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Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm

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Abstract

The costs of preventive maintenance have been extensively studied by scholars across all preventive optimization model disciplines. However, one phenomenon fails to be fully studied: breakdown and breakdown maintenance costs. We set out to fill this gap in this study. This study considered the cost of equipment preventive maintenance, and the breakdown maintenance cost caused by an accidental breakdown. In order to more accurately establish the reliability model of equipment breakdown, the three-parameter Weibull distribution was applied to set up the reliability model of equipment and the differential evolution algorithm was adopted to optimize the parameters. On this basis, preventive maintenance was regarded as imperfect maintenance in the study of preventive maintenance strategies for single equipment. In consideration of the combined influence of preventive maintenance and breakdown maintenance, a maintenance strategy in which preventive maintenance times N served as the decision variable was obtained to build a mathematical model on benefit expectation of single equipment in unit time. Based on the research of single equipment, a further study was performed on the multi-equipment series system. Moreover, two strategies were given, both of which use preventive maintenance times N as the decision variable. The first strategy is to take the average cost rate of the system under long-term operation as the optimization objective, while the second strategy is to apply single component maintenance strategy in series system. In the numerical example study, a series manufacturing system composed of two devices was chosen as the research object. Interestingly, we discussed the effect of initial conditions of two-parameter DE on output results. After acquiring the optimal initialization parameters, the failure rate function was achieved by the DE to estimate the parameters of three-parameter Weibull distribution. Meanwhile, the maintenance times N was optimized according to the two strategies respectively. The best result were selected from the results of both strategies based on availability. Thus, the validity and practicability of the proposed research methods are verified.

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Authors and Affiliations

  1. School of Intelligent Manufacturing, Changchun Sci-Tech University, Changchun, 130600, China

    Xiaofeng Wang, Shu Guo, Jian Shen & Yang Liu

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  1. Xiaofeng Wang
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  2. Shu Guo
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  3. Jian Shen
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  4. Yang Liu
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Correspondence to Shu Guo.

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Wang, X., Guo, S., Shen, J. et al. Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm. J Intell Manuf 31, 745–757 (2020). https://doi.org/10.1007/s10845-019-01475-y

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