maintenance and reliability – · pdf file1 maintenance and reliability – theory...

1

Maintenance and Reliability –Theory

John E. Skog P.E.WGA3-06 Tutorial

June 2006Rio de Janeiro

2

Today’s Agenda

• Evolution of Maintenance and Driving Theory

– Traditional Bimodal Maintenance

– Reliability Centered Maintenance

– Condition Based Maintenance

– Performance Focused

• Three Case Studies

– Cables

– SF6 Breakers

– Transformer On-line Monitoring

3

Overhaul

Time

CMCM

PMPMTr

aditio

nal

Asset Asset

Maintenance Evolution – Traditional

4

Characteristics of Traditional Maintenance

Intrusive Generalized

Conservative/Excessive

Belief More Maint.IncreasedReliability

5

Traditional Maintenance

Benefits DrawbacksPeriodic inspection servicing is necessary

Time is poor predictor of wear

Acknowledgement that full equipment operating life is only possible if worn parts are replaced.

Overhauls create more problems than they solve

High cost

Manufacturers did not understand the operating environmentReliability and availability were not being met

6

Overhaul

Safety Function

FailureCause

Criticality

EffectsTime

CMCM

PMPMTr

aditio

nal

Run toFailureRun toFailure

Event

FailureFindingFailureFinding

ConditionCondition

RCM

Asset Asset

Maintenance Evolution – RCM

7

RCM

“A structured process that identifies the effects of

failures and defines the appropriate maintenance path for managing their impacts. RCM identifies both the most technically and economic effective approach to maintenance .”

8

RCM History• Airlines

1965 MSG-1 (Maint. Steering Group)1970 MSG-2Experience

– DC 8• 339 Scheduled Removal Tasks• 7 Scheduled Removal Tasks

– 747• 8 Scheduled Removal Tasks

9

RCM History (cont.)

• Airline Observations:– Maintenance needs to focus on system that have

significant impact on safety or economics. – Hard time overhaul policies were ineffective.– Management of maintenance was crucial.

10

RCM History (cont.)

• US Navy – 1978 Contracted United Airlines

• US Electric Utilities - 80’s– EPRI Sponsored Nuclear 1985-1987– Fossil Fuel Plants

• US Electric Utilities - 90’s– Substations 1990– T&D

• EDF and Others– Nuclear Plants– Transmission Substations

11

RCM Task Selection

“The RCM task selection approach used to ensure that only applicable and cost effective tasks are selected to address

the causes of critical equipment failure modes”

RCM Task Categories– Inspection-Condition Monitoring-Predictive Maintenance– Periodic

• Rework-Time Directed• Discard-Time Directed

– Failure Finding– Run to Failure

12

Preserved

Simplified Task Selection Logic

PDM

PM

Design Change

Hidden Failure Finding

Yes

Yes

No

No

Yes

Yes

No

No

SafetyConsequences

Task Must ReduceRisk of Failure

OperationalConsequencesTask must cost

Less than the consequences

Non-OperationalConsequencesTask must costless than repair

Run to failure

Is the Failure Evident?

Can the failure be tolerated?

Is a time directed task effective?

Is Condition Monitoring Effective?

Review Effects

Mode & CauseSelected

Corrective Maintenance

Is the Failure Evident?

Can the failure be tolerated?

Is a periodic task effective?

Is Condition Monitoring Effective?

NoNo--Design Design ChangeChange

NoNo--Periodic Periodic PMPMNoNo--Failure Failure

Finding TaskFinding Task

Failed

13

Characteristics of Reliability Centered Maintenance

Preservationof

Function

SpecializedTasks

Focused onDominant

Causes

14

Reliability Centered Maintenance

Benefits DrawbacksCritical Functions Viewed as difficult and not applicable

to power industry

Equipment and application specific

99.999% (1 hour of outage per year) reliability is difficult to understand

Greater insight into failure process

Living program forgotten

Eliminated ineffective tasks Did not set maintenance intervals

15

Overhaul

Safety Function

FailureCause

Criticality

EffectsTime

Aging Models

Sensors

Real Time Data

Predictions

CMCM

PMPMTr

aditio

nal


Event


ConditionCondition

RCM

IMIM

PredictivePredictive

CBM

DiagnosticsDiagnosticsProactive

Asset Asset

Maintenance Evolution – CBM

16

Condition Based Maintenance

“Condition Based Maintenance accentuates the value of RCM task selection logic and emphasizes that more intrusive replacement and overhaul tasks only need to take place when measurable wear or aging occurs.”

