Generative adversarial network and transfer-learning-based fault detection for rotating machinery with imbalanced data condition

Abstract

Abstract Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of its outstanding data-driven capability. However, the severely imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis methods. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning (TL) method based on the time-generative adversarial network (Time-GAN) and efficient-net models. Firstly, the proposed model, called Time-GAN-TL, extends the imbalanced fault diagnosis of rolling bearings using time-series GAN. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the efficient-net into the transfer learning method. Finally, the proposed method is validated using two types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.