Conditional Variational Encoder Classifier for Open Set Fault Classification of Rotating Machinery Vibration Signals

Deep-learning-based fault diagnosis models perform well when the training and test sets have the same label set. However, these models are invalid in practical applications because they misclassify any unknown faults into existing known classes. An effective diagnosis model for practical industrial applications requires the ability to detect unknown faults as well as maintain high classification accuracy on known faults. To address this challenge, this article proposes a generic open-set classification method for vibration signals. We propose a variational encoder-classifier structure to extract the robust latent features that have different specific distributions with respect to their classes. According to the distances between the latent feature distributions, the samples from unknown faults are rejected using extreme value theory (EVT) and empirical threshold. In addition, we devised an EVT-based instance-level regularization weight function to allow the model to enhance the regularization on the samples that around the known and unknown decision boundaries, which can reduce the risk of bias in the empirical threshold setting caused by the hard training samples. Experimental results on five public rotating machinery vibration datasets reveal that the proposed method achieves the best performance for each dataset. This demonstrates the effectiveness and superiority of the proposed method for practical application scenarios.