Search

As the essential component of prognostic and health management (PHM), life prediction for equipment plays a more and more significant role in recent years. However, current studies cannot fully consider the influence of imperfect maintenance activities that the equipment may experience on the degradation process and prognostic result. In this paper, we propose a degradation model subjected to the influence of imperfect maintenance for life prediction. Firstly, the multi-stage Wiener process is employed to characterize the influence of imperfect maintenance activities on the degradation level and degradation rate. Then, the theoretical expression of life probability distribution is derived under the concept of first hitting time using the convolution operation, and the approximate expression of life probability distribution is evaluated by the Monte Carlo simulation algorithm. Furthermore, we utilize the maximum likelihood estimation (MLE) to estimate unknown parameters in the concerned model. Finally, a numerical example and a practical case study are provided to substantiate the practicality and effectiveness of the newly proposed life prediction method. The results indicate that the proposed model can guarantee that the relative error (RE) is almost below 5%. [ABSTRACT FROM AUTHOR]

Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime's closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on Expectation Maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration. [ABSTRACT FROM AUTHOR]