• Save
An introduction to weibull analysis

Like this? Share it with your network

Share

An introduction to weibull analysis

  • 850 views
Uploaded on

Objectives ...

Objectives
 To understand Weibull distribution
 To be able to use Weibull plot for failure time analysis and
diagnosis
 To be able to use software to do data analysis
Organization
 Distribution model
 Parameter estimation
 Regression analysis

More in: Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
850
On Slideshare
840
From Embeds
10
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 10

http://www.slideee.com 10

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. An Introduction to Weibull Analysis (威布尔分析引论) Rong Pan ©2014 ASQ http://www.asqrd.org
  • 2. RONG PA N ASSOCIATE PROFESSOR A RIZONA ST A T E U NIVERSIT Y EM A IL: RONG.PA N@ A SU .EDU An Introduction to Weibull Analysis
  • 3. Outlines 4/12/2014Webinar for ASQ Reliability Division 3  Objectives  To understand Weibull distribution  To be able to use Weibull plot for failure time analysis and diagnosis  To be able to use software to do data analysis  Organization  Distribution model  Parameter estimation  Regression analysis
  • 4. A Little Bit of History 4/12/2014Webinar for ASQ Reliability Division 4  Waloddi Weibull (1887-1979)  Invented Weibull distribution in 1937  Publication in 1951  A statistical distribution function of wide applicability, Journal of Mechanics, ASME, September 1951, pp. 293-297.  Was professor at the Royal Institute of Technology, Sweden  Research funded by U.S. Air Force
  • 5. Weibull Distribution 4/12/2014Webinar for ASQ Reliability Division 5  A typical Weibull distribution function has two parameters  Scale parameter (characteristic life)  Shape parameter  A different parameterization  Intrinsic failure rate  Common in survival analysis  3-parameter Weibull distribution  Mean time to failure  Percentile of a distribution  “B” life or “L” life                     t e t tf 1 )( .0,,0,1)(            tetF t  t etF  1)(             t etF 1)( )/11(  MTTF
  • 6. Functions Related to Reliability 4/12/2014Webinar for ASQ Reliability Division 6  Define reliability  Is the probability of life time longer than t  Hazard function and Cumulative hazard function  Bathtub curve )(1)(1)()( tFtTPtTPtR  )( )( )( tR tf th   t dxxhtH 0 )()( )( )( tH etR   Time Hazard
  • 7. Understanding Hazard Function 4/12/2014Webinar for ASQ Reliability Division 7  Instantaneous failure  Is a function of time  Weibull hazard could be either increasing function of time or decreasing function of time  Depending on shape parameter  Shape parameter <1 implies infant mortality  =1 implies random failures  Between 1 and 4, early wear out  >4, rapid wear out
  • 8. Connection to Other Distributions 4/12/2014Webinar for ASQ Reliability Division 8  When shape parameter = 1  Exponential distribution  When shape parameter is known  Let , then Y has an exponential distribution  Extreme value distribution  Concerns with the largest or smallest of a set of random variables  Let , then Y has a smallest extreme value distribution  Good for modeling “the weakest link in a system”  TY  TY log
  • 9. Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 9  Rectification of Weibull distribution  If we plot the right hand side vs. log failure time, then we have a straight line  The slope is the shape parameter  The intercept at t=1 is  Characteristic life  When the right hand side equals to 0, t=characteristic life  F(t)=1-1/e=0.63  At the characteristic life, the failure probability does not depend on the shape parameter    loglog))(1log(log  ttF  log
  • 10. Weibull Plot Example 4/12/2014Webinar for ASQ Reliability Division 10  A complementary log-log vs log plot paper  Estimate failure probability (Y) by median rank method  Regress X on Y  Find characteristic life and “B” life on the plot
  • 11. Complete Data 4/12/2014Webinar for ASQ Reliability Division 11  Order failure times from smallest to largest  Check median rank table for Y  Calculation of rank table uses binomial distribution  Y is found by setting the cumulative binomial function equal to 0.5 for each value of sequence number  Can be generated in Excel by BETAINV(0.5,J,N-J+1)  J is the rank order  N is sample size  By Bernard’s approximation Order number Failure time Median rank % (Y) 1 30 12.94 2 49 31.38 3 82 50.00 4 90 68.62 5 96 87.06 )4.0/()3.0(  NJY
  • 12. Censored Data 4/12/2014Webinar for ASQ Reliability Division 12  Compute reverse rank  Compute adjusted rank  Adjusted rank = (reverse rank * previous adjusted rank +N+1)/(reverse rank+1)  Find the median rank Rank Time Reverse rank Adjusted rank Median rank % 1 10S 8 Suspended 2 30F 7 1.125 9.8 3 45S 6 Suspended 4 49F 5 2.438 25.5 5 82F 4 3.750 41.1 6 90F 3 5.063 56.7 7 96F 2 6.375 72.3 8 100S 1 suspended
  • 13. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 13  Small sample uncertainty
  • 14. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 14  Low failure times
  • 15. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 15  Effect of suspensions
  • 16. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 16  Effect of outlier
  • 17. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 17  Initial time correction
  • 18. Diagnosis using Weibull Plot 4/12/2014Webinar for ASQ Reliability Division 18  Multiple failure modes
  • 19. Maximum Likelihood Estimation 4/12/2014Webinar for ASQ Reliability Division 19  Maximum likelihood estimation (MLE)  Likelihood function  Find the parameter estimate such that the chance of having such failure time data is maximized  Contribution from each observation to likelihood function  Exact failure time  Failure density function  Right censored observation  Reliability function  Left censored observation  Failure function  Interval censored observation  Difference of failure functions )(tR )(tF )()(   tFtF )(tf
  • 20. Plot by Software 4/12/2014Webinar for ASQ Reliability Division 20  Minitab  Stat  Reliability/Survival  Distribution analysis  Parametric distribution analysis  JMP  Analyze  Reliability and Survival  Life distribution  R  Needs R codes such as  data <- c(….)  n <- length(data)  plot(data, log(-log(1-ppoints(n,a=0.5))), log=“x”, axes=FALSE, frame.plot=TRUE, xlab=“time”, ylab=“probability”)  Estimation of scale and shape parameters can also be found by  res <- survreg(Surv(data) ~1, dist=“weibull”)  theta <- exp(res$coefficient)  alpha <- 1/res$scale
  • 21. Compare to Other Distributions 4/12/2014Webinar for ASQ Reliability Division 21  Choose a distribution model  Fit multiple distribution models  Criteria (smaller the better)  Negative log-likelihood values  AICc (corrected Akaike’s information criterion)  BIC (Baysian information criterion)
  • 22. Weibull Regression 4/12/2014Webinar for ASQ Reliability Division 22  When there is an explanatory variable (regressor)  Stress variable in the accelerated life testing (ALT) model  Shape parameter of Weibull distribution is often assumed fixed  Scale parameter is changed by regressor  Typically a log-linear function is assumed  Implementation in Software
  • 23. Final Remarks 4/12/2014Webinar for ASQ Reliability Division 23  Weibull distribution  2 parameters  3 parameters  Shape of hazard function  Different stages of bathtub curve  Weibull plot  Find the parameter estimation  Interpretation