The vibration signals of rolling bearings are susceptible to strong noise interference. In addition, the lacking of fault samples for rolling bearings increases the difficulty of fault diagnosis. A fault diagnosis model based on conditional generative adversarial network (CGAN) and convolutional denoising auto-encoder (CDAE) is proposed to solve these problems. CGAN is used to generate new samples with the same distribution as the real samples. In order to improve the anti-noise ability of the model, we use CDAE as the discriminator model of CGAN to extract more robust features and achieve more accurate discrimination and classification. The generator and the discriminator are optimized by the adversarial mechanism to improve the quality of sample generation and the accuracy of fault classification. The experimental results show that the CGAN-CDAE model has good anti-noise ability, and achieves good fault diagnosis performance of rolling bearings in the case of small samples and class imbalance.

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.