A prognostic approach for systems subject to wiener degradation process with cumulative-type random shocks

Condition monitoring data have been widely used to evaluate the health state and reliability, as well as estimate the remaining useful life (RUL) for degrading systems. Among various degradation modeling and RUL estimating methods, Wiener process based models is recognized by both scholars and engineers as the one of the most effect tools, and thus becomes very popular nowadays. In this paper, a prognostic approach is developed for degrading systems whose performance evolution is captured by an integration of Wiener process and cumulative-type of random shocks depicted by a compound Poisson pro-cess. Under the concept of first hitting time, an approximate lifetime distribution in analytical form, which greatly reduces the computing time while proving an adequately accurate result, has been formulated. The parameter estimation framework based expectation maximization (EM) algorithm has been derived. Simulations and cases study of the degradation of Li-ion batteries are executed to illustrate and validate the proposed method. The results demonstrate that the approach in this paper can not only handle the positive shocks but also process the negative shocks, which outperforms most of the existing models.