“Condition Directed Tasks are initiated when deterioration has gone beyond a prescribed limit”

17

Characteristics of CBM

AgingMechanismUnderstood

DataDriven

GreaterReliance onMeasurableConditions

Unobtrusive

18

Condition Based Maintenance

Benefits DrawbacksIncreased availability Data systems were may not be

adequate.

Reduced costs Process management overlooked.

More frequent analysis of asset condition

No methodology for justifying increased monitoringIncreased back-office analysis

19

Overhaul

Safety Function

FailureCause

Criticality

EffectsTime

Aging Models

Sensors

Real Time Data

Predictions

CMCM

PMPMTr

aditio

nal


Event


ConditionCondition

RCM

IMIM


CBM


Asset Asset

Maintenance Evolution – PFM

Orga

niza

tion

Process

Data

EventFailureFindingFailureFinding


IMIM


CMCM

PMPMConditionCondition

Asset Asset Tr

aditio

nal RCM

CBM


Performance Focused Maintenance

20

What is PFM?

PFM is a comprehensive maintenance strategy emphasizing the understanding of Failure Mechanisms, Measurement, Interval Optimization, Task Prioritization, Feedback and the use of Data. PFM recognizes the need for process control

21

What Maintenance is Included in PFM?

Maintenance includes all activities associated with

preserving or restoring critical functions. Typical maintenance activities include:

– Preventive Maintenance

– Condition Monitoring/Inspections

– Diagnostic Testing

– Integrated Monitoring

– Predictive Activities

– Hidden Failure Finding Tasks

– Condition Directed Corrective and Renewal Tasks

– Corrective Maintenance

– Pre-Emptive Replacement

22

PFM 12 Step Methodology

Reconciliation and Program Development

Step 8-9

Implementation Documentation

Step 11- 12

Perform Failure Mode

and Effect Analysis (FMEA)

Steps 3-5

Aging Mechanisms

Step 6

TimeTime

Failu

re

Failu

re

Rat

eR

ate

Task Selection and Interval Optimization

Step 7

Identify System Boundaries and Critical Functions

Steps 1-2

Measures, Metrics &KPIs

Step 10

23

Understanding: The Aging ProcessFailure Initiation Mechanisms

Bridging Business Issues andTechnical Requirements

24

TimeTime

BathtubBathtub

Failu

re R

ate

Failu

re R

ate

TimeTime

Pronounced Wear outPronounced Wear out

Failu

re R

ate

Failu

re R

ate

TimeTime

Low Initial failure rate with Low Initial failure rate with quick increasequick increase

