Non-destructive 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 strength deterioration of an adhesive bond. 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 degradation data analysis and modeling: 1) Repeated Measures that has been available in JMP for several releases and 2) a new Repeated Measures Degradation capability that implements the Bayesian statistical methods for reliability discussed in Meeker, Escobar and Pascual (2022).
The Repeated Measures Degradation 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 Degradation platform fits fixed effects models. Repeated Measures Degradation simulates the failure times using the posterior estimates of population parameters of the degradation path model, whereas the Repeated Measures extrapolates the pseudo failure times from the fixed effects path model.
In general, use Repeated Measures Degradation when you have at least two modeling uncertainties, when you want to extend it to include your own models, and when the models in Repeated Measures Degradation are appropriate for your problem. Do not use it when one of the Repeated Measures Degradation models in not appropriate for your problem.
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.
