William Q. Meeker, Distinguished Professor of Statistics, Iowa State University
Peng Liu, Principal Research Statistician Developer, JMP
Degradation data analysis is used to assess the reliability, failure-time distribution or shelf-life distribution of many different kinds of products including lasers, LEDs, batteries, and chemical and pharmaceutical products. Modeling degradation processes shines a light on the underlying physical-chemical failure-mechanisms, providing better justification for the extrapolation that is needed in accelerated testing. Additionally, degradation data provides much richer information about reliability, compared to time-to-event data. Indeed, by using appropriate degradation data, it is possible to make reliability inferences even if no failures have been observed.
Degradation data, however, bring special challenges to modeling and inference. This talk describes the new Repeated Measures Degradation platform in JMP 17, which uses state-of-the-art Bayesian hierarchical modeling to estimate failure-time distribution probabilities and quantiles. The methods we present are better grounded theoretically when compared to other existing approaches. Besides advantages, Bayesian methods do pose special challenges, such as the need to specify prior distributions. We outline our recommendations for this important step in the Bayesian analysis workflow. In this talk, we guide the audience through this exciting and challenging new approach, from theory to model specifications and, finally, the interpretation of results. The presenters conclude the talk with a live demonstration.
