Intelligent fault recognition has been a hot topic in the area of mechanical fault detection. However, it is difficult to collect sufficient monitoring data to represent the various kinds of faults and support network training. This paper proposes a convolutional adversarial auto-encoder (AE) for mechanical fault recognition with unseen classes via one-class classification. The generator is established based on the convolutional AE, while the discriminator is a multi-scale convolutional neural network. Through unsupervised adversarial training, the model can recognize unseen faults, which are not represented in the training data. The proposed method is verified by three bearing datasets, and some related research is also introduced for comparative analysis. Results show that the RR of the proposed method arrives at 100%, 100% and 97.3% in three cases, while the AC reaches 91.4%, 90.5% and 90.8% respectively.

智能故障识别已成为机械故障检测领域的热门话题。但是，很难收集足够的监视数据来表示各种故障并支持网络培训。本文提出了一种卷积对抗自动编码器（AE），用于通过一类分类对未知类别的机械故障进行识别。生成器是基于卷积AE建立的，而鉴别器是多尺度卷积神经网络。通过无人监督的对抗训练，模型可以识别未见过的错误，这些错误未在训练数据中表示。通过三个方位角数据集对提出的方法进行了验证，并介绍了一些相关研究进行比较分析。结果表明，该方法的RR分别为100％，100％和97。