Discriminative Angle Feature Learning for Open-Set Deep Fault Classification

Unknown faults may occur in practical applications, necessitating an open-set classifier that can classify known classes as well as recognize unknown faults. The current deep open-set classification methods are implicit in optimizing the intra- or inter-class distances, which may result in performance degradation when the number of unknown classes far exceeds that of the known. In this study, the discriminative angle deep features for vibration signals are investigated. A novel normalized one-versus-all classification loss with center and contrastive regularization is proposed. The trained network can explicitly optimize deep features to ensure intra-class compactness and inter-class divergence. In this case, such discriminative features can be used for open-set fault classification. Furthermore, the effectiveness of the proposed method is verified using field-measured motor bearing and gear vibration signals. The results demonstrate the evident advantages of our proposed method over other approaches in practical fault diagnosis scenarios.