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

• Autorzy: Cao Xiangang , Shi Xinyu , Zhao Jiangbin , Duan Yong , Yang Xin

Identyfikatory

DOI

10.17531/ein/187793

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

long short term memory; probabilistic remaining useful life prediction; Variational Auto-Encoder; Dynamic grouping maintenance; gazelle optimization algorithm

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 3
• Strony: art. no. 187793
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Cao Xiangang

• Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China

• https://orcid.org/0000-0003-2338-2497

autor

Shi Xinyu

• Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China

• https://orcid.org/0009-0006-3171-9191

autor

Zhao Jiangbin

• Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China

• https://orcid.org/0000-0002-3034-6710

autor

Duan Yong

• Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China

• https://orcid.org/0000-0003-0625-3570

autor

Yang Xin

• Department of Intelligence Manufacturing School of Mechanical Engineering, Xi’an University of Science and Technology, China

• https://orcid.org/0009-0006-9537-126X

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).