Failu

re R

ate

Failu

re R

ate

Failu

re R

ate

Failu

re R

ate Gradual Wear outGradual Wear out

TimeTime

TimeTime

Constant Failure RateConstant Failure Rate

Failu

re R

ate

Failu

re R

ate

TimeTime

Decreasing Failure RateDecreasing Failure Rate

Failu

re R

ate

Failu

re R

ate

General Age Reliability PatternsRenewal

Strategies are ineffective

25

Task Interval Optimization-Weibull Age Modeling

F(t) = 1-e-[(t-t0)/η]β

– t0 = Guarantee Period

– η = Characteristic Life .. MTBF

– β = Shape factor

Failure Probability Distribution

0%2%4%6%8%

10%12%14%16%18%

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Infant MortalityRandom

Wear

Pronounced Wear

Cumulative Failure Probability

0%

20%

40%

60%

80%

100%

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Infant Mortality

RandomWear

Pronounced Wear

With Some Good Data, Failures Can

be Predicted Mathematically

26

Failure Initiation Patterns

0

2

0 5 10 15 20 25

Binary Pattern

Increasing Age

OK

Failed0

2

0 5 10 15 20 25

Intermittant Failure Pattern

Increasing Age

OK

Failed

0

2

0 5 10 15 20 25

Fast Wear Pattern

Increasing Age

OK

Failed

0

2

0 5 10 15 20 25

Slow Wear Pattern

Increasing Age

OK

Failed

Some Failures Provide no Warning,

Some Give Adequate Warning

27

Characteristics of PFM

FocusedMaintenance

IntegratesBest Business

and TechnologyPractices

Optimization

Life CycleApproach to

Data Management

ProcessMeasurement

AndFeedback

28

Performance Focused Maintenance

Benefits DrawbacksIncreased availability and reliability

Requires quality data collection and storage processes

Reduced life-cycle costs Must be understood and supported by the highest management levels

Data collection fundamental part of the processIntegrated business and technology approach to maintenance

29

Performance Focused MaintenanceCase Studies (3)

30

Case I-15kV Distribution Cables• Issues:

– Aging population-(four insulation systems)– Inspection program that did not affect

failure rates– Complicated and time consuming

replacement ranking system– Ineffective condition assessment tasks – Poor asset data– 0.4% replacement rate

• Drivers– Performance Based Rates– High replacement costs

• Key Considerations– Design Improvements– Mostly in conduit

31

Equipment Group: Population by Age and Insulation Type

U G P r i m a r y C a b l e s

0

2 0 0

4 0 0

6 0 0

8 0 0

1 ,0 0 0

1 ,2 0 0

1 ,4 0 0

1 ,6 0 0

1 ,8 0 0

2 ,0 0 0

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Y e a r

Con

duct

or M

iles

X L P E

T R -X L P EP I L C

H M W

Type of Insulation

Expected Service Life

Circuit Miles

PILC 45 - 50 years 4,900HMW-PE 15-20 years 1,500XLPE 25-30 years 30,200TR-XLPE 45 years + 2,400

Identify System Boundaries and Critical Functions

Steps 1-2

32

FMEA: Cables Perform Failure Mode and Affect Analysis (FMEA)

Steps 3-5

Dominant Failure modes are “Failure

to Insulate and Failure to provide a

ground plane”

33

Tree Examples - Failure to Insulate

#2 AWG HMW vintage cable which failed in Ridgecrest.Water treeing more than 50% through the insulation

#2 AWG XLPE cable from Fullerton. Water treeing more than 40% through the insulation.

Cable Insulation

Source: DAE, Improving the performance of underground cable. Sept 14, 2001

Water TreesCable Insulation

Aging Mechanism

Step 6

Resultant Strategy:

Pre-emptive Replacement

34

Cable Unreliability

0

0.2

0.4

0.6

0.8

1

yr(s) 10 yr(s) 20 yr(s) 30 yr(s) 40 yr(s) 50 yr(s) 60 yr(s)Years after installation

Unre

liabi

lit PILCHMWXLPETR-XLPE

Cable Insulation Failure Model

10% Limit

35

Pre-emptive Replacement Strategy – Age Limit (10% failure)

SAIDI + SAIFI IMPACTS

$0

$10

$20

$30

$40

$50

$60

$70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Mill

ions

Year

No Age LimitAge Limit

36

Case II – SF6 Breaker Maintenance

• Issues:– Extension of Oil Breaker

Maintenance Philosophy– Declining Reliability– Increasing Maintenance

Costs– Increased Availability

Required– No CMMS– Maintenance Behind

Schedule

37

10 Year Results

• Effective Knowledge Transfer

• Improve Data Collection

• Extended Maintenance Intervals (double) with Defendable Basis

• Additional PM Triggers-Age Exploration

• Reduction in Rework Activities-Improved PM Effectiveness

• Increased Availability

• Improved Reliability

• Elimination of PM Backlog

38

SF6 PFM Implementation Results

10 Year Analysis Period

SF6 Breakers(69% of the total population)

More than 10,000 Inspections and Maintenance Tasks

39

SF6 CIRCUIT BREAKERSTYPE OF PROBLEMS DETECTED DURING MAINTENANCE ACTIVITIES

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002YEAR

0

20

40

60

80

100

120

140

160

180

200

No problems found Minor Problems DetectedMajor Problems Detected SF6 Circuit Breakers MeasuredMajor Failures (% / Installed) Minor Failures (% / Installed)

