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![Page 1: Presentazione di PowerPoint · t 100% 75% 50% 25% 0% X(t) Demand of system performance D(t) System unavailability U(D,t) = Pr[X (t) < D(t)] Degradation-to-failure modeling. 17 KID](https://reader033.vdocuments.site/reader033/viewer/2022042117/5e957aa8d6a24f44be1793f3/html5/thumbnails/1.jpg)
Reliability, Availability and Maintenance (RAM) &
Prognostics and Health Managemenrt (PHM) - 4.0 Enrico Zio
Chair on Systems Science and the Energy Challenge – CentraleSupelec, FondationElectricité de France (EDF), France
Energy Department, Politecnico di Milano, Italy
Aramis Srl, Italy
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INDUSTRY 4.0
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Industry 1-2-3-4
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Industry 4.0- (Cyber-Physical/Smart) Systems
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RAM & PHM 4.0
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The Big KID
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Big Knowledge(ID)
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Big (K)Information(D)
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Big (KI)Data
11101010010100010111001001010110000101010011101110111011101010010100010111001001010110000101010011101110111011101010010100010111001001010110000101010011101110111011101010010100010111001001010110000101110101001010001011100100101011000010101001110111011101110101001010001011100100101011000010101001110111011101110101001010001011100100101011000010
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Application
Can the Big KID become SMART for
Reliability Engineering ?
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FAILURES
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Prevented by
Design for Reliability
Time
Normal Degraded Failure
Failures
12
…
Maintenance
Failures
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Prevented by
Design for Reliability Maintenance
Time
Normal Degraded Failure
Problem statement
Failures
13
…
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Reliability and Availability Engineering 4.0
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Failure modelling (binary)
ON OFF
Failure
As Good As New Failed
t
X(t)
100%
0%
System unavailability U(t) = Pr[X (t) <100%]
U(t) = Pr[X (t) =0%]
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OFF
Failure
Degradation
state 1
Degradation
state n1
Failure Mode 1
…
Degradation
state 1
Degradation
state nM
Failure Mode M
…
…ON
Multi-state:
t
100%
75%
50%
25%
0%
X(t)
D(t)Demand of system performance
System unavailability U(D,t) = Pr[X (t) < D(t)]
Degradation-to-failure modeling
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ModelKID(Knowledge, Information, Data)
0 20 40 60 800.994
0.995
0.996
0.997
0.998
0.999
1
Year
Reliability
Sufficient failure
data
Physics knowledge
Expert judgment
Field dataHighly reliable
Statistical models
of time to failure
Stochastic process
models
Physics-based
models
Multi-state
models
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Reliability ?
SMART Reliability Engineering:
Big KID opportunities
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Multi-state physics model of crack development
in Alloy 82/182 dissimilar metal weld
Alloy 82/182 dissimilar metal weld of piping in a PWR primary coolant system
Physical laws
18SMART Reliability Engineering
Multi-State Physic-Based Models
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Internal leak
Failure state
3 2 1 0λ32 λ21 λ10
Initial state
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Degradation processes
Piecewise-deterministic Markov
process (PDMP)
SMART Reliability Engineering:
Big KID opportunities
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MC Simulation
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Finite-volume scheme
SMART Reliability Engineering:
Big KID opportunities
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Prevented by
Maintenance
Time
Normal Degraded Failure
Maintenance engineering 4.0
Failures
21
…
Design for Reliability
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Maintenance and PHM Engineering 4.0
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Prognostics and Health
Management (PHM)
1950 1980 2000
Corrective
Maintenance
Planned Periodic
Maintenance
Condition Based
Maintenance (CBM)
2016
Predictive
Maintenance (PrM)
PHM is fostered by advancements in:
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Maintenance
Sensor Algorithm Computation power
Maintenance
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PHM for what?
