FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916076374196224 |
|---|---|
| author | Shen, Jie Yang, Shusen Zhao, Cong Ren, Xuebin Zhao, Peng Yang, Yuqian Han, Qing Wu, Shuaijun |
| author_facet | Shen, Jie Yang, Shusen Zhao, Cong Ren, Xuebin Zhao, Peng Yang, Yuqian Han, Qing Wu, Shuaijun |
| contents | Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial agents with limited samples to construct a fault diagnosis model without jeopardizing their raw data privacy. Existing approaches, however, can neither address the intense sample heterogeneity caused by different working conditions of practical agents, nor the extreme fault label scarcity, even zero, of newly deployed equipment. To address these issues, we present FedLED, the first unsupervised vertical FTL equipment fault diagnosis method, where knowledge of the unlabeled target domain is further exploited for effective unsupervised model transfer. Results of extensive experiments using data of real equipment monitoring demonstrate that FedLED obviously outperforms SOTA approaches in terms of both diagnosis accuracy (up to 4.13 times) and generality. We expect our work to inspire further study on label-free equipment fault diagnosis systematically enhanced by target domain knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17451 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning Shen, Jie Yang, Shusen Zhao, Cong Ren, Xuebin Zhao, Peng Yang, Yuqian Han, Qing Wu, Shuaijun Machine Learning Artificial Intelligence Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial agents with limited samples to construct a fault diagnosis model without jeopardizing their raw data privacy. Existing approaches, however, can neither address the intense sample heterogeneity caused by different working conditions of practical agents, nor the extreme fault label scarcity, even zero, of newly deployed equipment. To address these issues, we present FedLED, the first unsupervised vertical FTL equipment fault diagnosis method, where knowledge of the unlabeled target domain is further exploited for effective unsupervised model transfer. Results of extensive experiments using data of real equipment monitoring demonstrate that FedLED obviously outperforms SOTA approaches in terms of both diagnosis accuracy (up to 4.13 times) and generality. We expect our work to inspire further study on label-free equipment fault diagnosis systematically enhanced by target domain knowledge. |
| title | FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2312.17451 |