Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm

In the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for featur...

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Bibliographic Details
Published in:Chinese journal of mechanical engineering 2021-12, Vol.34 (1), p.1-10, Article 119
Main Authors: Xiao, Maohua, Zhang, Wei, Wen, Kai, Zhu, Yue, Yiliyasi, Yilidaer
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Beetle algorithm
BP neural network
Cluster analysis
Clustering
Decomposition
Electrical Machines and Networks
Electronics and Microelectronics
Energy value
Engineering
Engineering Thermodynamics
Extreme values
Fault detection
Fault diagnosis
Feature extraction
Frequencies
Heat and Mass Transfer
Instrumentation
Intelligent Manufacturing Technology
Machines
Manufacturing
Mechanical Engineering
Model accuracy
Neural networks
Original Article
Power Electronics
Processes
Rolling bearing
Signal processing
Theoretical and Applied Mechanics
Vector quantization
Vibration analysis
Wavelet analysis
Wavelet packet transform
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Summary:In the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for feature extraction of vibration signals, which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals, and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition. The features are visualized by the K-Means clustering method, and the results show that the extracted energy features can accurately distinguish the different states of the bearing. Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algorithm is proposed to identify the bearing faults. Compared with the Particle Swarm Algorithm, Beetle Algorithm can quickly find the error extreme value, which greatly reduces the training time of the model. At last, two experiments are conducted, which show that the accuracy of the model can reach more than 95%, and the model has a certain anti-interference ability.
ISSN:1000-9345
2192-8258
DOI:10.1186/s10033-021-00648-2