11Piero Baraldi

LECTURE 14

MAINTENANCE: BASIC CONCEPTS

Piero BaraldiPolitecnico di Milano, Italy

piero.baraldi@polimi.it

22Piero Baraldi

LECTURE 14

• PART 1: Introduction to maintenance• PART 2: Condition-Based and Predictive Maintenance

33Piero Baraldi

PART 1: INTRODUCTION TO

MAINTENANCE

44Piero Baraldi

MAINTENANCE

“Equipments, however well designed, will not remain safe or reliable if they are not maintained”

4

FAILURE

DEGRADATION

55Piero Baraldi

Maintenance expenditures in some industrialized countries

Derived from M. Garetti

5

66Piero Baraldi

PART 2:MAINTENANCE STRATEGIC

PLANNING

6

77Piero Baraldi

Maintenance Strategic Planning

• WHEN to act- “Before or after the fact”: maintenance intervention approach;

• ON WHAT BASIS-”Reliability, Availability, Cost, Safety, Environmental-centred”: maintenance decision-making strategy

7

88Piero Baraldi

MAINTENANCE INTERVENTION APPROACHES

8

99Piero Baraldi

Types of maintenance approaces

Maintenance Intervention

PlannedUnplanned

1010Piero Baraldi

Planned Maintenance

Maintenance Intervention

Planned

ScheduledPerform

inspections, and possibly repairs,

following a predefined schedule

Condition-based

Monitor the health of the system and

then decide on repair actions based on the

degradation level assessed

PredictivePredict the

Remaining Useful Life (RUL) of the system and then decide on repair actions based on the predicted RUL

10

Unplanned

CorrectiveReplacement or

repair of failed units

1111Piero Baraldi

Corrective maintenance

• No maintenance action is carried out until the equipment or structure breaks down.

• Upon failure, the associated repair time is typically relatively large →large downtimes

• Efforts are undertaken to achieve Small Mean Times to Repair (MTTRs) → Logistics

Failure Maintenance

11

1212Piero Baraldi

Corrective maintenance: when is it applied?

• Equipments:• No safety critical• No crucial for production performance• Spare parts easily available and not expansive

Failure Maintenance

12

1313Piero Baraldi

Planned maintenance

Failure Maintenance

Decision

Why?

Production and safety benefits

Costs of performing Maintenance

13

1414Piero Baraldi

Maintenance Philosophies (2)

N.S. Arunraj, J. Maiti / Journal of Hazardous Materials 142 (2007) 653–661

14

1515Piero Baraldi

Scheduled Maintenance

• Maintenance is carried out at scheduled intervals• Intervals can be given in terms of:

• calendar time• running time• number of start and stop• their combination

• Equipments may be repaired or replaced

Planned

Scheduled Condition based Predictive

1616Piero Baraldi

Scheduled Maintenance: Objectives

• To rejuvenate the equipment = to decrease its failure rate• Planned replacement (e.g. Planned replacement of the bearing in a rotating

equipment)• To slow down degradation (wear, fatigue) = to limit the increase of the

failure rate• Lubrication• Routine maintenance (tightening of connectors)

1717Piero Baraldi

Scheduled Maintenance: Pros and Cons

• Pros:• Reducing number of failures• Maintenance can be planned when it has the lowest impact on

production or availability of the systems• Cons:

• A scheduled maintenance approach generates maintenance tasks after a specific time interval which can result in a too early replacement of components, which is unprofitable.

Failure

1818Piero Baraldi

Scheduled Maintenance: Pros and Cons

• Pros:• Reducing number of failures• Maintenance can be planned when it has the lowest impact on

production or availability of the systems• Cons:

• A scheduled maintenance approach generates maintenance tasks after a specific time interval which can result in a too early replacement of components, which is unprofitable.

Failure

Scheduled Maintenance

Scheduled Maintenance

1919Piero Baraldi

Scheduled Maintenance: Pros and Cons

• Pros:• Reducing number of failures• Maintenance can be planned when it has the lowest impact on

production or availability of the systems• Cons:

• A scheduled maintenance approach generates maintenance tasks after a specific time interval which can result in a too early replacement of components, which is unprofitable.

