A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework
Abstract Data-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model i...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06826-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849399697607753728 |
|---|---|
| author | Huihui Han Ye Zhao Hao Jiang Muxin Chen Song Zhou Zihan Lin Xin Wang Boman Mao Xinyue Yang Yuchun Li |
| author_facet | Huihui Han Ye Zhao Hao Jiang Muxin Chen Song Zhou Zihan Lin Xin Wang Boman Mao Xinyue Yang Yuchun Li |
| author_sort | Huihui Han |
| collection | DOAJ |
| description | Abstract Data-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data processing, which integrates multiscale features and an AIC-diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, a series of canonical correlation representations are extracted from the SMR models, and a combined model of CFLF method and SMR is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over the single SMR model with higher prediction accuracy of 35.73%. This study provides a new technique for evaluating gear oil in the wind energy sector. |
| format | Article |
| id | doaj-art-9916078af7dc4d0c9dd9c08ad5fa31d2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9916078af7dc4d0c9dd9c08ad5fa31d22025-08-20T03:38:16ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-06826-9A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning frameworkHuihui Han0Ye Zhao1Hao Jiang2Muxin Chen3Song Zhou4Zihan Lin5Xin Wang6Boman Mao7Xinyue Yang8Yuchun Li9East China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co., LtdEast China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co., LtdDatang Guoxin Binhai Offshore Wind Power Co., Ltd.East China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co., LtdDatang Guoxin Binhai Offshore Wind Power Co., Ltd.School of Chemistry and Pharmaceutical Engineering, Changsha University of Science and TechnologyEast China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co., LtdSchool of Computer Science and Technology, Changsha University of Science and TechnologySchool of Computer Science and Technology, Changsha University of Science and TechnologySchool of Chemistry and Pharmaceutical Engineering, Changsha University of Science and TechnologyAbstract Data-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data processing, which integrates multiscale features and an AIC-diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, a series of canonical correlation representations are extracted from the SMR models, and a combined model of CFLF method and SMR is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over the single SMR model with higher prediction accuracy of 35.73%. This study provides a new technique for evaluating gear oil in the wind energy sector.https://doi.org/10.1038/s41598-025-06826-9Stepwise multivariate regressionGear oil evaluationClustered federated learning frameworkWind turbines |
| spellingShingle | Huihui Han Ye Zhao Hao Jiang Muxin Chen Song Zhou Zihan Lin Xin Wang Boman Mao Xinyue Yang Yuchun Li A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework Scientific Reports Stepwise multivariate regression Gear oil evaluation Clustered federated learning framework Wind turbines |
| title | A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework |
| title_full | A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework |
| title_fullStr | A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework |
| title_full_unstemmed | A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework |
| title_short | A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework |
| title_sort | novel diagnosis methodology of gear oil for wind turbine combining stepwise multivariate regression and clustered federated learning framework |
| topic | Stepwise multivariate regression Gear oil evaluation Clustered federated learning framework Wind turbines |
| url | https://doi.org/10.1038/s41598-025-06826-9 |
| work_keys_str_mv | AT huihuihan anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT yezhao anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT haojiang anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT muxinchen anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT songzhou anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT zihanlin anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT xinwang anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT bomanmao anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT xinyueyang anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT yuchunli anoveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT huihuihan noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT yezhao noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT haojiang noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT muxinchen noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT songzhou noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT zihanlin noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT xinwang noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT bomanmao noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT xinyueyang noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework AT yuchunli noveldiagnosismethodologyofgearoilforwindturbinecombiningstepwisemultivariateregressionandclusteredfederatedlearningframework |