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Abstrakty
Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.
Czasopismo
Rocznik
Tom
Strony
730--740
Opis fizyczny
Bibliogr. 50 poz., rys., tab.
Twórcy
autor
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei, 430068, PR China
autor
- Lublin University of Technology, ul. Nadbystrzycka 36, 20-618, Lublin, Poland
autor
- Wuhan Fiberhome Technical Services Co., Ltd., Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China
autor
- Wuhan Fiberhome Technical Services Co., Ltd., Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China
autor
- School of Computer Science, Hubei University of Technology, Wuhan, Hubei, 430068, PR China
autor
- Lviv Polytechnic National University, Karpinskoho str. 1, Lviv, Ukraine
Bibliografia
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a4f6c24-7304-48df-af51-d503a9c7c9de