UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
Fault diagnosis of mechanical equipment involves data collection, feature
extraction, and pattern recognition but is often hindered by the imbalanced
nature of industrial data, introducing significant uncertainty and reducing
diagnostic reliability. To address these challenges, this study proposes the
Uncertainty-Aware Bayesian Meta-Learning Framework (UBMF), which integrates
four key modules: data perturbation injection for enhancing feature robustness,
cross-task self-supervised feature extraction for improving transferability,
uncertainty-based sample filtering for robust out-of-domain generalization, and
Bayesian meta-knowledge integration for fine-grained classification.
Experimental results on ten open-source datasets under various imbalanced
conditions, including cross-task, small-sample, and unseen-sample scenarios,
demonstrate the superiority of UBMF, achieving an average improvement of 42.22%
across ten Any-way 1-5-shot diagnostic tasks. This integrated framework
effectively enhances diagnostic accuracy, generalization, and adaptability,
providing a reliable solution for complex industrial fault diagnosis.
