ieee - aiaa aerospace 2011 - paper 1497

8/13/2019 IEEE - AIAA Aerospace 2011 - Paper 1497

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Method for

Detect ion and Con f i rmat iono f Mu lt ip le Fai lure Modes

w i th Numerous Surv ivo r Data

IEEE/AIAA

Aerospace Con ference

Paper #1497 (11.0801)

9 March 2011

Mark A. Powell

Attwater Consulting


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Int roduct ion

Sometimes,

Sometimes Easy to See in Data, Sometimes Not

When it Fails, In Which Mode is it Failing?

Identification of Failure Modes Needed to Findand Fix the Problems, or to Improve PreventativeMaintenance

US Coast Guard HU-25 Aircraft Had such a

Problem with its AC Generator Commercial Reliability Analysis Software Did not

Help Much

Another Approach was Needed

Method for Detect ion and Confirmation of Mult iple Failure Modes with Num erous Surviv or Data;Paper #1497; Mark A. Pow ell, Attwater Cons ult ing , [email protected] , 208-521-2941 Slide # 2

Aerospace Systems Fai l for More than One Reason


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The USCG HU-25 Falcon

The AC Generator was

Failing,

45 Failures

41 Survivors The Solution

Perform Reliability

Analysis with a

Commercially Available

SW Tool Investigate the Failure

Mode; then Fix the

Problem or Modify PM

Procedure


Often


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The COTS Reliab i l i ty

Analys is Resu l ts Bent

Regression

Line Suggests

Multiple

Failure Modes

Now What? Segregate Data

and ReprocessSeparately

How Do you

Segregate

Survivor Data?



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Segregat ion/Reprocess

COTS Resu lts Reference Single Batch Mean Estimates

: 3227.19 : 0.62

Segregation: t 1,000 Hours : 547.25 : 0.72

Segregation: 1,000 t 2,000 Hours : 1730.55 : 4.67

Segregation: t 2,000 Hours

: 3156.37 : 11.05


Not Part icular ly

Conclusive,

or Insight fu l ,

or Sat isfy ing


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An A lternate Solut ion

Model the Data with Two Failure Modes

No Classical Method Exists to Estimate Joint Model for ,

a, a, b, and b Use Conditional Approach

Avoid Unnecessary Questionable Assumptions

No Data Segregation Necessary

Must Use Markov Chain Monte Carlo (MCMC) Numerical

Methods

But will This Conditional Approach Work?

Will it Detect PhantomFailure Modes?

Will it Work with Survivor Data?


( | , , , , )

* | , 1 * | ,

f a a b b

a f a a b f b b

pd t

Weib t Weib t

Yes!


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Validate the Method

Create Dummy Data for Reference Multimode

Models, and Test


Parameter Value

a 800 a 0.7b 1500 b 3.7

10,000 Samples for Each Set:

= 0.01, 0.25, 0.5, 0.75, and 0.99

Surv ivors for tf> 1,800

Survivor Cutof f :

tf 1,800


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Val idat ion Resu lts

Markov Chain Initial Conditions for all Tests

= 0.5, a= b = 1,192, a= b= 1.5 First, for Data Sets with = 0.01 and 0.99, Markov

Chains Never Stabilized


No Phantom Modes Detected!

Minimum Maximum Mean (True) 0.066 0.423 0.223 (0.25) 0.066a 118.6 1864.4 777.5 (800) 351.2 a 0.573 1.156 0.789 (0.7) 0.077b 1387.3 1593.4 1500.9 (1500) 28.9 b 2.822 4.892 3.638 (3.7) 0.295

MCMC Marginal Sample Stats for = 0.25

10,000 Jo int

MCMCSamples

Post Burn -in


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More Validation Tes t Resu lts




MCMCMarginal

Sample

Stats for

= 0.5

MCMCMarginal

Sample

Stats for

= 0.75


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HU-25 Data Resu lts

Same Markov Chain Initial Conditions for HU-25 Data = 0.5, a= b = 1,192, a= b= 1.5

Two Failure Modes Detected

Infant Mortality, EarlyWearout Markov Chains Stabilized Nicely

Results not as Crazy as with Segregation and Reprocessing

with COTS


Minimum Maximum Mean 0.026 0.869 0.412 0.148a 0.7 3497.7 1106.1 987.0 a 0.033 0.574 0.284 0.068b 1547.2 4496.2 3052.0 491.4 b 0.439 5.919 2.099 0.909


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Method Compar isons

Recall, With COTS Segregation Sets

0.62 11.05

547.25 3227.19 With Conditional Approach

No Arbitrary Segregation of Survivors

Markov Chains Stabilized Nicely, No Phantom Modes

About 40/60% Split on Modes Weibaand Weibb

a 0.3; b 2 a 1,100; b 3,000

Recall, Validation Tests with 0.25 0.75 Well

Behaved for Similar Infant Mortality and Early

Wearout Combination of Failure Modes



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Summary and Conclus ions

Many More Details and Plots in Paper #1497 (11.0801)

Conditional Approach Using Mixture Model Validated

If only one Failure Mode in Data, Markov Chain will not

Stabilize Method will NOTDetect Phantom Fai lure Modes,

Will Detect Multiple Modes

No Arbi trarySegregation of Survivor Data

Method is Indicated When Failure Data Appears Clustered,

or COTS Reliability SW Shows a BentRegression Line

Method can be Expanded for More than Two Failure Modes

May Lead to Improved Failure Mode Investigations and PMPrograms


If you have Data and a Sim ilar Prob lem, Call Me!


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Con tact In format ion

Mark A. Powell

Attwater [email protected]

http://www.linkedin.com/in/attwatermarkpowell

208-521-2941

mailto:[email protected]://www.linkedin.com/in/attwatermarkpowellhttp://www.linkedin.com/in/attwatermarkpowellmailto:[email protected]

ieee - aiaa aerospace 2011 - paper 1497

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