A prognosis approach for systems with Alternative Degradation and Recovery

There are various industrial products whose performances deteriorate with time yet recovers under some special conditions. However, prognosis approaches addressing degradation process including recoveries are rarely mentioned by the existing literature. In this paper, an approach is presented to estimate the remaining useful life (RUL) of systems with alternative degradations and recoveries, which are related to the switching state transitions during the system's operation process. To depict the stochastic characteristic of the operating process, a continuous-time Markovian model (CTMM) is constructed and incorporated into the RUL estimate procedure. After identifying the degradation, recovery models and the operating model based on the available historical information, RUL distribution is obtained through a Monte-Carlo simulation based algorithm. The proposed approach differs from other degradation modeling based methods in that the influence of recoveries on the degradation paths has been considered. At the end of the paper, a case study of the aging of Li-ion batteries has also been provided to illustrate and demonstrate the proposed approach.