Abstract
Focusing on the fact that the existing research on optimal maintenance decision for remaining useful lifetime (RUL) prediction and imperfect maintenance has low accuracy of RUL prediction and rationality of decision results, an optimal maintenance decision method based on RUL prediction for the equipment subject to imperfect maintenance is proposed in this paper. Firstly, the nonlinear Wiener process is used to characterize the degradation law of the equipment. Secondly, the imperfect maintenance model that meets the upper limit of the maintenance number is established based on the nonhomogeneous Poisson process. Then, based on the concept of the first hitting time, the probability density function (PDF) of the RUL is derived. Finally, based on the RUL prediction results, the optimal maintenance decision model for the equipment subject imperfect maintenance is constructed. Through the example verification and cost parameter sensitivity analysis, the proposed method can effectively improve the accuracy of the RUL prediction and the scientific of maintenance decision results, which has engineering application value.
Highlights
Since the industrial revolution, production has improved dramatically in terms of technology and automation levels, and traditional manufacturing enterprises are facing more challenges than ever
prognostics and health management (PHM) has two core elements, namely, prediction of the equipment remaining useful lifetime (RUL) and condition-based maintenance (CBM) of the equipment based on the RUL prediction
CBM can be categorized into three types: perfect maintenance (PM), imperfect maintenance (IM), and minimal maintenance (MM) [11]–[13]
Summary
Production has improved dramatically in terms of technology and automation levels, and traditional manufacturing enterprises are facing more challenges than ever. In view of the above problems, assuming that the PvM threshold is unknown, Wang et al [21] modeled the cumulative effect of IM actions using a nonlinear Wiener process and a homogeneous Poisson process and improved RUL prediction accuracy This method did not study the maintenance decision of the equipment. By introducing an upper limit constraint for the number of IM activities, the degradation pattern of the equipment subject to IM is depicted accurately, and RUL prediction accuracy for the equipment subject to IM is effectively improved On this basis, an optimal maintenance decision model that accounts for RUL predictions is constructed using the renewal reward theorem. Di is the total number of occasions of IM to which the ith equipment is subject
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