A Cross Domain Adaptation Method based on Adversarial Cycle Consistence Learning for Rotary Machine Fault Diagnosis

Gye-Bong Jang, Sung-Bae Cho

http://doi.org/10.5626/JOK.2022.49.7.530

Research on data-based fault diagnosis models is being actively conducted in various industries. However, in the case of industrial equipment, various operating conditions occur, and it is difficult to secure sufficient training data. To solve this problem, a cross-domain adaptation technique can be utilized. In this study, we propose an adversarial consistency-maintaining transformation learning method that can maintain failure classification consistency even for the new untrained environmental data using the rotating body vibration data. The data generated through consistent learning creates a continuous invariant latent space between the new operating condition data distribution and the known data distribution and learns to maintain the failure classification performance through an adversarial learning network that shares the failure classification characteristic information. Therefore, the proposed method can provide a more stable and general classification performance by expanding the potential space to minimize the discrepancy between domain data. The experimental results of the proposed model showed about 88% accuracy for a real-machine dataset, and compared to the existing cross-domain adaptive learning methods, it showed a performance improvement of about 5-10%. According to the results of this study, it is expected to be an effective solution for the problem of equipment failure diagnosis at actual industrial sites.