Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning
Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2023-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/417628 |
| _version_ | 1797206626017476608 |
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| author | Yanping Du Xuemin Cheng Yuxin Liu Shuihai Dou Juncheng Tu Yanlin Liu Xianyang Su |
| author_facet | Yanping Du Xuemin Cheng Yuxin Liu Shuihai Dou Juncheng Tu Yanlin Liu Xianyang Su |
| author_sort | Yanping Du |
| collection | DOAJ |
| description | Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time. |
| first_indexed | 2024-04-24T09:10:00Z |
| format | Article |
| id | doaj.art-f9b9a26a64d349a6bd5533526174f03f |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:10:00Z |
| publishDate | 2023-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-f9b9a26a64d349a6bd5533526174f03f2024-04-15T18:11:34ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392023-01-0130119820610.17559/TV-20221025165425Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer LearningYanping Du0Xuemin Cheng1Yuxin Liu2Shuihai Dou3Juncheng Tu4Yanlin Liu5Xianyang Su6Department of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaDepartment of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, ChinaUnder different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time.https://hrcak.srce.hr/file/417628deep learninggearbox fault diagnosisSTFTTL-Pro-MobileNetV3 networktransfer learning |
| spellingShingle | Yanping Du Xuemin Cheng Yuxin Liu Shuihai Dou Juncheng Tu Yanlin Liu Xianyang Su Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning Tehnički Vjesnik deep learning gearbox fault diagnosis STFT TL-Pro-MobileNetV3 network transfer learning |
| title | Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning |
| title_full | Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning |
| title_fullStr | Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning |
| title_full_unstemmed | Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning |
| title_short | Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning |
| title_sort | gearbox fault diagnosis method based on improved mobilenetv3 and transfer learning |
| topic | deep learning gearbox fault diagnosis STFT TL-Pro-MobileNetV3 network transfer learning |
| url | https://hrcak.srce.hr/file/417628 |
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