A fault diagnosis method for rolling bearings of wind turbine generators based on MCGAN data enhancement

In view of the problems such as poor diagnostic capability and generalization ability of wind turbine generator bearing fault diagnosis methods caused by complex wind turbine generator bearing conditions and few fault samples under actual operating conditions, a wind turbine generator bearing vibration signal data enhancement method based on improved multiple fully convolutional generative adversarial neural networks (MCGAN) was proposed. Firstly, two-dimensional time-frequency features are extracted from the raw data using a Short-Time Fourier Transform (STFT). Secondly, by incorporating multiple CGANs of different scales and a hybrid loss function, the original GAN network was enhanced to learn the intrinsic distribution of bearing vibration signals and generate diverse vibration signals with distinct bearing fault characteristics, resulting in an expanded dataset. Finally, a comparative experiment was conducted using real wind turbine generator-bearing data. The results demonstrate that the augmented samples generated by MCGAN contain rolling bearing fault information while maintaining sample distribution and diversity. By utilizing the augmented dataset to train commonly used fault diagnostic classifiers, the diagnostic accuracy for the original vibration signals exceeds 80%, providing a theoretical basis for addressing the scarcity of fault samples in practical engineering scenarios. A data enhancement method for turbine generator bearing based on improved multiple full convolutions generating adversarial neural networks (MCGAN) was proposed, which can effectively solve the problem of data imbalance.STFT was used to extract the TF features of the original data as the input of MCGAN. Compared with CWT, GI, WVD and EMD algorithms, STFT can generate samples with richer TF information, which was conducive to MCGAN feature extraction.By designing multiple CGAN of different scales and corresponding mixed loss functions, the original GAN network is improved to learn the internal distribution of bearing vibration signals and generate samples with fault characteristics and diversity of the original data.