Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
This work addresses data heterogeneity and privacy concerns in multi-task fault diagnosis across factories, offering an incremental improvement in federated learning for industrial applications.
The paper tackles the problem of poor generalization in fault diagnosis for rotating machinery due to scarce fault samples and data heterogeneity by proposing a personalized federated learning framework, which significantly improves diagnosis accuracy, particularly for machines with limited fault data.
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.