• Maintenance induced failures

Failure

Scheduled Maintenance

Scheduled Maintenance

2020Piero Baraldi

Scheduled maintenance: decision

• Optimize the Decision:• Intervals between PM maintenance actions• Action rules

• Model:• Failure/degradation process• Maintenance effects, time to repair• Costs of planned maintenance, corrective maintenance,

production unavailability

Failure/degradation•Failure times

•Degradation evolution

Maintenance•Effects on future

failure/degradation behavior•Time to Repair

Decision•Intervals between PM actions•Action Rules

2121Piero Baraldi

Scheduled Maintenance: Decision

• Optimize the Decision (intervals between maintenance and action rules)

• Model:• Failure/degradation process• Maintenance effects, time to repair• Costs

interval between maintenance

Unavailability Costs

interval between maintenance

2222Piero Baraldi

Condition-Based Maintenance

Planned

Scheduled Condition based Predictive

2323Piero Baraldi

Maintenance Philosophies (2)

N.S. Arunraj, J. Maiti / Journal of Hazardous Materials 142 (2007) 653–661

23

2424Piero Baraldi

Condition-Based Maintenance (CBM)

• Equipment degradation monitoring:• Periodic inspection by manual or automatic systems

Failure Maintenance

Decision Monitoring

dfailure

x

0Inspection time

xdfailure

ddetection

2525Piero Baraldi

Condition-Based Maintenance (CBM)

• Equipment degradation monitoring:• Periodic inspection by manual or automatic systems• Continuous observations

Failure Maintenance

Decision Monitoring

Ultrasonic Monitoring (regularly used in the oil and gas industry)

2626Piero Baraldi

Condition-Based Maintenance (CBM)

• Equipment degradation monitoring:• Periodic inspection by manual or automatic systems• Continuous observations

• Equipment degradation level identification by:• Direct measure (crack depth of a mechanical component)• Indirect observations (symptoms related to the degradation

process, e.g. quality of the oil in an engine, partial discharges inelectrical cables, vibrations frequencies and amplitudes in rotatingmachinery)

Failure Maintenance

Decision Monitoring

2727Piero Baraldi

CBM: Conclusions

• Identification of problems in equipment or structures at the earlystage so that necessary downtime can be scheduled for the mostconvenient and inexpensive time.

Failure

Scheduled Maintenance

Scheduled Maintenance

Failure

Condition Based

Maintenance

2828Piero Baraldi

CBM: Conclusions

• Identification of problems in equipment or structures at the earlystage so that necessary downtime can be scheduled for the mostconvenient and inexpensive time.

• Machine or structure operate as long as it is healthy: repairs orreplacements are only performed when needed as opposed toroutine disassembly and servicing.

• Availability

• Unscheduled shutdowns of production

• Reduced costs• Improved safety

2929Piero Baraldi

Predictive Maintenance

Planned

Scheduled Condition based Predictive

3030Piero Baraldi

Maintenance Philosophies (2)

N.S. Arunraj, J. Maiti / Journal of Hazardous Materials 142 (2007) 653–661

30

3131Piero Baraldi

Predictive Maintenance

• Equipment degradation monitoring:

• Remaining Useful Life (RUL) prediction

• Maintenance Decision

Failure Maintenance

Decision MonitoringRUL PROGNOSIS

PROGNOSIS RUL

3232Piero Baraldi

Predictive Maintenance: Ex. 1

• t=300: perform maintenance now or postpone it to the next planned outage at t=400?

time

Degradation level

t=300Present Time t=400

dfailure pastdegradation observations

degradation model

RUL PREDICTION

postpone maintenance to the next planned outage at t=400

3333Piero Baraldi

Types of maintenance approaches

Maintenance Intervention

Planned

ScheduledReplacement or

Repair following a predefined schedule

Condition-based

Monitor the health of the system and

then decide on repair actions based on the

degradation level assessed

PredictivePredict the

Remaining Useful Life (RUL) of the system and then decide on repair actions based on the predicted RUL

