Analysis of warranty data with covariates

Abstract

The reliability characteristics of automobile components depend on factors or covariates such as the automobile operating environment (e.g. temperature, rainfall, humidity, etc.), usage conditions, manufacturing periods, types of automobile that use the components, etc. In recent years, many automotive manufacturing companies utilize the warranty database as a very rich source of field reliability data that provides valuable information on such covariates for feedback to new product development systems on product performance in actual usage conditions. In the warranty database, the information on those covariates is known for the components that fail within the warranty period and are unknown for the censored components. This article considers covariates associated with some reliability-related factors and presents a Weibull regression model for the lifetime of the component as a function of such covariates. The expectation maximization (EM) algorithm is applied to obtain the ML estimates of the parameters of the model because of incomplete information on covariates. An example based on real field data of an automobile component is given and simulation studies are conducted to illustrate the use of the proposed method.