  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.

Machine Learning;Artificial Intelligence