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Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.
Rocznik
Strony
atr. no. 20
Opis fizyczny
Bibliogr. 40 poz., rys., tab., wykr.
Twórcy
autor
  • School of Mechatronic Engineering, Changchun University of technology, Changchun, China
autor
  • School of Mechatronic Engineering, Changchun University of technology, Changchun, China
autor
  • School of Mechatronic Engineering, Changchun University of technology, Changchun, China
autor
  • School of Mechatronic Engineering, Changchun University of technology, Changchun, China
autor
  • School of Mechatronic Engineering, Changchun University of technology, Changchun, China
Bibliografia
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  • 22. Mlynarski S, R Pilch, M Smolnik, J Szybka, G Wiazania. A MODEL OF AN ADAPTIVE STRATEGY OF PREVENTIVE MAINTENANCE OF COMPLEX TECHNICAL OBJECTS. Eksploatacja I Niezawodnosc-Maintenance and Reliability 2020; 22(1):35-41, https://doi.org/10.17531/ein.2020.1.5.
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  • 34. Wu ZY, B Guo, Axita, X Tian, LJ Zhang. A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process. IEEE Access 2020; 8:104-117, https://doi.org/10.1109/ACCESS.2019.2958137.
  • 35. Xin JY, M Akiyama, DM Frangopol, MY Zhang. Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks. Structure and Infrastructure Engineering 2022; 18(7):1002-1019, https://doi.org/10.1080/15732479.2022.2038641.
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  • 37. Yang XZ, YH He, D Zhou, X Zheng. Mission reliability-centered maintenance approach based on quality stochastic flow network for multistate manufacturing systems. Eksploatacja I Niezawodnosc-Maintenance and Reliability 2022; 24(3):455-467, https://doi.org/10.17531/ein.2022.3.7.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eac8edc3-36b0-48e6-a520-3511dabef4da
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