In recent years, the method based on deep learning has been widely used in power equipment fault diagnosis. However, in practical application, the deep learning model can not be applied to one-dimensional condition monitoring data, and the scarcity of fault data will lead to the overfitting of the deep learning model, which will seriously reduce the accuracy of fault diagnosis. To solve the above problems, this paper proposes a data enhancement method based on WGAN-GP network. Firstly, the original data is preprocessed, and the one-dimensional vibration signal collected by the sensor is converted into a two-dimensional gray image. A network based on WGAN-GP is established to generate sample images and these sample images are similar to the original images, which realize the expansion of image samples. On this basis, a fault diagnosis method based on Convolutional Neural Network (CNN) is established. Numerical experiments are carried out and experiments data was obtained from the Case Western Reserve University (CWRU) Bearing Data Center. The experimental results show that this method can realize the reasonable transformation of data structure, the reasonable expansion of fault samples and the improvement of the accuracy of fault diagnosis results.

deep learning, data generation, data enhancement, fault diagnosis