Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network
As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/5005263 |
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| _version_ | 1832555195130707968 |
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| author | Li-Hong Guo Lai-Ming Yang Yan-Feng Peng Yong Guo |
| author_facet | Li-Hong Guo Lai-Ming Yang Yan-Feng Peng Yong Guo |
| author_sort | Li-Hong Guo |
| collection | DOAJ |
| description | As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification. |
| format | Article |
| id | doaj-art-a11d08f9ab874b5eb737ef61e794d1b2 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a11d08f9ab874b5eb737ef61e794d1b22025-02-03T05:49:25ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/5005263Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural NetworkLi-Hong Guo0Lai-Ming Yang1Yan-Feng Peng2Yong Guo3Five Meters Wide Thick Plate FactoryFive Meters Wide Thick Plate FactoryHunan Provincial Key Laboratory of Health Maintenance for Mechanical EquipmentHunan Provincial Key Laboratory of Health Maintenance for Mechanical EquipmentAs the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.http://dx.doi.org/10.1155/2022/5005263 |
| spellingShingle | Li-Hong Guo Lai-Ming Yang Yan-Feng Peng Yong Guo Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network Shock and Vibration |
| title | Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network |
| title_full | Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network |
| title_fullStr | Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network |
| title_full_unstemmed | Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network |
| title_short | Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network |
| title_sort | fault identification of low speed hub bearing of crane based on mbmd and bp neural network |
| url | http://dx.doi.org/10.1155/2022/5005263 |
| work_keys_str_mv | AT lihongguo faultidentificationoflowspeedhubbearingofcranebasedonmbmdandbpneuralnetwork AT laimingyang faultidentificationoflowspeedhubbearingofcranebasedonmbmdandbpneuralnetwork AT yanfengpeng faultidentificationoflowspeedhubbearingofcranebasedonmbmdandbpneuralnetwork AT yongguo faultidentificationoflowspeedhubbearingofcranebasedonmbmdandbpneuralnetwork |