Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training diff...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (16), p.7302
Main Authors: Yan, Zhenhao, Sun, Jiachen, Zhang, Yixiang, Liu, Lilan, Gao, Zenggui, Chang, Yuxing
Format: Article
Language:English
Subjects:
Adaptation
Computational linguistics
data privacy
Data security
Deep learning
domain adaptation
Fault diagnosis
federated learning
Knowledge
Language processing
Learning strategies
Methods
Natural language interfaces
Privacy
transfer learning
Working conditions
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Summary:Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23167302