An Intelligent Gearbox Fault Diagnosis under Different Operating Conditions using Adversarial Domain Adaptation

Abstract

Effective gearbox diagnostic procedures can assist in rotary machinery's reliable and safe operation. On the other hand, the constant change in operational conditions, along with an absence of labeled data, have made fault diagnosis difficult. Changes in working conditions that cause discrepancies in data distribution and a lack of labeled data dramatically reduce fault diagnosis accuracy in deep learning algorithms. In recent years, unsupervised domain adaptation (UDA) has been utilized to overcome these challenges in various applications. This paper introduces a new approach based on the CNN model and hybrid domain adaptation called deep coral adversarial network (DCAN) to solve these issues. The CNN model is used to extract features, and the distribution discrepancy between domains is decreased by applying two modules of domain adversarial learning and deep coral adaptation. The proposed method's performance was evaluated using the SEU gearbox dataset. The results demonstrate the proposed method's proper performance in diagnosing gearbox faults under different operating conditions.