Accelerated Degradation Testing With Long-Term Memory Effects

The accelerated degradation testing (ADT) has been widely applied as an efficient strategy to obtain the reliability (life) information of the assets in a shorter-than-normal period of time by exposing the assets to higher-than-normal stresses. Recently, with advances in the sensor technology, it has been revealed that the degradation of some assets demonstrates a long-term memory effect, which implies that the future degradation process not only depends on the current degradation state but also strongly correlates with the past degradation history across a long period of time, and the degradation increments are correlated for nonoverlapping time intervals. The existing ADT methods do not consider the long-term memories, which could lead to biased life testing results. In this article, we propose a novel ADT model by integrating the long-term degradation memory effect based on a utilization of the fractional Brownian motion. A maximum likelihood approach is developed to estimate the model parameters. A likelihood-ratio hypothesis test is designed to test the existence of long-term memories. Simulation studies are implemented to illustrate the developed methods. Physical experiments on accelerated testing of a atalyst are designed and conducted to demonstrate the proposed model and its advantage over benchmark approaches. The results show that the traditional ADT paradigm, which ignores the long-term memories, significantly underestimates asset lifetime uncertainties.