Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

Abstract

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.