Meta-learning Based Domain Generalization Framework for Fault Diagnosis With Gradient Aligning and Semantic Matching

Abstract

Intelligent fault diagnosis models have demonstrated a superior performance in industrial prognostics health management scenarios. However, these models may struggle to generalize in complicated industrial environments, when encountering new working conditions and handling low-resource and heterogeneous data. To cope with the aforementioned issues, we focus on constructing a universal training framework with domain generalization technique that will encourage fault diagnosis model to generalize well in unseen working conditions. Firstly, a model-agnostic meta-learning based training framework called Meta-GENE is proposed for homogeneous and heterogeneous domain generalization. Secondly, a gradient aligning algorithm is introduced in meta-learning framework to learn domain-invariant strategy for robust prediction in unseen working conditions. Thirdly, a semantic matching technique is proposed for utilizing heterogeneous data to alleviate low-resource problem. Our method has yielded excellent performance on the PHM09 fault diagnosis dataset and achieved superior results on a set of generalization tasks across various working conditions.