Decreasing Failure Rate

Decreasing Number of Inspections

Improved Maintenance Plan Resulted in a Decreased Failure Rate

Catch-up Period

StabilityRCM

Implementation

40

S F 6 C IR C U IT B R E A K E R SF IN D IN G S D U R IN G R E V IS IO N S - R E S O L U T IO N O F M IN O R F A IL U R E S

0 %

2 0 %

4 0 %

6 0 %

8 0 %

1 0 0 %

1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1

R e s o lve d d u rin g w o rk s R e s o lve d in s h o rt te rm S c h e d u le fo r n e x t in s p e c tio n

Improvement in Maintenance Effectiveness

Improved Effectiveness

Improved Maint.

Techniques

41

Case III-Application of On-Line Monitors

• Issues:– Aging Asset Population– Recent Cascading

Failure– Push to Install New

Monitoring Technology– Poor Experience with

Hydrogen Monitors– Increased Insurance

Rates

42

Fleet Characteristics

• “Large” Power Transformers

• 220 KV to 115 or 66KV

• 120 to 280 MVA

• Single and Three Phase

• Average Age = 39 Years

• Max Age = 76 Years

• Replacement Costs $3M to $4M (on the pad)

• Population = 188

43

Failure History (population = 188)

Failure Events

01234567

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Failu

res

.s

44

Age Distribution

A-Bank Age Distribution

01020304050

76-80

66-70

56-60

46-50

36-40

26-30

16-20 6-1

0

Age

0%20%40%60%80%100%

AgeDistribution

AccumulatedPercentage

Median AgeMedian Age

44 years44 years

45

Failures as a Function of Age

A Bank Failures

0

1

2

3

4

5

6

0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50

Age at Time of Failure

Failu

res

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

Rat

e

46

Industry Reported Failure Distribution

Failure Distribution by Impacted System

Unknown (45%)Dielectric (38% to

45%)

Containment (3%-6%) Current Carying

(7%)

Mechanical (0%-4%)

47

Utility Reported Failure and Trouble Distribution

Dielectric46%

External Interface

7%

Electro-magnetic

7%

Voltage Regulating

40%

Problem Occurrence Distribution

NLTC7%

Can't do Maintenance

2%

Accessories12%

Cooling13% Bushings

18%

Inert18%

Leaks19%

None9%

High PF2%

ProblemsFailures On-lineMonitorTarget

48

TimeTime

BathtubBathtub

Failu

re R

ate

Failu

re R

ate

TimeTime

Pronounced Wear outPronounced Wear out

Failu

re R

ate

Failu

re R

ate

TimeTime

Low Initial failure rate with Low Initial failure rate with quick increasequick increase

Failu

re R

ate

Failu

re R

ate

Failu

re R

ate

Failu

re R

ate Gradual Wear outGradual Wear out

TimeTime

TimeTime

Constant Failure RateConstant Failure Rate

Failu

re R

ate

Failu

re R

ate

TimeTime

Decreasing Failure RateDecreasing Failure Rate

Failu

re R

ate

Failu

re R

ate

Age Reliability Patterns-Failure Probability

Monitoringcan be veryeffective if..

