Prediction of Future Failures for Heterogeneous Reliability Field Data

TL;DR

This paper develops Bayesian hierarchical methods to accurately predict future failures in heterogeneous reliability data, especially when data is sparse or censored, by borrowing information across subpopulations.

Contribution

It introduces a novel Bayesian hierarchical approach for constructing prediction bounds for failure counts in heterogeneous reliability data with limited observations.

Findings

Method provides stable failure predictions with limited data.

Prediction intervals achieve good coverage in simulations.

Applications demonstrate practical utility of the approach.

Abstract

This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some combination of small sample sizes, censoring, and truncation. In such cases, we use a Bayesian hierarchical model to model jointly multiple lifetime distributions arising from different subpopulations of similar products. By borrowing information across subpopulations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Three applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.