Abstract:

In classical life data analysis, one typically collects failure-time data by operating a set of units under usual (or design) stress conditions. But in reliability engineering, due to a variety of reasons such as cost and time constraints, one often wishes to collect the data more quickly than is allowed under the normal operating conditions. This can be achieved by applying higher-than-usual levels of stresses to the units, resulting in accelerated life testing data. In this paper, we provide a short review of the methods and models used to analyze such data. We concentrate on accelerated failure time models and on the related statistical inference. We describe some open questions and future research directions.