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.

尽管滚动轴承的智能故障诊断有所进步，但在工业中，数据驱动的方法仍然存在数据获取和不平衡的问题。我们提出了一种增强的少拍 Wasserstein 自动编码器 (fs-WAE) 来扭转不平衡的负面影响。首先，提出了一种用于数据增强的增强型 WAE，其中应用挤压和激励块来自适应地校准通道特征响应，增强编码器的表示能力。其次，利用一种称为爬虫的元学习策略，进一步增强了 WAE 在面对小数据集时从先验分布到振动信号的映射能力。最后，引入梯度惩罚作为正则化项以提供灵活的优化功能。将所提出的方法应用于基于实验和工程数据集的模式识别。此外，比较结果证明了 fs-WAE 在效率和对不平衡程度的恢复能力方面相对于其他模型的实用性和优越性。