Open topic with navigation

About Weibull Distribution

A Weibull Distribution describes the type of failure mode experienced by the population (infant mortality, early wear out, random failures, rapid wear-out). Estimates are given for Beta (shape factor) and Eta (scale). MTBF (Mean Time Between Failures) is based on characteristic life curve, not straight arithmetic average.

A Weibull Distribution uses the following parameters:

If the value of Beta is greater than one (1), you can perform Preventative Maintenance (PM) Optimizations. A Gamma different from a value zero (0) means that the distribution is shifted to fit the datapoints more closely.

Note: This is an advanced feature and should be used in the proper context and with a good understanding of how to apply a three-parameter Weibull distribution.

Weibull Analysis InformationClosed

You can use the following information to compare the results of individual Weibull analyses. The following results are for good populations of equipment.

Beta Values Weibull Shape Factor

Components

Low

Typical

High

Low (days)

Typical (days)

High (days)

Ball bearing

0.7

1.3

3.5

583

1667

10417

Roller bearings

0.7

1.3

3.5

375

2083

5208

Sleeve bearing

0.7

1

3

417

2083

5958

Belts drive

0.5

1.2

2.8

375

1250

3792

Bellows hydraulic

0.5

1.3

3

583

2083

4167

Bolts

0.5

3

10

5208

12500

4166667

Clutches friction

0.5

1.4

3

2792

4167

20833

Clutches magnetic

0.8

1

1.6

4167

6250

13875

Couplings

0.8

2

6

1042

3125

13875

Couplings gear

0.8

2.5

4

1042

3125

52083

Cylinders hydraulic

1

2

3.8

375000

37500

8333333

Diaphragm metal

0.5

3

6

2083

2708

20833

Diaphragm rubber

0.5

1.1

1.4

2083

2500

12500

Gaskets hydraulics

0.5

1.1

1.4

29167

3125

137500

Filter oil

0.5

1.1

1.4

833

1042

5208

Gears

0.5

2

6

1375

3125

20833

Impellers pumps

0.5

2.5

6

5208

6250

58333

Joints mechanical

0.5

1.2

6

58333

6250

416667

Knife edges fulcrum

0.5

1

6

70833

83333

695833

Liner recip. comp. cyl.

0.5

1.8

3

833

2083

12500

Nuts

0.5

1.1

1.4

583

2083

20833

"O"-rings elastomeric

0.5

1.1

1.4

208

833

1375

Packings recip. comp. rod

0.5

1.1

1.4

208

833

1375

Pins

0.5

1.4

5

708

2083

7083

Pivots

0.5

1.4

5

12500

16667

58333

Pistons engines

0.5

1.4

3

833

3125

7083

Pumps lubricators

0.5

1.1

1.4

542

2083

5208

Seals mechanical

0.8

1.4

4

125

1042

2083

Shafts cent. pumps

0.8

1.2

3

2083

2083

12500

Springs

0.5

1.1

3

583

1042

208333

Vibration mounts

0.5

1.1

2.2

708

2083

8333

Wear rings cent. pumps

0.5

1.1

4

417

2083

3750

Valves recip comp.

0.5

1.4

4

125

1667

3333

Equipment Assemblies

Low

Typical

High

Low (days)

Typical (days)  

High (days)

Circuit breakers

0.5

1.5

3

2792

4167

58333

Compressors centrifugal

0.5

1.9

3

833

2500

5000

Compressor blades

0.5

2.5

3

16667

33333

62500

Compressor vanes

0.5

3

4

20833

41667

83333

Diaphgram couplings

0.5

2

4

5208

12500

25000

Gas turb. comp. blades/vanes

1.2

2.5

6.6

417

10417

12500

Gas turb. blades/vanes

0.9

1.6

2.7

417

5208

6667

Motors AC

0.5

1.2

3

42

4167

8333

Motors DC

0.5

1.2

3

4

2083

4167

Pumps centrifugal

0.5

1.2

3

42

1458

5208

Steam turbines

0.5

1.7

3

458

2708

7083

Steam turbine blades

0.5

2.5

3

16667

33333

62500

Steam turbine vanes

0.5

3

3

20833

37500

75000

Transformers

0.5

1.1

3

583

8333

591667

Instrumentation

Low

Typical

High

Low (days)

