Intelligent Deep Adversarial Network Fault Diagnosis Method Using Semisupervised Learning

In recent years, deep learning has become a popular issue in the intelligent fault diagnosis of industrial equipment. Under practical working conditions, although the collected vibration data are of large capacity, most of the vibration data are not labeled. Collecting and labeling sufficient fault data for each condition are unrealistic. Therefore, constructing a reliable fault diagnosis model with a small amount of labeled vibration data is a significant problem. In this paper, the vibration time-domain signal of the fault bearing is transformed into a 2-dimensional image by wavelet transform to obtain the time-frequency domain information of the original data. A deep adversarial convolutional neural network based on semisupervised learning is proposed. A large amount of fake data generated by the generator and unlabeled true vibration data are used in the discriminator to learn the overall distribution of data by judging the authenticity of the input. Three regular terms for different loss functions are designed to constrain the parameters of the discriminator to improve the learning ability of the model. The proposed method is validated by two bearing fault diagnosis cases. The experiment results show that the proposed method has higher diagnostic accuracy than traditional deep models on multigroup small datasets of different capacities. The proposed method provides a new solution to the fault diagnosis problem with large vibration data but few labels.