A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition

Vibration signals can be used to extract effective fault features for fault diagnosis. However, traditional supervised learning requires considerable manpower and time to mark samples manually, and this process is difficult to apply to practical fault diagnosis. Deep reinforcement learning which combines the perception ability of deep learning with the decision-making ability of reinforcement learning, can independently extract hidden fault features and effectively improve the accuracy of fault diagnosis. Semi-supervised learning can reduce the proportion of labeled samples to decrease the learning cost while improving the recognition accuracy with unlabeled samples. In this study, we propose a novel semi-supervised deep reinforcement learning method. A semi-supervised generative adversarial network combined with the improved actor-critic algorithm is proposed to perform fault diagnosis when the labeled sample size is small. In the experiment of rolling bearing fault and engineering application, three-channel time-frequency graphs extracted from raw signals with the wavelet packet are compressed into single channel gray graphs. Then, to simulate the less labeled sample dataset, 2%, 5%, 20%, 50% and 100% sample labels are set by dislodging partial label from the processing sample. The results of the proposed method and other intelligent methods are listed to demonstrate that the proposed method could provide better performance over other methods even if the size of labeled sample is small in compound fault diagnosis.