کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / هشتمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

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

نویسندگان :

Mohammadreza Kavianpour¹ Mohammadreza Ghorvei² Parisa Kavianpour³ Amin Ramezani⁴ Mohammad TH Beheshti⁵

1- Tarbiat Modares University 2- Tarbiat Modares University 3- University of Mazandaran 4- Tarbiat Modares University 5- دانشگاه تربیت مدرس

کلمات کلیدی :

Gearbox fault diagnosis, adversarial domain adaptation, coral, domain adaptation

چکیده :

Effective gearbox diagnostic procedures can assist in rotary machinery's reliable and safe operation. On the other hand, the constant change in working conditions and the lack 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. Unsupervised domain adaptation (UDA) has been utilized to overcome these challenges in various applications in recent years. This paper presents a novel method 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 operation conditions.