Partial Domain Intelligent Diagnosis Method for Rotor-Bearing System Based on Deep Learning

Abstract

Recently, deep transfer learning (TL) has successfully addressed the problem of fault diagnosis under variable operating conditions. Existing methods default that the source and target domains have the same label space, and solve distribution discrepancy problem under different working conditions by aligning their feature distributions. However, in the practical industry, is unlikely to guarantee the health conditions of the target domain data are consistent with the source domain. Therefore, industrial applications usually face the challenge of more difficult partial domain diagnosis scenarios. In this paper, a deep partial domain adaptation network based on a balanced alignment constraint strategy is proposed to realize cross-domain diagnosis. The proposed method combines balanced augmentation and subdomain alignment, which can effectively facilitate the positive transfer of shared categories. Meanwhile, the conditional entropy minimization is introduced to encourage the predictions of target domain samples with high confidence. The experimental results on the rolling bearing dataset verify the effectiveness and feasibility of the proposed method in handling the actual partial domain fault diagnosis problem.