33

Unplanned

CorrectiveReplacement or

repair of failed units

3434Piero Baraldi

PART 2:

CONDITION-BASED AND PREDICTIVE MAINTENANCE

3535Piero Baraldi

Prognostics and Health Management

Normal operation

Remaining Useful Life(RUL)

t

1x

t

2x

Detect Diagnose Predict

Equipment (System, Structure or Component)

c2c1 c3

Measuredsignals

Anomalous operation

Malfunctioning type (classes)

3636Piero Baraldi

PHM & INDUSTRY 4,036

20172012 Time

Dat

a

Available data

• Digitalization2.8 Trillion GD (ZD) generated in 2016

• Analytics

AnalyticsData

PHM

3737Piero Baraldi

Maintenance Intervention Approaches & PHM

Maintenance Intervention

Unplanned

Corrective

Planned

Scheduled Condition-based

Predictive

37

Detection X X

Diagnostics X X

Prognostics X

Fault Detection

Piero Baraldi

38

3939Piero Baraldi

39

Measured signals

Fault Detection: what is it?

Equipment

4040Piero Baraldi

40

Measured signals

f1

f2

Forcing functions

f1

f2

Normal condition

Fault Detection: objective40

Equipment

Automatic algorithm

4141Piero Baraldi

41

• Methods for Fault Detection:• Limit-based• Model-based• Data-driven

4242Piero Baraldi

Data & Information for fault detection (I)

• Normal operation ranges of key signals

Normal operation

range

Abnormal condition

Abnormal condition

Pressurizer of a PWR nuclear reactor

10.2 m

3.8 m

Water level

Example:

time

42

4343Piero Baraldi

• Normal operation ranges of key signals

• Limit Value-Based Fault Detection

Normal operation

range

Abnormal condition

Abnormal condition

Pressurizer of a PWR nuclear reactor

10.2 m

3.8 m

Example:

time

Methods for fault detection (I) 43

Water level

4444Piero Baraldi

• Normal operation ranges of key signals

• Limit Value-Based Fault Detection

Normal operation

range

Abnormal condition

Abnormal condition

Pressurizer of a PWR nuclear reactor

10.2 m

3.8 m

Example:

time

Methods for fault detection (I)

Drawbacks:• No early detection•Not applicable to fault detection during operational transients•Control systems operations may hide small anomalies (the signal remains in the normal range although there is a process anomaly)

44

Water level

4545Piero Baraldi

Methods for fault detection (II)

• Normal operation ranges of key signals• Physics-based model of the process (used to reproduce the expected behavior of

the signals in normal condition)

Pressurizer model

Signalreconstructions

Example:

45

4646Piero Baraldi

Methods for fault detection (II)

• Normal operation ranges of key signals• Physics-based model of the process (used to reproduce the expected behavior of

the signals in normal condition)

Pressurizer model

≠Abnormal Condition

Signalreconstructions

Realmeasurements

Example:

46

4747Piero Baraldi

Methods for fault detection (II)

Abnormal Condition Typically not availablefor complex systemsLong computational time

47

Pressurizer model

≠Signal

reconstructionsReal

measurements

Example:

• Normal operation ranges of key signals• Physics-based model of the process (used to reproduce the expected behavior of

the signals in normal condition)

4848Piero Baraldi

Data & Information for fault detection (III)

• Normal operation ranges of key signals• Physics-based model of the process in normal operation• Historical signal measurements in normal operation

Water level

PressurePressureLiquid

temperature

Steam temperat

ure

Spray flow

Surge line flow

Heaters power Level

150.2 321 362 539 244 0 7.2

150.4 322 363 681 304 0 7.5

150.3 323 364 690 335 1244 7.7

… … … … … … …

Example:

48

4949Piero Baraldi

Methods for fault detection (III)

