Using degradation data to assess reliability: a case study on train wheel degradation

TLDR

Degradation experiments are used to estimate the lifetime distribution of highly reliable products, and when degradation can be linked to reliability, nonlinear regression models with random coefficients allow parameter estimates that yield the failure‑time distribution. This study compares numerical and approximate parameter‑estimation methods in a simulation of a simple linear degradation path and contrasts them with traditional failure‑time analysis. The comparison evaluates the mean‑squared error of the estimated 100th‑percentile lifetime for each approach and applies the methods to a real degradation data set. © 2008 John Wiley & Sons, Ltd.

Abstract

Abstract Degradation experiments are usually used to assess the lifetime distribution of highly reliable products, which are not likely to fail under the traditional life tests or accelerated life tests. In such cases, if there exist product characteristics whose degradation over time can be related to reliability, then collecting ‘degradation data’ can provide information about product reliability. In general, the degradation data are modeled by a nonlinear regression model with random coefficients. If we can obtain the estimates of parameters under the model, then the failure‐time distribution can be estimated. In order to estimate those parameters, three basic methods are available, namely, the analytical, numerical and the approximate. They are chosen according to the complexity of the degradation path model used in the analysis. In this paper, the numerical and the approximate methods are compared in a simulation study, assuming a simple linear degradation path model. A comparison with traditional failure‐time analysis is also performed. The mean‐squared error of the estimated 100pth percentile of the lifetime distribution is evaluated for each one of the approaches. The approaches are applied to a real degradation data set. Copyright © 2008 John Wiley & Sons, Ltd.