PHM in support to CBM and PrM
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EquipmentMaintenance
Decision
Abnormal
Conditions
Normal
Conditions
Anomaly of Type 1
Anomaly of Type 2
Anomaly of Type 3
Maintenance
No
Maintenance
Decision
Maker
Remaining Useful
Life (RUL)
Fault
Detection
Fault
Diagnostics
Fault
Prognostics
…
Vibration
t
Sensors
measurements
t
Temperature
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➢ Increase maintainability, availability, safety,
operating performance and productivity
➢Reduce downtime, number and severity of failure
and life-time cost
PHM: why? (Industry)
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➢ Improve cash flow, profit stream and utilization ofassets
➢Guarantee long term business
➢ Increasemarket share
PHM: why? (Business)
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Abnormal Condition
MODEL OF
PLANT BEHAVIOR
IN NORMAL OPERATION
PHM: how? (Fault detection)
≠
Signal
reconstructions
Real
measurements
0 500 10000
10
20
0 500 100065
70
75
80
0 500 100065
70
75
80
0 500 10000
10
20
Nominal Range-based
Physics-based
Data-Driven (AAKR, PCA,
RNN,…)
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• Empirical classification methods:
• Support Vector Machines
• K-Nearest Neighbours
• Multilayer Perceptron Neural Networks
• Supervised clustering algorithms
• Ensemble of classifiers
• …
Empirical Classifier
C1 = Inner race
C2 = Balls
C3 = Outer race
2x
1x
3x
Peak ValueNorm Node 5
Wavelet
No
rmN
od
e1
4
Wa
ve
let
• Signal measurements representative of the fault classes: «x1,x2,…xn, class»
PHM: how? (Fault diagnostics)
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Health
Index
tp
tp
FAILURE THRESHOLD
Prognostic
model
tf
LUR ˆ
Health index
prediction
t
t
t
t
Rotating
machinery (e.g.
pump)
Baraldi, P., Cadini, F., Mangili, F., Zio, E. Model-based and data-driven prognostics under different available information (2013) Probabilistic
Engineering Mechanics, 32, pp. 66-79.
E. Zio, F. Di Maio, “A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear Power Plant”,
Reliability Engineering and System Safety, RESS, 10.1016/j.ress.2009.08.001, 2009.
F. Di Maio, K.L. Tsui, E. Zio, “Combining Relevance Vector Machines and Exponential Regression for Bearing RUL estimation”, Mechanical Systems
and Signal Processing, Mechanical Systems and Signal Processing, 31, 405–427, 2012.
PHM: how? (Fault prognostics)
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Sources of uncertainty:
1) noise on the observations (measurements)
2) intrinsic stochasticity of the degradation process
3) unknown future external/operational conditions
4) Modeling errors, i.e. inaccuracy of the prognostic model used to
perform the prediction
Uncertainty on the RUL prediction ?
Uncertainty management (prognostics)
0 500 1000 1500 2000 2500 3000 35000
1
2
3
4
5
6x 10
-3
RUL
RUL pdf estimate
True RUL
Maximum
acceptable failure
probability is 5% Prognostic Model
Present
Time
Probability to have
a failure in this interval is
lower than 5%
time for
maintenance
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POLITECNICO DI MILANOL. Bellani Reliability and Availability with PHM
td tf
Failure ThresholdNominal Functioning Threshold
Remaining Useful Life (RUL)
RAM & PHM 4.0
System failure behavior:-Component intrinsicreliability-Component Maintanibility…
Predictive maintenancepolicy criteria:-Inspections interval-Time required to preparemaintenance based on predicted RUL…
PHM system:-Diagnostic Performance-Prognostic Performance
System Reliability (Safety Indicator)Survival probability
System Availability (Economic Indicator)Probability that the component fulfills the assigned mission at any specific moment of the lifetime
PHM metrics
MODEL
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Conclusions
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Conclusions: Big KID and Smart KID
Fuzzy Logic
Systems
Optimization
Algorithms
FTA
ETA
FMECA
Hazop
Clustering
Algorithms
Graph
Theory
Petri Nets
Neural
Networks
Bayesian
Belief
Networks
Complex Network
Theory
Monte Carlo
Simulation
Process and
Stochastic
Flowgraphs
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Simulation, Modeling, Analysis, Research for Treasuring Knowledge, Information and Data
(for Reliability and Maintenance 4.0)
SMART KID
Data
Information
Knowledge
Conclusions: Smart KID for Reliability Engineering
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Conclusions: Smart KID for Reliability Engineering
E. Zio, IEEE Trans on Reliability, 2016
Some challenges and opportunities in reliability engineering
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Thanks…
…for your attention