• Normal operation ranges of key signals• Physics-based model of the process in normal operation• Historical signal measurements in normal plant operation

Empirical model of the process:• Auto Associative Kernel Regression• Principal Component Analysis• Artificial Neural Networks• …Water level

Pressure

49

5050Piero Baraldi

50

Abnormal Condition

• Normal operation ranges of key signals• Physics-based model of the process in normal operation• Historical signal measurements in normal plant operation

EMPIRICAL MODEL OF PLANT BEHAVIOR

IN NORMAL OPERATION

Methods for fault detection (III)

≠Signal

reconstructionsReal

measurements

Example:

5151Piero Baraldi

51

COMPARISON

MODEL OF COMPONENT BEHAVIOR IN NORMAL

CONDITIONS

ŝ1

t

t

ŝ2s1

t

t

s1 – ŝ1 s2 – ŝ2

s2

t

…

DECISION

t

NORMALCONDITION:

No maintenance

ABNORMALCONDITION:maintenance

required

The fault detection approach

Pb. 1

Pb. 2

SignalreconstructionsReal

measurements

Residuals

5252Piero Baraldi

52

• Modeling the component behavior in normal conditions• The Auto Associative Kernel Regression (AAKR) method

Auto Associative KernelRegression (AAKR)

5454Piero Baraldi

What is AAKR?

• Auto-associative model

• Empirical model built using training patterns = historical signal measurements in normal plant condition

x1

x2

Auto-Associative

Model

1x

2x

nx

1x̂

2x̂

nx̂

( )ni

xxxfx ni

,...,1,...,,ˆ 21

=∀=

=

−−

−−

−

ncobsNnNj

ncobsN

knkjk

ncobsnj

ncobs

ncobs

xxx

xxx

xxx

X

.........

...

.........

......

.........

...

.........

1

1

1111

Signal

Observation

5555Piero Baraldi

How does AAKR work?

55

Training pattern = historical signal measurements in normal plant condition

Test pattern: input = measured signals at current time

Output = signal reconstructions (expected values of the signals in normal condition)

),...,( 1obsn

obsobs xxx =

=

−−

−−

−

ncobsNnNj

ncobsN

knkjk

ncobsnj

ncobs

ncobs

xxx

xxx

xxx

X

.........

...

.........

......

.........

...

.........

1

1

1111

AAKR

obsx1

obsx2

obsnx

ncx1ˆncx2ˆ

ncnx̂

ncobsX −

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

5656Piero Baraldi

How does AAKR work?

56

Training pattern = historical signal measurements in normal plant condition

Test pattern: input = measured signals at current time

Output = weighted sum of the training patterns:

),...,( 1obsn

obsobs xxx =

x1

x2

=

−−

−−

−

ncobsNnNj

ncobsN

knkjk

ncobsnj

ncobs

ncobs

xxx

xxx

xxx

X

.........

...

.........

......

.........

...

.........

1

1

1111

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

5757Piero Baraldi

How does AAKR work?

57

Training pattern = historical signal measurements in normal plant condition

Test pattern: input = measured signals at current time

Test pattern: output = weighted sum of the training patterns:

),...,( 1obsn

obsobs xxx =

x1

x2

=

−−

−−

−

ncobsNnNj

ncobsN

knkjk

ncobsnj

ncobs

ncobs

xxx

xxx

xxx

X

.........

...

.........

......

.........

...

.........

1

1

1111

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

On all the training pattern

∑

∑

=

=

−⋅= N

k

N

k

ncobskj

ncj

kw

xkwx

1

1

)(

)(ˆ

5858Piero Baraldi

How does AAKR work?

