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Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate
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Publication date: 2023-02-01
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(1):20
  • Combining physical degradation models with BP-LSTM deep learning models for predicting equipment failure moments.
  • Dynamic failure rate thresholds are determined strictly based on historical equipment failure data and merit-seeking guidelines.
  • GPSO is used to solve the dynamic equation seeking problem.
  • Example of a preventive maintenance strategy realized for a coating machine
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.