Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis

Research on the health diagnosis of mechanical equipment has developed unprecedentedly in recent years, and a large number of diagnostic solutions have considerably improved the stability of mechanical equipment in industrial production. However, such satisfactory diagnostic performance relies on a large number of data samples, which are frequently difficult to obtain in real industrial scenarios. The traditional strategy of data sharing is no longer advisable due to the potential conflict of interest among users. A federated transfer learning scheme is proposed to alleviate the data island problem in industrial production while protecting data privacy. This solution adopts a distributed structure, which includes local model training and global model update. A differential training scheme is proposed to enhance the domain adaptability of the local model. The central server evaluates the contribution ability of each local model to the target task. It also weights and aggregates each client model on the basis of parameter importance ranking in the form of model fusion. The target task of the experiment is performed on two sets of bearing datasets. By comparing with other diagnostic methods, a conclusion can be drawn that the proposed scheme provides a promising federated learning method while protecting client data privacy.