58

• Output = weighted sum of the training patterns:

• weights w(k) = similarity measures between and (the test and the k-th training pattern):

• with Euclidean distance between and

• h = bandwidth parameter

On all the training pattern

∑

∑

=

=

−⋅= N

k

N

k

ncobskj

ncj

kw

xkwx

1

1

)(

)(ˆ

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

obsx ncobskx −

2

2

2)(

21)( h

kd

eh

kw−

=π

∑=

−−=n

j

ncobskj

obsj xxkd

1

22 )()( obsx ncobskx −

x2

x1

high weight

low weight

5959Piero Baraldi

Bandwidth parameter

-6 -4 -2 0 2 4 60

2

4

6

8

10

12

14

h=0.2h=2

d=0 w=0.40/h d=h w=0.24/h

d=2h w=0.05/hd=3h w=0.004/h

( ) 60004.024.0

)3(==

==

hdwhdw

d

w w

6060Piero Baraldi

Example 1

),...,( 1obsn

obsobs xxx =

•Signal values at current time: •Signal reconstructions?•Normal or abnormal condition?

x1

x2

•available historical signal measurements in normal plant condition

6161Piero Baraldi

Example 1: Solution

),...,( 1obsn

obsobs xxx =

•Signal values at current time: •Signal reconstructions: based on the available historical signal measurements in normal plant condition

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

ncobs xx

ˆ≅

x1

x2

normal condition

6262Piero Baraldi

Example 2

),...,( 1obsn

obsobs xxx =

•Signal values at current time: •Signal reconstructions?•Normal or abnormal condition?

•available historical signal measurements in normal plant condition

x1

x2

6363Piero Baraldi

Example 2: Solution

63

x1

x2

•available historical signal measurements in normal plant condition

ncobs xx

ˆ≠

abnormal condition

),...,( 1obsn

obsobs xxx =

•Signal values at current time: •Signal reconstructions: based on the available historical signal measurements in normal plant condition

)ˆ,...,ˆ(ˆ 1ncn

ncnc xxx =

6464Piero Baraldi

AAKR: Computational Time

• Computational time:• No training of the model• Test: computational time depends on the number of training

patterns (N) and on the number of signals (n)

64

∑=

−−=n

j

ncobskj

obsj xxkd

1

22 )()(

6565Piero Baraldi

AAKR Performance: Accuracy

• Accuracy:• depends on the training set:

• ↑N ↑ Accuracy

65

x1

x2

6666Piero Baraldi

AAKR Performance: Accuracy (2)

• Accuracy:• depends on the training set:

• ↑N ↑ Accuracy

66

x1

Few patterns and not welldistributed in the training space

Inaccurate reconstruction

x2

6767Piero Baraldi

FAULT DETECTION IN NPPAPPLICATION

Reactor coolant pumps

6868Piero Baraldi

Fault Detection: Application*68

COMPONENT TO Reactor Coolant Pumps of a PWRBE MONITORED Nuclear Power Plant

x4

__________________________________________________

MEASURED 48 signals

Training patterns = historical signal measurements in normal plant condition measured for 1 year, every 30 seconds

* Work developed with EDF-R&D

6969Piero Baraldi

Results: reconstruction of three different sensor failures

0 10 20 30 40 50 60 70 80 90 100Ti

xtest nc (4a)xtest ac (4a)

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1

Time

resi

dual

s

SENSOR: Temperature of the water flowing to the first seal of the pump in line 1:

Failure 2 = sensor offset

Failure 3 =sensor stuck

Time

0 10 20 30 40 50 60 70 80 90 100Time

e

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1

Ti

resi

dual

s

Time

0 10 20 30 40 50 60 70 80 90 100Time

Time

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1

Time

resi

dual

s

Failure 1 = measurement noise increase

resi

dual

resi

dual

resi

dual

Fault injection

7070Piero Baraldi

Results: seal deterioration detection70

COMPARISON

DECISION

ŝ1

t

t

s1 – ŝ1

t

s1

ABNORMAL CONDITION: seal deterioration

(SEALOUTCOMING

FLOW)

MEASURED SIGNALS

NORMALCONDITION

ABNORMALCONDITION

AUTO-ASSOCIATIVE MODEL OF PLANT

BEHAVIOR IN NORMAL CONDITIONS