Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery

• URL: http://arxiv.org/abs/2211.09406v1
• Date: Thu, 17 Nov 2022 08:28:25 GMT
• Title: Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
• Authors: Sheng Guo, Zengxiang Li, Hui Liu, Shubao Zhao and Cheng Hao Jin
• Abstract summary: This paper proposes a personalized federated learning framework, enabling multi-task fault diagnosis method across multiple factories. A multi-task deep learning model based on convolutional neural network is constructed to diagnose the faults of machinery with heterogeneous information fusion. The case study on collected data from real machines verifies the effectiveness of the proposed framework.
• Score: 6.442377498489894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent fault diagnosis is essential to safe operation of machinery. However, due to scarce fault samples and data heterogeneity in field machinery, deep learning based diagnosis methods are prone to over-fitting with poor generalization ability. To solve the problem, this paper proposes a personalized federated learning framework, enabling multi-task fault diagnosis method across multiple factories in a privacypreserving manner. Firstly, rotating machines from different factories with similar vibration feature data are categorized into machine groups using a federated clustering method. Then, a multi-task deep learning model based on convolutional neural network is constructed to diagnose the multiple faults of machinery with heterogeneous information fusion. Finally, a personalized federated learning framework is proposed to solve data heterogeneity across different machines using adaptive hierarchical aggregation strategy. The case study on collected data from real machines verifies the effectiveness of the proposed framework. The result shows that the diagnosis accuracy could be improved significantly using the proposed personalized federated learning, especially for those machines with scarce fault samples.

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