- Tytuł:
- Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
- Autorzy:
-
Sun, Shuang
Przystupa, Krzysztof
Wei, Ming
Yu, Han
Ye, Zhiwei
Kochan, Orest - Powiązania:
- https://bibliotekanauki.pl/articles/1841936.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
malfunction diagnostics
naive Bayes
moth-flame optimization algorithm
ensemble empirical mode decomposition - Opis:
- 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.
- Źródło:
-
Eksploatacja i Niezawodność; 2020, 22, 4; 730-740
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł