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

3

Industry 1-2-3-4

4

Industry 4.0- (Cyber-Physical/Smart) Systems

5

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

Prevented by

Design for Reliability

Time

Normal Degraded Failure

Failures

12

…

Maintenance

Failures

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

Prevented by

Maintenance

Time

Normal Degraded Failure

Maintenance engineering 4.0

Failures

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…

Design for Reliability

Maintenance and PHM Engineering 4.0

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

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

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