Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1
In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/4632540 |
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| _version_ | 1832564085028290560 |
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| author | Peng Dai JianPing Wang Lulu Wu ShuPing Yan FengTao Wang Linkai Niu |
| author_facet | Peng Dai JianPing Wang Lulu Wu ShuPing Yan FengTao Wang Linkai Niu |
| author_sort | Peng Dai |
| collection | DOAJ |
| description | In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system. |
| format | Article |
| id | doaj-art-c48be545470b4934b49a8f87f5fc3e2b |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c48be545470b4934b49a8f87f5fc3e2b2025-02-03T01:11:57ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/4632540Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1Peng Dai0JianPing Wang1Lulu Wu2ShuPing Yan3FengTao Wang4Linkai Niu5School of Mechanical EngineeringSchool of Mechanical EngineeringSchool of Mechanical EngineeringSchool of Mechanical EngineeringSchool of Mechanical EngineeringCollege of Mechanical and Vehicle EngineeringIn the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.http://dx.doi.org/10.1155/2022/4632540 |
| spellingShingle | Peng Dai JianPing Wang Lulu Wu ShuPing Yan FengTao Wang Linkai Niu Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 Shock and Vibration |
| title | Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 |
| title_full | Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 |
| title_fullStr | Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 |
| title_full_unstemmed | Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 |
| title_short | Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1 |
| title_sort | defect diagnosis of gear shaft bearing system based on the owf tscnn composed of wavelet time frequency map and fft spectrum 1 |
| url | http://dx.doi.org/10.1155/2022/4632540 |
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