Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN
Aiming at the problems of insufficient samples of gear fault signals collected in practical engineering，insufficient training，low fault recognition rate and easy pattern collapse of generative adversarial networks when using common deep learning network for pattern recognition under noise interferen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2022-07-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.07.022 |
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| _version_ | 1841548873990930432 |
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| author | Zheng Li Shengli Wu Wenting Xing Shiyong Liao |
| author_facet | Zheng Li Shengli Wu Wenting Xing Shiyong Liao |
| author_sort | Zheng Li |
| collection | DOAJ |
| description | Aiming at the problems of insufficient samples of gear fault signals collected in practical engineering，insufficient training，low fault recognition rate and easy pattern collapse of generative adversarial networks when using common deep learning network for pattern recognition under noise interference. An intelligent diagnosis method of gear faults based on MGAN （Mixture Generative Adversarial Nets） and CNN （Convolutional Neural Networks） is proposed. The real gear signals are transformed into time-frequency signals by time-frequency transformation technology after morphological filtering，and new samples are synthesized by MGAN to obtain a balanced data set，which overcomes the problem of insufficient samples. At the same time，the influence law of main parameters of MGAN on the quality of synthesized samples is analyzed. The balanced data set is used to train CNN for fault diagnosis，which effectively improves the fault diagnosis rate. Through the comparative test and bench test，the effectiveness and advantages of this method in accurately identifying faults and overcoming the collapse of neural network mode under the condition of insufficient samples are verified，which provides a new research idea for typical fault extraction and intelligent identification of gearboxes. |
| format | Article |
| id | doaj-art-f84693866c95498282f1584936d1a0e0 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-f84693866c95498282f1584936d1a0e02025-01-10T13:58:29ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392022-07-014614415130476142Intelligent Diagnosis Method of Gear Faults based on MGAN and CNNZheng LiShengli WuWenting XingShiyong LiaoAiming at the problems of insufficient samples of gear fault signals collected in practical engineering，insufficient training，low fault recognition rate and easy pattern collapse of generative adversarial networks when using common deep learning network for pattern recognition under noise interference. An intelligent diagnosis method of gear faults based on MGAN （Mixture Generative Adversarial Nets） and CNN （Convolutional Neural Networks） is proposed. The real gear signals are transformed into time-frequency signals by time-frequency transformation technology after morphological filtering，and new samples are synthesized by MGAN to obtain a balanced data set，which overcomes the problem of insufficient samples. At the same time，the influence law of main parameters of MGAN on the quality of synthesized samples is analyzed. The balanced data set is used to train CNN for fault diagnosis，which effectively improves the fault diagnosis rate. Through the comparative test and bench test，the effectiveness and advantages of this method in accurately identifying faults and overcoming the collapse of neural network mode under the condition of insufficient samples are verified，which provides a new research idea for typical fault extraction and intelligent identification of gearboxes.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.07.022Generative adversarial networkModel collapseInsufficient samplesFault diagnosis |
| spellingShingle | Zheng Li Shengli Wu Wenting Xing Shiyong Liao Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN Jixie chuandong Generative adversarial network Model collapse Insufficient samples Fault diagnosis |
| title | Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN |
| title_full | Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN |
| title_fullStr | Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN |
| title_full_unstemmed | Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN |
| title_short | Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN |
| title_sort | intelligent diagnosis method of gear faults based on mgan and cnn |
| topic | Generative adversarial network Model collapse Insufficient samples Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.07.022 |
| work_keys_str_mv | AT zhengli intelligentdiagnosismethodofgearfaultsbasedonmganandcnn AT shengliwu intelligentdiagnosismethodofgearfaultsbasedonmganandcnn AT wentingxing intelligentdiagnosismethodofgearfaultsbasedonmganandcnn AT shiyongliao intelligentdiagnosismethodofgearfaultsbasedonmganandcnn |