Key fault propagation path identification of CNC machine tools based on maximum occurrence probability

• Autorzy: Zhang Yingzhi , Zhou Yutong , Yang Fang , Wang Zhiqiong , Sun Mo

Identyfikatory

DOI

10.17531/ein/169887

• Języki publikacji: EN

Abstrakty

EN

In order to revise the deviation caused by ignoring the dynamic character of fault propagation in traditional fault propagation path identification methods, a method based on the maximum occurrence probability is proposed to identify the key fault propagation path. Occurrence probability of fault propagation path is defined by dynamic importance, dynamic fault propagation probability and fault rate. Taking the fault information of CNC machine tools whichsubject to Weibull distribution as an example, this method has been proven to be reasonable through comparative analysis. Result shows that the key fault propagation path of CNC machine tools is not unique, but changes with time. Before 1000 hours, key fault propagation path is electrical component (E) to mechanical component (M); after 1000 hours, key fault propagation path is auxiliary component (A) to mechanical component (M). This change should be taken into account when developing maintenance strategies and conducting reliability analysis.

Słowa kluczowe

EN

fault propagation; CNC machine tool; key fault propagation path; improved Pagerank; fault propagation probability

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 3
• Strony: art. no. 169887
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Zhang Yingzhi

• Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130022
• School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

• https://orcid.org/0000-0002-2856-5156

autor

Zhou Yutong

• Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130022
• School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

autor

Yang Fang

• China FAW Group Co., LTD. NEV Inst., China

autor

Wang Zhiqiong

• Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130022
• School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

autor

Sun Mo

• Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130022
• School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).