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Weibull analysis is the world's most popular method of analyzing and predicting failures and malfunctions of all types. About 85%-95% of all life data are adequately described with Weibull probability plots whereas about 85%-95% of all repair times are adequately described by log normal probability plots—thus the methods provide practical tools for analyzing life/repair data.
The Weibull method identifies the category of failure: infant mortality, random or wear out. Weibull analysis provides the quantitative information needed for making RCM decisions which are often made from a qualitative approach. Weibull failure predictions are accurate even with very small samples of data. Weibull analysis calculates optimal parts replacement intervals for minimizing cost. Weibull analysis is needed for making risk based inspection decisions to take action or defer action on potential failures.
Crow-AMSAA charts, a different use of Weibull analysis, predict future failures, analyze R&D testing, track fleets of repairable systems, and provide the most accurate trending of significant events for management. Both techniques may show either events or cost analysis. Crow-AMSAA numbers tell if failure rates are increasing, decreasing, or staying the same with time. Crow-AMSAA plots are also useful for forecasting safety issues concerning future safety incidents, spills, and other controllable events. The Crow-AMSAA plots are useful tools for analyzing mixed failure modes and predicting future failures.
If you already own the 4th edition, why purchase the 5th edition? Weibull analysis is becoming more sophisticated. Old ideas are being refined. New tools are introduced. Early numbers are being improved with new techniques. Best practices are improved and modified for more accurate analysis. More experts are providing thoughtful details to the technology through improved usage of computer analysis and simulations. Everything old is new and improved again!
New Additions Include:
- More accurate goodness of fit measures using the coefficient of determination, the likelihood ratio and Anderson-Darling methods are presented. A new appendix compares their "power," the probability of making the selecting the correct distribution.
- Grouped or interval data methods including the inspection option, the probit method and the interval MLE, are compared with simulation to determine best practices.
- Life cycle costs is added, written by Paul Barringer, using Weibull analysis including simulation.
- Warranty Analysis contributed by Joe Weuben of Honda using warranty claims matrixes.
- Weibull libraries with automatic filing contributed by Randy Pirtle of Honeywell.
- Revised Best Practices
Rewritten Sections Include:
- Improved Crow-AMSAA analysis based on accuracy comparisons between regression and MLE and also, critical correlation coefficients for measuring the goodness of fit of the plotted data. The analysis may be done on failure events, warranty claims, or cost using the latest IEC standards.
- Reduced Bias Estimation for small sample sizes for "Now Risk" producing better tests for batch problems.
- Small samples reduced bias adjustments (RBA) for maximum likelihood estimates for the Weibull, normal and log normal istributions providing significant improvements in accuracy. Maximum likelihood estimates can now be made for small samples as accurately as median rank regression.
- Dr. Wayne Nelson's new methodology: graphical repair analysis.
- There are many methods and types of analysis described in The New Weibull Handbook. How can the novice analyst decide which is the most appropriate procedure for his problem? Best practices are indicated throughout the text.
- The logic diagram developed by Bob Rock of PACCAR, has been improved to provide step-by-step guidance to select the best practice methods for all types of data sets.
- New case studies have been added.
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