Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster
Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/11/972 |
| _version_ | 1797467492460789760 |
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| author | Wanqian Yang Gang Yu |
| author_facet | Wanqian Yang Gang Yu |
| author_sort | Wanqian Yang |
| collection | DOAJ |
| description | Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding to one wind turbine in a cluster. Each node is equivalent and functional replicable with a new federated transfer learning method, including model transfer based on multi-task learning and model fusion based on dynamic adaptive weight adjustment. Models with convolutional neural networks are trained locally and transmitted among the nodes. A solution for the processes of data management, information transmission, model transfer and fusion is provided. Experiments are conducted on a fault signal testing bed and bearing dataset of Case Western Reserve University. The results show the excellent performance of the method for fault diagnosis of a gearbox in a wind turbine cluster. |
| first_indexed | 2024-03-09T18:54:25Z |
| format | Article |
| id | doaj.art-0743360316244d2a8bebb087006afef3 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T18:54:25Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-0743360316244d2a8bebb087006afef32023-11-24T05:32:19ZengMDPI AGMachines2075-17022022-10-01101197210.3390/machines10110972Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine ClusterWanqian Yang0Gang Yu1School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, ChinaIntelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding to one wind turbine in a cluster. Each node is equivalent and functional replicable with a new federated transfer learning method, including model transfer based on multi-task learning and model fusion based on dynamic adaptive weight adjustment. Models with convolutional neural networks are trained locally and transmitted among the nodes. A solution for the processes of data management, information transmission, model transfer and fusion is provided. Experiments are conducted on a fault signal testing bed and bearing dataset of Case Western Reserve University. The results show the excellent performance of the method for fault diagnosis of a gearbox in a wind turbine cluster.https://www.mdpi.com/2075-1702/10/11/972collaborative intelligencedeep learningfault diagnosisgroup technologypeer-to-peer computingtransfer learning |
| spellingShingle | Wanqian Yang Gang Yu Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster Machines collaborative intelligence deep learning fault diagnosis group technology peer-to-peer computing transfer learning |
| title | Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster |
| title_full | Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster |
| title_fullStr | Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster |
| title_full_unstemmed | Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster |
| title_short | Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster |
| title_sort | federated multi model transfer learning based fault diagnosis with peer to peer network for wind turbine cluster |
| topic | collaborative intelligence deep learning fault diagnosis group technology peer-to-peer computing transfer learning |
| url | https://www.mdpi.com/2075-1702/10/11/972 |
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