Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance

Abstract

When existing methods consider the impact of the imperfect maintenance activities on random degraded equipment, they usually assume that the degraded equipment is linear, and set the number of imperfect maintenance activities in advance. However, in engineering practice, most of the degraded equipment is nonlinear, and the number of imperfect maintenance activities has an impact on the development of maintenance strategies. Therefore, this paper proposes a new joint maintenance strategy considering the uncertainty of the number of imperfect maintenance activities for nonlinear degraded equipment. First, nonlinear degradation data are linearized based on Box–Cox transformation (BCT), and the degradation model under the influence of imperfect activities is constructed by the random coefficient regression model. Accordingly, the remaining useful life (RUL) probability distribution can be derived. Secondly, the number of maintenances is calculated by imperfect maintenance level and success probability. Then, the detection cycle, preventive maintenance threshold and maintenance times are taken as decision variables. The optimization objective is to minimize the expected average cost. However, this is restricted by availability and probability of success for imperfect maintenance activities. A multi-objective joint optimization model of condition-based maintenance and spare parts ordering is constructed. Finally, the analysis results based on numerical examples verify the feasibility of the proposed joint optimization strategy.

Dynamic grouping maintenance optimization by considering the probabilistic remaining useful life prediction of multiple equipment

“…(2) Train the VAE model. Map it to the latent space (the latent space is assumed to obey a prior distribution, and a normal distribution is chosen in this paper) via the encoder 𝑞 ∅ (𝑧|𝑥), and compute the mean 𝜇 𝑥 and the variance parameter 𝜎 𝑥 2 for the latent space of each input variable;…”

“…Currently, researchers have done a lot of research on maintenance. There are many maintenance decision-making methods, including condition-based maintenance [2], maintenance based on degenerate models such as nonlinear Wiener process [3], Markov process [4], gamma process [5].…”

“…For equipment maintenance modelling, many scholars have also done much research in the past. Many of which are based on physical models [3][5] [12], and there have also been studies on data prediction-based maintenance schedules [2][13] [14].…”