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RESEARCH PAPER
Dynamic grouping maintenance optimization by considering the probabilistic remaining useful life prediction of multiple equipment
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Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China
 
 
Submission date: 2023-12-16
 
 
Final revision date: 2024-02-26
 
 
Acceptance date: 2024-04-21
 
 
Online publication date: 2024-05-11
 
 
Publication date: 2024-05-11
 
 
Corresponding author
Xinyu Shi   

Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58 Yanta Road, Beilin District, 710054, Xi’an Shaanxi, China
 
 
 
HIGHLIGHTS
  • The probabilistic RUL prediction is obtained by using LSTM and VAE resampling.
  • A multi-equipment dynamic grouping maintenance model is established.
  • The gazelle optimization algorithm is used to solve the optimization model.
  • The effectiveness of the proposed method is verified by the numerical case with 6 wind turbines.
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ABSTRACT
For multi-equipment maintenance of modern production equipment, the economic correlation and degradation uncertainty may lead to insufficient or excessive maintenance, increasing maintenance costs. This paper proposes a dynamic grouping maintenance method based on probabilistic remaining useful life (RUL) prediction for multiple equipment. Long short term memory (LSTM) is developed to predict the equipment probability RUL by the Variational Auto-Encoder (VAE) resampling. Then, the dynamic grouping maintenance model is constructed to minimize the maintenance cost rate under the known probabilistic RUL information. The gazelle optimization algorithm (GOA) is used to determine the optimal maintenance time for each equipment. To better verify the effectiveness of the proposed method, a numerical case with six wind turbines is introduced to analyse the performance of GOA. Moreover, the advantages of dynamic grouping maintenance is verified by comparing with independent maintenance, whose maintenance cost rate is reduced by 10.01%.
eISSN:2956-3860
ISSN:1507-2711
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