Statistical Manifold Generation-Driven Equipment Collaborative Personalized Fault Diagnosis

Abstract

In Industrial Internet of Things (IIoT) scenarios, data privacy constraints and distribution discrepancies caused by time-varying working conditions hinder existing intelligent fault diagnosis models from maintaining robust generalization performance across different, particularly unknown, working conditions. To address this challenge, a federated generalization fault diagnosis method driven by statistical manifold generation is proposed. The method constructs a closed-loop collaborative strategy. Initially, individual users extract and upload representative statistical information (SI) as lightweight, privacy-preserving knowledge carriers. Subsequently, a global Gaussian mixture model coupled with a covariance expansion mechanism is established in the cloud to fit multi-source distributions and extrapolate uncertainty boundaries, thereby generating virtual SI. Finally, this virtual SI is assigned via a difference-aware mechanism and integrated locally using instance normalization to achieve domain-invariant augmented training. Extensive distributed collaborative fault diagnosis experiments conducted on rolling bearing and gearbox datasets demonstrate that, when facing completely unknown working conditions, the proposed method achieves an average diagnostic accuracy of over 85%, exceeding 90% in some tasks. Furthermore, the communication payload per round is merely 1.25 KB. While strictly preserving data privacy, the proposed method significantly enhances the cross-domain generalization capability of local models with minimal communication overhead, providing an efficient and robust collaborative intelligent diagnosis solution for resource-constrained IIoT edge devices.