This course is for anyone who needs to analyze data about how long an object (reliability) or person (survival) operates within acceptable parameters ('time to event'). The course is presented using manufacturing examples, but those interested in survival analysis or studying recidivism will also find the course useful. Learn how to

• distinguish unique characteristics of life data
• compute non-parametric (Kaplan-Meier product-limit) estimates of failure probability
• fit distribution models specific to life data
• estimate reliability or survival measures and hazard
• estimate survival in the presence of competing causes
• use parametric survival models to estimate effects of covariates or experimental factors
• design an accelerated life test.

Who should attend Engineers, scientists, researchers, and analysts who perform reliability, survival, or recidivism studies Formats available Duration
: 2 half-day session(s)

Before attending this course, it is recommended you complete the and courses or have equivalent experience.

This course addresses JMP software.

Introduction to Reliability

• understanding principles of reliability, measures of reliability, and the nature of life data and censored observations
• using nonparametric estimation of failure probabilities
• applying parametric models of failure reliability and hazard
• accounting for uncertainty about reliability estimates
• determine sufficient sample size for reliability determination

Reliability with Factors

• identifying failure modes and competing causes
• fitting mixture models
• fitting competing risk mixture models
• describing deficient data
• including the effect of covariates in the reliability model

Rapid Reliability

• understanding stress factors and accelerated failures
• computing acceleration factors
• using acceleration relationships
• designing accelerated life tests
• performing degradation analysis for repeated measures and destructive tests

JREL13