A multi-stage Wiener process-based prognostic model for equipment considering the influence of imperfect maintenance activities

Abstract

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%.

Keywords

Life prediction imperfect maintenance multi-stage Wiener process Monte Carlo maximum likelihood estimation

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