Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost
In order to improve the diagnostic performance and model generalization ability of the fault diagnosis model in data imbalance scenarios, a time series signal fault diagnosis method based on Nadam-TimeGAN and XGBoost was proposed. Firstly, the TimeGAN model based on LSTM and GRU was compared, and th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-04-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024081/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539229360848896 |
|---|---|
| author | HEI Xinhong GAO Miao ZHANG Kuan FEI Rong QIU Yuan JI Wenjiang |
| author_facet | HEI Xinhong GAO Miao ZHANG Kuan FEI Rong QIU Yuan JI Wenjiang |
| author_sort | HEI Xinhong |
| collection | DOAJ |
| description | In order to improve the diagnostic performance and model generalization ability of the fault diagnosis model in data imbalance scenarios, a time series signal fault diagnosis method based on Nadam-TimeGAN and XGBoost was proposed. Firstly, the TimeGAN model based on LSTM and GRU was compared, and the GRU network with better performance was selected as the component unit of the TimeGAN model. The Nadam optimization algorithm was used to optimize the components of the TimeGAN model, that was, the Nadam-TimeGAN model was constructed for data expansion. After data expansion, a balanced data set was constructed and input into the XGBoost integrated learning model for classification training. In the experiment, the action current data set of switch machine was selected for verification experiment, the MFPT bearing data set and the CWRU bearing data set were selected for generalization experiment, and compared with eight methods. The results show that the proposed method is higher than other methods in accuracy, recall and F1-score. The experimental results validate the effectiveness and generalization of the proposed method for imbalanced data fault diagnosis. |
| format | Article |
| id | doaj-art-c13aa1ccfe004e268a689af867b171dd |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-c13aa1ccfe004e268a689af867b171dd2025-01-14T07:24:10ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-04-014518520059254669Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoostHEI XinhongGAO MiaoZHANG KuanFEI RongQIU YuanJI WenjiangIn order to improve the diagnostic performance and model generalization ability of the fault diagnosis model in data imbalance scenarios, a time series signal fault diagnosis method based on Nadam-TimeGAN and XGBoost was proposed. Firstly, the TimeGAN model based on LSTM and GRU was compared, and the GRU network with better performance was selected as the component unit of the TimeGAN model. The Nadam optimization algorithm was used to optimize the components of the TimeGAN model, that was, the Nadam-TimeGAN model was constructed for data expansion. After data expansion, a balanced data set was constructed and input into the XGBoost integrated learning model for classification training. In the experiment, the action current data set of switch machine was selected for verification experiment, the MFPT bearing data set and the CWRU bearing data set were selected for generalization experiment, and compared with eight methods. The results show that the proposed method is higher than other methods in accuracy, recall and F1-score. The experimental results validate the effectiveness and generalization of the proposed method for imbalanced data fault diagnosis.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024081/TimeGANNadamXGBoostfault diagnosisdata augmentation |
| spellingShingle | HEI Xinhong GAO Miao ZHANG Kuan FEI Rong QIU Yuan JI Wenjiang Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost Tongxin xuebao TimeGAN Nadam XGBoost fault diagnosis data augmentation |
| title | Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost |
| title_full | Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost |
| title_fullStr | Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost |
| title_full_unstemmed | Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost |
| title_short | Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost |
| title_sort | fault diagnosis method of timing signal based on nadam timegan and xgboost |
| topic | TimeGAN Nadam XGBoost fault diagnosis data augmentation |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024081/ |
| work_keys_str_mv | AT heixinhong faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost AT gaomiao faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost AT zhangkuan faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost AT feirong faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost AT qiuyuan faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost AT jiwenjiang faultdiagnosismethodoftimingsignalbasedonnadamtimeganandxgboost |