The abnormal detection of rotating machinery under small sample size conditions is of prime importance in the field of fault diagnosis. In this work, we proposed an unsupervised representation learning method called Bidirectional InfoMax GAN (BIMGAN), which can perform fast and effective feature extraction and fault recognition with few samples. First, we obtain the low-dimensional feature representation by a prior normalized encoder and reconstruction of the sample via the generator. Second, the mapping relationship between the sample and its corresponding feature representation is learned by maximizing mutual information estimation with the constraint of the feature matching (FM) strategy. Different from the general GANs, we are aiming at learning a good feature mapping of an encoder to capture the feature representation instead of reconstructing realistic samples. And then, a supervised pattern recognition task based on the feature representation is conducted for fault diagnosis. Finally, the inverse mapping learned by the encoder is visualized and the effectiveness is demonstrated. And the performance of the proposed method outperforms several advanced unsupervised methods on two case studies of rolling bearings fault recognition with some standard architectures, where the average accuracy can achieve 99.73% and 98.36% respectively.

小样本条件下旋转机械的异常检测在故障诊断领域至关重要。在这项工作中，我们提出了一种称为双向 InfoMax GAN (BIMGAN) 的无监督表示学习方法，它可以用很少的样本进行快速有效的特征提取和故障识别。首先，我们通过先验归一化编码器获得低维特征表示，并通过生成器重建样本。其次，在特征匹配（FM）策略的约束下，通过最大化互信息估计来学习样本与其对应的特征表示之间的映射关系。与一般的 GAN 不同，我们的目标是学习编码器的良好特征映射以捕获特征表示，而不是重建真实样本。然后，基于特征表示的监督模式识别任务进行故障诊断。最后，将编码器学习的逆映射可视化并证明其有效性。并且所提出的方法在具有一些标准架构的滚动轴承故障识别的两个案例研究中的性能优于几种先进的无监督方法，平均准确率可以分别达到 99.73% 和 98.36%。编码器学习的逆映射被可视化并证明其有效性。并且所提出的方法在具有一些标准架构的滚动轴承故障识别的两个案例研究中的性能优于几种先进的无监督方法，平均准确率可以分别达到 99.73% 和 98.36%。编码器学习的逆映射被可视化并证明其有效性。并且所提出的方法在具有一些标准架构的滚动轴承故障识别的两个案例研究中的性能优于几种先进的无监督方法，平均准确率可以分别达到 99.73% 和 98.36%。