Stochastic degradation models with several accelerating variables

Many products & systems age, wear, or degrade over time before they fail or break down. Thus, in many engineering reliability experiments, measures of degradation or wear toward failure can often be observed over a period of time before failure occurs. Because the degradation values provide additional information beyond that provided by the failure observations, both sets of observations need to be considered when doing inference on the statistical parameters of the product or system lifetime distributions. For highly-reliable modern products, it often takes much more time to obtain lifetime & degradation data under usual use conditions, and this requires one to use accelerated tests. Accelerated tests expose the products to greater environmental stress levels so that we can obtain lifetime & degradation measurements in a more timely fashion. In addition, many products are exposed to several environmental variables in some manufacturing processes, or under some operating conditions. This motivates the need for developing general accelerated test models with several accelerating variables for inference based on both observed failure values, and degradation measurements. In this paper, new accelerated test models are developed based on a generalized cumulative damage approach with a stochastic process characterizing a degradation phenomenon.