Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm
Abstract 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 f...
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oai:doaj.org-article:11a1eb1c7ca14b978561f57af6242e3e2021-12-05T12:03:38ZFault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm10.1186/s10033-021-00648-21000-93452192-8258https://doaj.org/article/11a1eb1c7ca14b978561f57af6242e3e2021-12-01T00:00:00Zhttps://doi.org/10.1186/s10033-021-00648-2https://doaj.org/toc/1000-9345https://doaj.org/toc/2192-8258Abstract 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.Maohua XiaoWei ZhangKai WenYue ZhuYilidaer YiliyasiSpringerOpenarticleRolling bearingBP neural networkBeetle algorithmWavelet packet transformOcean engineeringTC1501-1800Mechanical engineering and machineryTJ1-1570ENChinese Journal of Mechanical Engineering, Vol 34, Iss 1, Pp 1-10 (2021) |
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Rolling bearing BP neural network Beetle algorithm Wavelet packet transform Ocean engineering TC1501-1800 Mechanical engineering and machinery TJ1-1570 |
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Rolling bearing BP neural network Beetle algorithm Wavelet packet transform Ocean engineering TC1501-1800 Mechanical engineering and machinery TJ1-1570 Maohua Xiao Wei Zhang Kai Wen Yue Zhu Yilidaer Yiliyasi Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
description |
Abstract 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. |
format |
article |
author |
Maohua Xiao Wei Zhang Kai Wen Yue Zhu Yilidaer Yiliyasi |
author_facet |
Maohua Xiao Wei Zhang Kai Wen Yue Zhu Yilidaer Yiliyasi |
author_sort |
Maohua Xiao |
title |
Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
title_short |
Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
title_full |
Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
title_fullStr |
Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
title_full_unstemmed |
Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm |
title_sort |
fault diagnosis based on bp neural network optimized by beetle algorithm |
publisher |
SpringerOpen |
publishDate |
2021 |
url |
https://doaj.org/article/11a1eb1c7ca14b978561f57af6242e3e |
work_keys_str_mv |
AT maohuaxiao faultdiagnosisbasedonbpneuralnetworkoptimizedbybeetlealgorithm AT weizhang faultdiagnosisbasedonbpneuralnetworkoptimizedbybeetlealgorithm AT kaiwen faultdiagnosisbasedonbpneuralnetworkoptimizedbybeetlealgorithm AT yuezhu faultdiagnosisbasedonbpneuralnetworkoptimizedbybeetlealgorithm AT yilidaeryiliyasi faultdiagnosisbasedonbpneuralnetworkoptimizedbybeetlealgorithm |
_version_ |
1718372298009870336 |