Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning

Despite the advance of intelligent fault diagnosis for rolling bearings, in industries, data-driven methods still suffer from data acquisition and imbalance. We propose an enhanced few-shot Wasserstein auto-encoder (fs-WAE) to reverse the negative effect of imbalance. Firstly, an enhanced WAE is proposed for data augmentation, in which squeeze-and-excitation blocks are applied to calibrate channel-wise feature responses adaptively, strengthening the representational power of encoder. Secondly, a meta-learning strategy called Reptile is utilized to further enhance the mapping ability of WAE from prior distribution to vibration signals in the face of small dataset. Finally, gradient penalty is introduced as a regularization term to provide a flexible optimization function. The proposed method is applied to the pattern recognition based on experimental and engineering datasets. Moreover, comparative results demonstrate the utility and superiority of fs-WAE over other models in terms of efficiency and the resilience to imbalance degree.