lecture 12 maintenance: basic concepts · 2018-05-07 · • normal operation ranges of key signals...

11Piero Baraldi

LECTURE 12

MAINTENANCE: BASIC CONCEPTS

Piero Baraldi

Politecnico di Milano, Italy

[email protected]

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LECTURE 12

• 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

Scheduled

Perform

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

Predictive

Predict the

Remaining Useful

Life (RUL) of the

system and then

decide on repair

actions based on

the predicted RUL

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Unplanned

Corrective

Replacement 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

ScheduledCondition

basedPredictive

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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 thefailure 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

ScheduledCondition

basedPredictive

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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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• Continuous observations

Failure Maintenance

Decision Monitoring

Ultrasonic Monitoring

(regularly used in the oil and gas industry)

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• Continuous observations

• Equipment degradation level identification by:

• Direct measure (crack depth of a mechanical component)

• Indirect observations (symptoms related to the degradationprocess, 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

ScheduledCondition

basedPredictive

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Predictive Maintenance


• Remaining Useful Life (RUL) prediction

• Maintenance Decision

Failure Maintenance

Decision MonitoringRUL

PROGNOSIS

0 500 10005

10

15

PROGNOSIS

0 500 10000

10

20RUL

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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=300

Present Time t=400

dfailure past

degradation

observations

degradation

model

RUL PREDICTION

▪ postpone maintenance to the next planned outage at t=400

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Types of maintenance approaches


Planned

Scheduled

Replacement 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

Predictive

Predict the

Remaining Useful

Life (RUL) of the

system and then

decide on repair

actions based on

the predicted RUL

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Unplanned

Corrective

Replacement 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

operationMalfunctioning type

(classes)

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PHM & INDUSTRY 4,0

36

20172012 Time

Da

ta

Available data

• Digitalization

2.8 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: objective

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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•Control systems operations may hide small anomalies (the signal remains in the normal range although there is a process anomaly)•Not applicable to fault detection during operational transients

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Water level

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Methods for fault detection (II)


• Physics-based model of the process (used to reproduce the expected behavior of the signals in normal condition)

Pressurizer model

Signalreconstructions

Example:

0 500 100065

70

75

80

0 500 10000

10

20

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Pressurizer model

≠Abnormal Condition


Realmeasurements

Example:

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10

20

0 500 100065

70

75

80

0 500 100065

70

75

80

0 500 10000

10

20

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Abnormal Condition➢ Typically not availablefor complex systems➢Long computational time

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Pressurizer model

≠


Realmeasurements

Example:

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10

20

0 500 100065

70

75

80

0 500 100065

70

75

80

0 500 10000

10

20



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Data & Information for fault detection (III)


• Physics-based model of the process in normal operation

• Historical signal measurements in normal operation

Water level

PressurePressure

Liquid

temperat

ure

Steam

temperat

ure

Spray

flow

Surge

line

flow

Heaters

powerLevel

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)



• 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



• Historical signal measurements in normal plant operation

EMPIRICAL MODEL OF

PLANT BEHAVIOR

IN NORMAL OPERATION

Methods for fault detection (III)

≠


Realmeasurements

Example:

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10

20

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70

75

80

0 500 100065

70

75

80

0 500 10000

10

20

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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

NORMAL

CONDITION:

No

maintenance

ABNORMAL

CONDITION:

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

,...,,ˆ21

ncobs

NnNj

ncobs

N

knkjk

ncobs

nj

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)

),...,( 1

obs

n

obsobs xxx

ncobs

NnNj

ncobs

N

knkjk

ncobs

nj

ncobs

ncobs

xxx

xxx

xxx

X

......

...

...

.........

......

.........

...

...

......

1

1

1111

AAKR

obsx1

obsx2

obs

nx

ncx1̂

ncx2ˆ

nc

nx̂

ncobsX

)ˆ,...,ˆ(ˆ1

nc

n

ncnc xxx

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How does AAKR work?

