Digital twin-driven feature enhancement generative adversarial network for rolling bearings fault diagnosis

The difficulty of collecting fault samples of bearings under stable operation results in imbalanced data and considerably weakened capability of the deep learning-based intelligent fault diagnosis methods. Thus, a novel digital twin (DT)-driven feature enhancement generative adversarial network (DFGAN) was proposed in this study to augment the imbalanced multisensor data and improve diagnostic accuracy. First, a generic DT model with multiple degrees of freedom was developed to obtain simulated vibration data containing fault features. Subsequently, DFGAN was adopted to translate simulated data into measured data and generate synthetic samples with distributions similar to those of the measured samples. Specifically, the DFGAN incorporated an improved squeeze-and-excitation U-Net as the generator and integrated a spectral correlation loss to enhance the quality of synthetic samples. Finally, the imbalanced multisensor data were augmented with the synthetic samples, and bearing fault diagnosis was achieved by a multibranch convolutional neural network. Furthermore, the proposed method was verified to diagnose two rolling bearing datasets. The results reveal that the proposed method effectively augmented imbalanced data and significantly enhanced diagnostic performance.