Reliability estimation considering multi-stress monotonic degradation test data with non-constant scale parameter

Abstract

The accelerated degradation testing (ADT) has been proven to be effective in quickly assessing the reliability of a highly reliable product, especially during product design and development stage. However, the modeling of ADT data to estimate reliability at normal operating conditions is still a challenging task. It becomes even more complex when there are more than one accelerating factors. In this study, a nonstationary gamma process is considered to model the degradation behavior assuming that the nature of product deterioration is strictly monotonic and non-negative. It further assumes that both gamma process parameters are stress-dependent while considering multiple stresses and their interaction effects. To deal with a problem of computational complexity caused by the consideration of multiple stresses and parameter dependency, a maximum likelihood method has been used for the model parameter estimation. The lifetime and reliability parameters under normal operating conditions are estimated using the acceleration models. A Monte Carlo simulation with the Bayesian updating method has been incorporated to update the gamma parameters when new degradation data become available. A carbon-film resistor degradation data is employed to demonstrate the efficacy of the proposed reliability assessment methodology.