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8.
Assessing Product Reliability
8.4. Reliability Data Analysis
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| Several methods for comparing reliability between populations are described |
Comparing reliability among populations based on samples of failure data usually means asking whether the samples came from populations with the same reliability function (or CDF). Three techniques already described can be used to answer this question for censored reliability data. These are:
Assume each sample is a random sample from possibly a different lot, vendor or production plant. All the samples are tested under the same conditions. Each has an observed proportion of failures on test. Call these sample proportions of failures \(p_1, \, p_2, \, p_3, \, \ldots, \, p_n\). Could these all have come from equivalent populations? This is a question covered in Chapter 7 for two populations, and for more than two populations, and the techniques described there apply equally well here. Likelihood Ratio Test Comparisons The Likelihood Ratio test was described earlier. In this application, the Likelihood ratio \(\lambda\) has as a denominator the product of all the Likelihoods of all the samples assuming each population has its own unique set of parameters. The numerator is the product of the Likelihoods assuming the parameters are exactly the same for each population. The test looks at whether \(-2\mbox{ ln } \lambda\) is unusually large, in which case it is unlikely the populations have the same parameters (or reliability functions). This procedure is very effective if, and only if, it is built into the analysis software package being used and this software covers the models and situations of interest to the analyst. Lifetime Regression Comparisons Lifetime regression is similar to maximum likelihood and likelihood ratio test methods. Each sample is assumed to have come from a population with the same shape parameter and a wide range of questions about the scale parameter (which is often assumed to be a "measure" of lot-to-lot or vendor-to-vendor quality) can be formulated and tested for significance. For a complicated, but realistic example, assume a company manufactures memory chips and can use chips with some known defects ("partial goods") in many applications. However, there is a question of whether the reliability of "partial good" chips is equivalent to "all good" chips. There exists lots of customer reliability data to answer this question. However the data are difficult to analyze because they contain several different vintages with known reliability differences as well as chips manufactured at many different locations. How can the partial good vs all good question be resolved? A lifetime regression model can be constructed with variables included that change the scale parameter based on vintage, location, partial versus all good, and any other relevant variables. Then, a good lifetime regression program will sort out which, if any, of these factors are significant and, in particular, whether there is a significant difference between "partial good" and "all good". |
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