RESEARCH PAPER
Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
| 1 | School of Computer Science, Hubei University of Technology, Wuhan, Hubei, 430068, PR China |
| 2 | Lublin University of Technology, ul. Nadbystrzycka 36, 20-618, Lublin, Poland |
| 3 | Wuhan Fiberhome Technical Services Co., Ltd., Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China |
| 4 | Lviv Polytechnic National University, Karpinskoho str. 1, Lviv, Ukraine |
Publication date: 2020-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(4):730–740
HIGHLIGHTS
- A fault diagnosis method based on LMFO, ensures high classification accuracy and better efficiency.
- EEMD-based feature extraction method effectively removes signal noise.
- The feature selection method based on LMFO effectively removes feature redundancy.
- The NB-based fault diagnosis method ensures accuracy with high efficiency.
KEYWORDS
malfunction diagnosticsnaive Bayesmoth-flame optimization algorithmensemble empirical mode decomposition
ABSTRACT
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.
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