Industrial Edge Intelligence: Federated-Meta Learning Framework for Few-Shot Fault Diagnosis

The scarcity of fault samples has been the bottleneck for the large-scale application of mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional few-shot FD methods are fundamentally limited in that the models can only learn from the direct dataset, i.e., a limited number of local data samples. Federated learning (FL) has recently shown the capacity of collaborative artificial intelligence and privacy preservation. Based on these capabilities, we propose a novel approach to solve the few-shot FD problem, which includes a generic framework (i.e., FedMeta-FFD) and an easy-to-implement enhancement technique (i.e., AILR). The FedMeta-FFD framework allows clients to learn from indirect datasets owned by other collaborators while training a global meta-learner to solve the few-shot problem directly. More concretely, with only a few labeled examples and training iterations, the global meta-learner can quickly adapt to a new client (e.g., a machine under different operating conditions) or a newly encountered fault category. Adopting AILR can significantly improve the performance of the FedMeta-FFD framework while also increasing the stability of the learning process. Further, we conduct a theoretical analysis of the proposed framework's convergence in a non-convex setting. We thoroughly evaluate the proposed FedMeta-FFD on two fault diagnosis datasets and also perform the practical validation on real-world IIoT scenarios. They demonstrate that our proposed approach achieves significantly faster convergence and higher accuracy than the existing approaches.