An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems
In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisens...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2023/6928871 |
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| _version_ | 1849700933247696896 |
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| author | Xianbin Sun Mohan Wang Bo Zhan Yuanyuan Xiong Wei Yu |
| author_facet | Xianbin Sun Mohan Wang Bo Zhan Yuanyuan Xiong Wei Yu |
| author_sort | Xianbin Sun |
| collection | DOAJ |
| description | In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance. |
| format | Article |
| id | doaj-art-52d6670a62a248df87f353e47c629322 |
| institution | DOAJ |
| issn | 1875-9203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-52d6670a62a248df87f353e47c6293222025-08-20T03:18:06ZengWileyShock and Vibration1875-92032023-01-01202310.1155/2023/6928871An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical SystemsXianbin Sun0Mohan Wang1Bo Zhan2Yuanyuan Xiong3Wei Yu4Qingdao University of TechnologyQingdao University of TechnologyQingdao Haier International Trade Co., Ltd.Haier COSMO IoT Ecosystem Technology Co., Ltd.Qingdao Mingyu Intelligent Equipment Technology Research InstituteIn the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance.http://dx.doi.org/10.1155/2023/6928871 |
| spellingShingle | Xianbin Sun Mohan Wang Bo Zhan Yuanyuan Xiong Wei Yu An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems Shock and Vibration |
| title | An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems |
| title_full | An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems |
| title_fullStr | An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems |
| title_full_unstemmed | An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems |
| title_short | An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems |
| title_sort | intelligent diagnostic method for multisource coupling faults of complex mechanical systems |
| url | http://dx.doi.org/10.1155/2023/6928871 |
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