An End-to-end Domain Generalization Fault Diagnosis Method Based on Adaptive Weighted Domain Adversarial Learning

Abstract

Domain adaptation (DA) based fault diagnosis methods have attracted great attention for a long time, which leverages domain-invariant knowledge from a source domain (SD) to a special target domain (TD). However, in practise, there not usually exists only single SD and a specific and available TD. Mostly, data is generated from multiple domain distributions due to varied working conditions, and the TD is not always available during model training. Hence, how to use these multiple domain distribution data to build a model to generalize domain knowledge to an “unseen” TD, is an emerging and challengeable issue. In this paper, a novel domain generalization (DG) based fault diagnosis methods is proposed. Multiple domains are considered jointly to extract generalized domain-invariant features. A multiple residual blocks structure is adopted as the main feature extractor, an additional extractor is added to extract prior knowledge from statistic features. Besides, a k-means based adaptive weighted domain adversarial learning is designed to realize multiple domain confusion in a latent space while the wasserstein distance is calculated to assist in narrowing discrepancy between different domain distributions. Experiments of multiple domain distributions tasks from two datasets are conducted to evaluate the proposed method, and the results have verified the effectiveness of proposed method.