Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things

Abstract

Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a challenge. One feasible approach is to leverage prior knowledge from similar source domains to enhance fault diagnosis with limited samples in the target domain. Nevertheless, complex operating conditions and fault types can give rise to domain shift issues between different domains, therefore hindering direct data-sharing due to data privacy concerns. To address these challenges, this article introduces a novel federated few-shot fault-diagnosis method called FedCDAE-MN. FedCDAE-MN employs a convolutional denoising auto-encoder and feature-space metric learning to enhance the model’s generalization across domains for improving the adaptability to varying working conditions, new fault types, and noisy data. Moreover, our approach ensures privacy preservation by avoiding the need to share sensitive data with other participants. Through extensive experiments on real-world datasets, FedCDAE-MN surpasses existing methods and significantly improves the accuracy of fault diagnosis.