Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network
Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective informat...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6669006 |
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| author | Decai Zhang Xueping Ren Hanyue Zuo |
| author_facet | Decai Zhang Xueping Ren Hanyue Zuo |
| author_sort | Decai Zhang |
| collection | DOAJ |
| description | Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production. |
| format | Article |
| id | doaj-art-400a66d975d049658d84afa590ccba65 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-400a66d975d049658d84afa590ccba652025-02-03T05:58:30ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66690066669006Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural NetworkDecai Zhang0Xueping Ren1Hanyue Zuo2School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaVibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.http://dx.doi.org/10.1155/2021/6669006 |
| spellingShingle | Decai Zhang Xueping Ren Hanyue Zuo Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network Shock and Vibration |
| title | Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network |
| title_full | Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network |
| title_fullStr | Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network |
| title_full_unstemmed | Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network |
| title_short | Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network |
| title_sort | compound fault diagnosis for gearbox based using of euclidean matrix sample entropy and one dimensional convolutional neural network |
| url | http://dx.doi.org/10.1155/2021/6669006 |
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