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Output = weighted sum of the training patterns:

),...,( 1

obs

n

obsobs xxx

x1

x2

ncobs

NnNj

ncobs

N

knkjk

ncobs

nj

ncobs

ncobs

xxx

xxx

xxx

X

......

...

...

.........

......

.........

...

...

......

1

1

1111

)ˆ,...,ˆ(ˆ1

nc

n

ncnc xxx

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How does AAKR work?

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

),...,( 1

obs

n

obsobs xxx

x1

x2

ncobs

NnNj

ncobs

N

knkjk

ncobs

nj

ncobs

ncobs

xxx

xxx

xxx

X

......

...

...

.........

......

.........

...

...

......

1

1

1111

)ˆ,...,ˆ(ˆ1

nc

n

ncnc xxx

On all the

training pattern

N

k

N

k

ncobs

kjnc

j

kw

xkw

x

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

ncobs

kjnc

j

kw

xkw

x

1

1

)(

)(

ˆ

)ˆ,...,ˆ(ˆ1

nc

n

ncnc xxx

obsx ncobs

kx

2

2

2

)(

2

1)( h

kd

eh

kw

n

j

ncobs

kj

obs

j xxkd1

22 )()(obsx ncobs

kx

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=0.2

h=2

▪ d=0 w=0.40/h

▪ d=h w=0.24/h

d=2h w=0.05/h

d=3h w=0.004/h

60

004.0

24.0

)3(

hdw

hdw

d

w w

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Example 1

),...,( 1

obs

n

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

),...,( 1

obs

n

obsobs xxx


•Signal reconstructions: based on the available

historical signal measurements in normal plant condition

)ˆ,...,ˆ(ˆ1

nc

n

ncnc xxx

ncobs xxˆ

x1

x2

normal condition

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Example 2

),...,( 1

obs

n

obsobs xxx


•Signal reconstructions?

•Normal or abnormal condition?


x1

x2

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Example 2: Solution

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x1

x2


ncobs xxˆ

abnormal condition

),...,( 1

obs

n

obsobs xxx


•Signal reconstructions: based on the available

historical signal measurements in normal plant condition

)ˆ,...,ˆ(ˆ1

nc

n

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

ncobs

kj

obs

j xxkd1

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 well

distributed 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*

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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

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5050

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

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49

50

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

47

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49

50

Time

x(4

a)

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

resid

ua

ls

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

-0.5

0

0.5

1

Time

resid

ua

ls

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

-0.5

0

0.5

1

Timere

sid

ua

ls

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

Failure 2 = sensor offset

Failure 3 =sensor stuck

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5050

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

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49

50

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

47

48

49

50

Time

x(4

a)

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

resid

ua

ls

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

-0.5

0

0.5

1

Time

resid

ua

ls

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

-0.5

0

0.5

1

Time

resid

ua

ls

0 10 20 30 40 50 60 70 80 90 10046

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49

5050

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

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49

50

Time

x(4

a)

0 10 20 30 40 50 60 70 80 90 10046

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48

49

50

Time

x(4

a)

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

resid

ua

ls

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

-0.5

0

0.5

1

Time

resid

ua

ls

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

-0.5

0

0.5

1

Time

resid

ua

ls

Failure 1 = measurement noise increase

resid

ual

resid

ual

resid

ual

Fault injection

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Results: seal deterioration detection

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COMPARISON

DECISION

ŝ1

t

t

s1 – ŝ1

t

s1

ABNORMAL CONDITION:

seal deterioration

(SEAL

OUTCOMING

FLOW)

MEASURED SIGNALS

NORMAL

CONDITION

ABNORMAL

CONDITION

AUTO-ASSOCIATIVE

MODEL OF PLANT

BEHAVIOR IN NORMAL

CONDITIONS

lecture 12 maintenance: basic concepts · 2018-05-07 · • normal operation ranges of key signals...

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