Sparse Filtering with Joint Distribution Adaptation for Intelligent Fault Diagnosis

Abstract:

Most existing intelligent fault diagnosis schemes rely on the assumption that the training and test samples are independent and identically distributed, ignoring the domain distribution shift caused by diverse operating conditions, which may limit their flexible applications in practical diagnostic tasks. To address this problem, we propose a novel unsupervised transfer learning method, namely, sparse filtering with joint distribution adaptation (SFJDA) for mechanical fault diagnosis. Specifically, two sparse filters (SFs) are used to jointly extract features from each domain, and the final feature space is formed by stacking the subspaces obtained from double SFs. Then, the maximum mean difference (MMD) is introduced to measure the distribution discrepancy between different domains. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the constructed framework can capture domain-invariant and class-separable features. Finally, the effectiveness of the proposed scheme is verified by a motor bearing dataset. 

Index Terms： 

Intelligent fault diagnosis, sparse filtering, transfer learning, domain adaptation, joint distribution adaptation.

原文链接：DOI10.23919/CCC58697.2023.10241214.