Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

Abstract Intelligent data-driven machinery fault diagnosis methods have been successfully and popularly developed in the past years. While promising diagnostic performance has been achieved, the existing methods generally require large amounts of high-quality supervised data for training, which are mostly difficult and expensive to collect in real industries. Therefore, it is motivated that the distributed data of multiple clients can be integrated and exploited to build a powerful data-driven model. However, that basically requires data sharing among different users, and is not preferred in most industrial cases due to potential conflict of interests. In order to address the data island problem, a federated learning method for machinery fault diagnosis is proposed in this paper. Model training is locally implemented within each participated client, and a self-supervised learning scheme is proposed to enhance the learning performance. The server aggregates the locally updated models in each training round under the dynamic validation scheme, and a global fault diagnosis model can be established. Only the models are mutually communicated rather than the data, which ensures data privacy among different clients. The experiments on two datasets suggest the proposed method offers a promising approach on confidential decentralized learning.