Robust Industrial Equipment Fault Detection via Bayesian Federated Learning with Channel Importance

Abstract

In the era of industrial automation and smart manufacturing, the reliability of industrial equipment is of paramount importance as equipment failures can trigger severe production disruptions and safety hazards. While Bayesian Learning effectively handles uncertainties and enhances fault detection interpretability, it faces challenges in data privacy and centralized processing, whereas Federated Learning safeguards data privacy. Bayesian Federated Learning (BFL) integrates their strengths for privacy-preserving probabilistic modeling with superior generalization. This paper innovatively proposes BFL-CI (Bayesian Federated Learning with Channel Importance), presenting a comprehensive framework and detection process that analyzes channel parameter distributions and transmits selected channel distributions as prior distributions for subsequent clients. Experiments on bearing and gearbox datasets simulating multi-sensor scenarios verify BFL-CI’s effectiveness in improving fault detection accuracy and robustness. This paper fills the gap in applying BFL to industrial equipment fault detection, offering a cutting-edge privacy-preserving solution for reliable intelligent industrial monitoring.