基于动态EM-SHSMM的异常数据下设备健康预测研究 postprint

Abstract: Aiming at the problem of remaining useful life prognosis under abnormal data during equipment degradation, this paper developed a prognostic method based on dynamic expectation maximization (EM) -segmented hidden semi-Markov model (SHSMM) . First, based on the SHSMM model framework, it used the expectation maximization algorithm to estimate the unknown parameters of the model. Secondly, to process the anomaly data in the samples, it proposed a dynamic forward-backward gray-fill algorithm based on WGM (1, 1) , and it carried out the equipment health prognosis. Finally, it used a case study to evaluate the performance of the model. The results show that the proposed method could effectively solve the problem of abnormal data.