Federated Generalized Zero-Sample Industrial Fault Diagnosis Across Multisource Domains

Federated learning (FL) and zero-shot learning have been becoming increasingly popular due to the data-privacy protection and the diagnosis of unseen faults in the industrial fault diagnosis. However, most existing diagnosis methods have the consistency assumption of distributions across different clients under multisource domain scenarios and cannot effectively diagnose both seen and unseen faults. Therefore, to diagnose both seen and unseen faults without data sharing and with distribution discrepancies across different clients, a federated generalized zero-sample fault diagnosis (GZSFD) paradigm is proposed in this article. In the client side, a stacked autoencoder (AE)-based feature extractor is introduced in each client for low-level features. In the cloud server, a feature-level distribution alignment scheme is developed to alleviate discrepancies for more discriminative high-level features. Moreover, a bidirectional AE (BAE) with reconstruction and cross-reconstruction streams is designed to enhance the feature-semantic consistency. Finally, a gating model based on BAE is proposed to identify online samples and mitigates the misclassification of unseen samples. Results on two practical industrial cases show that the proposed method achieves the improvement in federated GZSFD and effectively handles distribution discrepancies across different clients.