• First principles
• Machine learning
• Objective: Separate anomalies and noise

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Motivation – Do we spot anomalies?

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First principles
• The physical equations describing the top oil and hot-spot
temperature dynamics are the following
• The physical model parameters may be estimated from data
during normal operation
• From the model we can predict (estimate) temperature and
compare with actual temperature:

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Machine learning
• The first principle model requires a physical
understanding of the relation between input (current and
ambivalent temperature) and output (hot-spot
temperature)
• If the relation is complex, we can use huge training data
sets to estimate the relation between input and output:

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Big data analytics and data driven models
• There are several techniques for these data driven
models
– Classical multivariate regression analysis
– Artificial neural networks
– Deep learning
– Decision tree learning
– Support vector machines
• The hot-spot temperature example:

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Case study – Turnout monitoring
• Turnouts (switches) are important components in the railway
infrastructure, and failure of a turnout will usually give large
problems with the circulation, and delays are expected
• A range of condition monitoring techniques
exist
• BaneNOR is running at test project
in Norway where the electric current of the
motor is monitored, and represents a
signature that can alert a coming failure

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Signature (Normal operation)
0
0 1 2 3 4 5
Current
Time, seconds

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Indication of a potential failure
0 1 2 3 4 5
Signature, s Actual, a
𝑎𝑖 − 𝑠𝑖
2 > Threshold?

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Digital twins
• A maintenance twin
• A production twin (including punctuality)

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Digital twin for maintenance (degradation)
• The focus is on “when to act” upon a potential failure rather
than the classical inspection interval approach
• A PF-model is used, z(t) = f(t)/R(t) for TPF:
• 𝑧 𝑡 𝑦, 𝑥 𝑡 = 𝑧0 𝑡 𝑒𝛽𝑦𝑦+𝛽𝑥𝑥𝑡
• Where
– z0(t) = baseline failure rate function, TPF
– y = degradation level at the point of warning
– x(t) = future load in the near future (t is typically minutes and hours)
– 𝛽𝑦 and 𝛽𝑥 regression coefficient in the Cox-proportional hazard
model
Failure
progression
Time
Failure
Critical failure progression
F
P
PF-
interval

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Real-time ?
• Not actually, the case study is only illustrative
• But
– y = current degradation level in real-time is in principle
accessible
– x(t) = future loads
• This is something the digital twin for the production in principle can
respond on, i.e., how many trains are scheduled to pass the
coming hours and how many shifting operations are required?
• In a “what-if” analysis, we may also investigate what is happening if
we relax on operation, i.e., if we move crossing to another station to
avoid operating the switch (and reduce the likelihood of a failure)

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For example y is obtained in real-time by
0 1 2 3 4 5
Signature, s Actual, a
𝑦 = 𝑎𝑖 − 𝑠𝑖
2

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Real-time ?
• Not actually, the case study is only illustrative
• But
– y = current degradation level in real-time is in principle
accessible
– x(t) = future loads
• This is something the digital twin for the production in principle can
respond on, i.e., how many trains are scheduled to pass the
coming hours and how many shifting operations are required?
• In a what-if analysis, we may also investigate what is happening if
we relax on operation, i.e., if we move crossing to another station to
avoid operating the switch (and reduce the likelihood of a failure)

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Stochastic digital twin?
• Yes:
– 𝐹 𝑡 𝑦, 𝑥 𝑡 = 1 − 𝑒 0
𝑡
𝑧(𝑢|𝑦,𝑥 𝑡 𝑑𝑢
– The cumulative distribution function, F(), is essentially what we
need for the optimization:

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The objective function to minimize:
• 𝐶 𝑡, 𝑥 = 𝑐PM 𝑡 + 𝑐U𝐹 𝑡 𝑦, 𝑥 + 𝑐R(𝑥)
– t = time for when to act upon the potential failure = decision
variable
– x = how many times we operate the degraded switch
– 𝑐PM 𝑡 = Cost of preventive action, decreases as function of t, to
be obtained from the production digital twin
– 𝑐U = Punctuality (unavailability) cost upon a failure (production
digital twin, or punctuality model)
– 𝑐R(𝑥) = Relaxing cost, i.e., a function of how many times the
switch is operated (production digital twin)

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Results
• It is assumed that maintenance opportunities exist at
time 3, 5, 7 and 9 hours at various “costs”
• Calculation examples are shown in the paper
• Result (optimal intervention time, t):

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Conclusions
• Straight forward mathematical models are presented
• We have demonstrated the need to synchronize
maintenance and operation
• We have indicated what is required, i.e., a maintenance
model and a production model that are updated in real-
time (the digital twins)
• We have demonstrated how these twins interact
• There is still a long way to go get the models actually
running in real-time

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Thank you
Questions?