Gearbox Fault Diagnosis based on GAF-inceptionResNet
In order to improve the accuracy of gearbox fault diagnosis and accurately express the health status of the gearbox， combined with deep learning algorithms， a GAF-inceptionResNet model for gear fault diagnosis is proposed. The model can directly take the original one-dimensional vibration signal aft...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2022-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.06.020 |
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| _version_ | 1841548758256451584 |
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| author | Changwen Li Peng Li Hua Ding |
| author_facet | Changwen Li Peng Li Hua Ding |
| author_sort | Changwen Li |
| collection | DOAJ |
| description | In order to improve the accuracy of gearbox fault diagnosis and accurately express the health status of the gearbox， combined with deep learning algorithms， a GAF-inceptionResNet model for gear fault diagnosis is proposed. The model can directly take the original one-dimensional vibration signal after GAF transformation to form photos as the input of the model. Through the stem-block， residual inception， residual module and classification layer， the residual inception network can broaden the network depth and improve the training time and accuracy， the residual block uses identity mapping to greatly reduce the training difficulty of the model. Therefore， the model can effectively mine the information between the signal features and enhance the feature learning ability of the model， thereby improving accuracy and accurately determine the faults. The test results show that the model can achieve a fault diagnosis accuracy of 99.59%. It can effectively achieve good gearbox fault identification and classification. |
| format | Article |
| id | doaj-art-1afbafff74674fbbad116a3a97691b5c |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-1afbafff74674fbbad116a3a97691b5c2025-01-10T14:01:45ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392022-05-014613414030471353Gearbox Fault Diagnosis based on GAF-inceptionResNetChangwen LiPeng LiHua DingIn order to improve the accuracy of gearbox fault diagnosis and accurately express the health status of the gearbox， combined with deep learning algorithms， a GAF-inceptionResNet model for gear fault diagnosis is proposed. The model can directly take the original one-dimensional vibration signal after GAF transformation to form photos as the input of the model. Through the stem-block， residual inception， residual module and classification layer， the residual inception network can broaden the network depth and improve the training time and accuracy， the residual block uses identity mapping to greatly reduce the training difficulty of the model. Therefore， the model can effectively mine the information between the signal features and enhance the feature learning ability of the model， thereby improving accuracy and accurately determine the faults. The test results show that the model can achieve a fault diagnosis accuracy of 99.59%. It can effectively achieve good gearbox fault identification and classification.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.06.020GearboxFault diagnosisGAFVibration signalDeep residual network |
| spellingShingle | Changwen Li Peng Li Hua Ding Gearbox Fault Diagnosis based on GAF-inceptionResNet Jixie chuandong Gearbox Fault diagnosis GAF Vibration signal Deep residual network |
| title | Gearbox Fault Diagnosis based on GAF-inceptionResNet |
| title_full | Gearbox Fault Diagnosis based on GAF-inceptionResNet |
| title_fullStr | Gearbox Fault Diagnosis based on GAF-inceptionResNet |
| title_full_unstemmed | Gearbox Fault Diagnosis based on GAF-inceptionResNet |
| title_short | Gearbox Fault Diagnosis based on GAF-inceptionResNet |
| title_sort | gearbox fault diagnosis based on gaf inceptionresnet |
| topic | Gearbox Fault diagnosis GAF Vibration signal Deep residual network |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.06.020 |
| work_keys_str_mv | AT changwenli gearboxfaultdiagnosisbasedongafinceptionresnet AT pengli gearboxfaultdiagnosisbasedongafinceptionresnet AT huading gearboxfaultdiagnosisbasedongafinceptionresnet |