49

Typical Reliability Predictive Models

Annual Failure Rate

0.0%

0.5%

1.0%

1.5%

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55

Year

Linear Failure RateActual

Annual Failure Rate

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48

HSB Failure ModelActual

Annual Failure Rate-Weibull

0.00%

0.50%

1.00%

1.50%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56

Age

Failure Rate-WeibullActual

50

Model Comparison Applied to Existing Fleet

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ann

ual F

ailu

re R

a

WeibullHSBLinear

Current FailureRate

51

Failure Mechanisms

0

2

0 5 10 15 20 25

Binary Pattern

Increasing Age

OK

Failed0

2

0 5 10 15 20 25

Intermittant Failure Pattern

Increasing Age

OK

Failed

0

2

0 5 10 15 20 25

Fast Wear Pattern

Increasing Age

OK

Failed

0

2

0 5 10 15 20 25

Slow Wear Pattern

Increasing Age

OK

Failed

52

Incipient Failure Pre-cursor Model

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

0 2 4 6 8 10 1214 16 18 20 22 24 2628 30 32 34 36 38 40

Months

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%

Prob

abili

ty o

f Fai

lure

IncrementalProbaility of Failure

CumulativeProbability of Failure

Incipient Failure Model


0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

0 2 4 6 8 10 1214 16 18 20 22 24 2628 30 32 34 36 38 40

Months

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%

Prob

abili

ty o

f Fai

lure



Probability ofSuccessfulDetection


0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

0 2 4 6 8 1012 14 16 18 2022 24 26 28 3032 34 36 38 40

Months

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%

Pro

babi

lity

of F

ailu

re



Probability ofSuccessfulDetection

Mean Time for an incipient fault = 14 mos.

63% Probability of Detection

12 Month DGA Sampling Cycle

53

On-line Monitoring Decision Model

• Failure Model• Direct Costs

– Transformer– Collateral Damage– Fines

• Indirect Costs– Commissions and Ratepayers– Insurance– Stress on other units– Supply impacts

• True Risk Reduction• Fleet Replacement Impacts

54

-$250,000

-$200,000

-$150,000

-$100,000

-$50,000

$0

$50,000

$100,000

$150,000

40Years

41Years

42Years

43Years

44Years

45Years

46Years

47Years

48Years

49Years

50Years

51Years

52Years

53Years

54Years

55Years

56Years

57Years

58Years

59Years

2. No Maintenance

Cumulative Cash Flow for Multi-Gas Monitors

-$250,000

-$200,000

-$150,000

-$100,000

-$50,000

$0

$50,000

$100,000

$150,000

40Years

41Years

42Years

43Years

44Years

45Years

46Years

47Years

48Years

49Years

50Years

51Years

52Years

53Years

54Years

55Years

56Years

57Years

58Years

59Years

1. On-line Monitoring 2. No Maintenance

-$250,000

-$200,000

-$150,000

-$100,000

-$50,000

$0

$50,000

$100,000

$150,000

40Years

41Years

42Years

43Years

44Years

45Years

46Years

47Years

48Years

49Years

50Years

51Years

52Years

53Years

54Years

55Years

56Years

57Years

58Years

59Years

1. On-line Monitoring 2. No Maintenance 3. Periodic DGA

-$250,000

-$200,000

-$150,000

-$100,000

-$50,000

$0

$50,000

$100,000

$150,000

40Years

41Years

42Years

43Years

44Years

45Years

46Years

47Years

48Years

49Years

50Years

51Years

52Years

53Years

54Years

55Years

56Years

57Years

58Years

59Years

1. On-line Monitoring 2. No Maintenance5. No Maintenance-On-line Monitoring 3. Periodic DGA4. No Maintenance-Periodic DGA

55

Transformer Fleet Risk Exposure Profiles

$-$2$4$6$8

$10$12

Millions

NoMaintenance

Current PMProgram

Current PM +On-Line DGA

Total Annual "A Bank" Failure Risk

Electro-Magnetic

LTC

Bushing

Transformer Main InsulationSystem

56

Extended Useful Life

Deferred Transformer Replacement

($100)$0

$100$200$300$400$500$600$700$800$900

Year 0 Year 1 Year 2 Year 3

Thou

sand

Extended Life

Interest Deferral onCapital Expenditure

Cost of On-line Monitorand annual O&M

Cumulative Cash Flow

57

Conclusions from PFM Approach:

Substantial benefit can be obtained from installation of multi-gas monitors across a large fleet of power transformers– Improved transformer reliability– Reduced failure impacts– Realization of full transformer useful life– Identification of units in urgent need of

repair/replacement.– Substantial reduction in overall

transformer operating risks

58

Future Trends in Maintenance

• Sharing of Failure and Trouble data– Mode and Cause Level– Demographics

• Application• Type

– Population• Age models vs. Failure Rate

• Full Asset Utilization• Risk Reduction• Key Performance Indicators

– Asset Family– Maintenance Process– Life Cycle Costs

59

Open Discussion and Questions:

Need More Information?

[email protected]

360.352.9977

2037 Berry St. NE

Olympia WA, 98506 USA

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