Repeated degradation data measures the reliability over time of a product such as propagation of cracks in a material, decrease in output of power amplifiers or other performance measurement changes over time on individual subjects. The goal of degradation modeling and analysis is to predict the failure times and probabilities for the product under study.
Beginning with JMP 17, JMP offers two different approaches for repeated measures degradation data analysis and modeling: 1) pseudo-failure approach that has been available in JMP Degradation platform for several releases and 2) a new Repeated Measures Degradation platform that implements the hierarchical Bayesian modeling for reliability discussed in Meeker, Escobar and Pascual (2022).
The hierarchical Bayesian modeling approach enables you to estimate degradation models with random parameters with Bayesian inference and find failure times through Monte Carlo simulation. It fits degradation path models with random parameters, whereas the pseudo-failure approach approach fits fixed parameter models. The hierarchical Bayesian modeling simulates the failure times using the posterior estimates of population parameters of the degradation path model, whereas the pseudo-failure approach extrapolates the pseudo failure times from the fixed parameter path model.
In general, use the new Repeated Measures Degradation when one of its supported models is appropriate. If you don't see any models in the new platform appropriate for your data, you should consider using the pseudo-failure approach in the existing Degradation platform, where you can define your own model.
See a video that gives three case studies using JMP Sample data (GaAs Laser.jmp, Alloy A.jmp and Device B.jmp respectively) found in the Reliability folder of your JMP Sample Data.
Question and answers are included after each case study. Case Study 1 begins at ~Time 24:00 , Cast Study 2 begins at ~ Time 57:00 and Case Study 3 begins at ~ Time 1:04:00.
