Statistical Analysis of Modern Reliability Data

TL;DR

This paper reviews recent statistical methods for reliability analysis that incorporate rich covariate data from modern sensor technology, enhancing prediction accuracy and test planning strategies.

Contribution

It introduces new statistical methods tailored for reliability data with covariates and demonstrates their application through industry examples and a Bayesian test planning approach.

Findings

Enhanced reliability prediction using covariate data

Development of specific statistical methods for different data types

Application of Bayesian design in fatigue testing

Abstract

Traditional reliability analysis has been using time to event data, degradation data, and recurrent event data, while the associated covariates tend to be simple and constant over time. Over the past years, we have witnessed the rapid development of sensor and wireless technology, which enables us to track how the product has been used and under which environmental conditions it has been used. Nowadays, we are able to collect richer information on covariates which provides opportunities for better reliability predictions. In this chapter, we first review recent development on statistical methods for reliability analysis. We then focus on introducing several specific methods that were developed for different types of reliability data with covariate information. Illustrations of those methods are also provided using examples from industry. Test planning is also an important part of reliability analysis. In addition to data analysis, we also provide a briefly review on recent developments of test planning and then focus on illustrating the sequential Bayesian design with an example of fatigue testing for polymer composites. The paper is concluded with some discussions and remarks.