Typical (days)

High (days)

Controllers pneumatic

0.5

1.1

2

42

1042

41667

Controllers solid state

0.5

0.7

1.1

833

4167

8333

Control valves

0.5

1

2

583

4167

13875

Motorized valves

0.5

1.1

3

708

1042

41667

Solenoid valves

0.5

1.1

3

2083

3125

41667

Transducers

0.5

1

3

458

833

3750

Transmitters

0.5

1

2

4167

6250

45833

Temperature indicators

0.5

1

2

5833

6250

137500

Pressure indicators

0.5

1.2

3

4583

5208

137500

Flow instrumentation

0.5

1

3

4167

5208

416667

Level instrumentation

0.5

1

3

583

1042

20833

Electro-mechanical parts

0.5

1

3

542

1042

41667

Static Equipment

Low

Typical

High

Low (days)

Typical (days)

High (days)

Boilers condensers

0.5

1.2

3

458

2083

137500

Pressure vessels

0.5

1.5

6

52083

83333

1375000

Filters strainers

0.5

1

3

208333

208333

8333333

Check valves

0.5

1

3

4167

4167

52083

Relief valves

0.5

1

3

4167

4167

41667

Service Liquids

Low

Typical

High

Low (days)

Typical (days)

High (days)

Coolants

0.5

1.1

2

458

625

1375

Lubricants screw compr.

0.5

1.1

3

458

625

1667

Lube oils mineral

0.5

1.1

3

125

417

1042

Lube oils synthetic

0.5

1.1

3

1375

2083

10417

Greases

0.5

1.1

3

292

417

1375

Weibull Results InterpretationClosed

GE Digital APM Reliability shows the failure pattern of a single piece of equipment or groups of similar equipment using Weibull analysis methods. This helps you determine the appropriate repair strategy to improve reliability.

Is the Probability Plot a good fit?Closed

Follow these steps to determine whether or not the plot is a good fit:

  • Identify Beta (slope) and its associated failure pattern.
  • Compare Eta (characteristic life) to standard values.
  • Check goodness of fit, compare with Weibull database.
  • Make a decision about the nature of the failure and its prevention.

The following chart demonstrates how to interpret the Weibull analysis data using the Beta parameter, Eta parameter, and typical failure mode to determine a failure cause.

Weibull Results Interpretation

Beta

Eta

Typical Failure Mode

Failure Cause

Greater than 4

Low compared with standard values for failed parts (less than 20%)

Old age, rapid wear out (systematic, regular)

Poor machine design

Greater than 4

Low compared with standard values for failed parts (less than 20%)

Old age, rapid wear out (systematic, regular)

Poor material selection

Between 1 and 4

Low compared with standard values for failed parts (less than 20%)

Early wear out

Poor system design

Between 1 and 4

Low

Early wear out

Construction problem

Less than 1

Low

Infant Mortality

Inadequate maintenance procedure

Between 1 and 4

Between 1 and 4

Less than manufacturer recommended PM cycle

Inadequate PM schedule

 

Around 1

Much less than

Random failures with definable causes

Inadequate operating procedure
Goodness of Fit (GOF) Tests for a Weibull DistributionClosed

A Goodness of Fit test is a statistical test that determines whether the analysis data follows the distribution model.

Weibull results are valid if Goodness of Fit (GOF) tests are satisfied. Goodness of Fit tests for a Weibull distribution include the following types:

Note:The R-Squared test statistic is calculated only for reference. The GE Digital APM system uses the Kolmogorov-Smirnov test as the Goodness of Fit test.

Related Information

Copyright © 2018 General Electric Company. All rights reserved.