LECTURE 14 MAINTENANCE: BASIC CONCEPTS · BASIC CONCEPTS Piero Baraldi Politecnico di Milano,...

11Piero Baraldi

LECTURE 14

MAINTENANCE: BASIC CONCEPTS

Piero BaraldiPolitecnico di Milano, Italy

[email protected]

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LECTURE 14

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

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PART 1: INTRODUCTION TO

MAINTENANCE

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MAINTENANCE

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

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FAILURE

DEGRADATION

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Maintenance expenditures in some industrialized countries

Derived from M. Garetti

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PART 2:MAINTENANCE STRATEGIC

PLANNING

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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

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MAINTENANCE INTERVENTION APPROACHES

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Types of maintenance approaces

Maintenance Intervention

PlannedUnplanned

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Planned Maintenance


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

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Unplanned

CorrectiveReplacement or

repair of failed units

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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

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Corrective maintenance: when is it applied?

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

Failure Maintenance

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Planned maintenance

Failure Maintenance

Decision

Why?

Production and safety benefits

Costs of performing Maintenance

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Maintenance Philosophies (2)

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

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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

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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)

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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

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Failure



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• Maintenance induced failures

Failure



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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

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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

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Condition-Based Maintenance

Planned


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Condition-Based Maintenance (CBM)

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

Failure Maintenance

Decision Monitoring

dfailure

x

0Inspection time

xdfailure

ddetection

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• 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)

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• 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

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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



Failure

Condition Based

Maintenance

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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

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Predictive Maintenance

Planned


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Predictive Maintenance

• Equipment degradation monitoring:

• Remaining Useful Life (RUL) prediction

• Maintenance Decision

Failure Maintenance

Decision MonitoringRUL PROGNOSIS

PROGNOSIS RUL

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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

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Types of maintenance approaches


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

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Unplanned

CorrectiveReplacement or

repair of failed units

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PART 2:

CONDITION-BASED AND PREDICTIVE MAINTENANCE

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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)

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PHM & INDUSTRY 4,036

20172012 Time

Dat

a

Available data

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

• Analytics

AnalyticsData

PHM

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Maintenance Intervention Approaches & PHM


Unplanned

Corrective

Planned

Scheduled Condition-based

Predictive

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Detection X X

Diagnostics X X

Prognostics X

Fault Detection

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Measured signals

Fault Detection: what is it?

Equipment

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Measured signals

f1

f2

Forcing functions

f1

f2

Normal condition

Fault Detection: objective40

Equipment

Automatic algorithm

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

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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

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• Limit Value-Based Fault Detection

Normal operation

range

Abnormal condition

Abnormal condition


10.2 m

3.8 m

Example:

time

Methods for fault detection (I) 43

Water level

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• Limit Value-Based Fault Detection

Normal operation

range

Abnormal condition

Abnormal condition


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)

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Water level

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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:

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Pressurizer model

≠Abnormal Condition

Signalreconstructions

Realmeasurements

Example:

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Abnormal Condition Typically not availablefor complex systemsLong computational time

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Pressurizer model

≠Signal

reconstructionsReal

measurements

Example:



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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:

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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

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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:

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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

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

Auto Associative KernelRegression (AAKR)

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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

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How does AAKR work?

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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 =

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How does AAKR work?

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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 =

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How does AAKR work?

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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

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How does AAKR work?

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• 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

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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

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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

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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

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Example 2

),...,( 1obsn

obsobs xxx =

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


x1

x2

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Example 2: Solution

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x1

x2


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 =

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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)

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∑=

−−=n

j

ncobskj

obsj xxkd

1

22 )()(

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AAKR Performance: Accuracy

• Accuracy:• depends on the training set:

• ↑N ↑ Accuracy

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x1

x2

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AAKR Performance: Accuracy (2)

• Accuracy:• depends on the training set:

• ↑N ↑ Accuracy

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x1

Few patterns and not welldistributed in the training space

Inaccurate reconstruction

x2

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FAULT DETECTION IN NPPAPPLICATION

Reactor coolant pumps

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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

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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

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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

LECTURE 14 MAINTENANCE: BASIC CONCEPTS · BASIC CONCEPTS Piero Baraldi Politecnico di Milano,...

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Transcript of LECTURE 14 MAINTENANCE: BASIC CONCEPTS · BASIC CONCEPTS Piero Baraldi Politecnico di Milano,...