Statistical Inference for Lifetime Delayed Degradation Process with Gamma Process

Degradation process has been successfully applied in modeling the degradation failure modes like wearout and corrosion for reliability assessment. However, there are several degradation failure modes due to different physical mechanisms with different products operated under various environments. Therefore, non-standard circumstances also make great influence on the process of degradation process. A typical failure phenomenon suits degradation failure mode is composed of hard and soft ones sequentially. Based on the phenomenon of this kind of failure mode, the whole failure process can be separated by initiation stage and the degradation stage. Intuitively, a lifetime delayed degradation process (LDDP) would provide a general framework for such typical mixture failure mode composed of hard and soft ones sequentially. In this study, a general reliability inference approach has been proposed for the LDDP based on the parameter estimation of maximum likelihood method, with numerical simulations and practical application with real testing data.