  Intelligent fault diagnosis has made extraordinary advancements currently.
Nonetheless, few works tackle class-incremental learning for fault diagnosis
under limited fault data, i.e., imbalanced and long-tailed fault diagnosis,
which brings about various notable challenges. Initially, it is difficult to
extract discriminative features from limited fault data. Moreover, a
well-trained model must be retrained from scratch to classify the samples from
new classes, thus causing a high computational burden and time consumption.
Furthermore, the model may suffer from catastrophic forgetting when trained
incrementally. Finally, the model decision is biased toward the new classes due
to the class imbalance. The problems can consequently lead to performance
degradation of fault diagnosis models. Accordingly, we introduce a supervised
contrastive knowledge distillation for incremental fault diagnosis under
limited fault data (SCLIFD) framework to address these issues, which extends
the classical incremental classifier and representation learning (iCaRL)
framework from three perspectives. Primarily, we adopt supervised contrastive
knowledge distillation (KD) to enhance its representation learning capability
under limited fault data. Moreover, we propose a novel prioritized exemplar
selection method adaptive herding (AdaHerding) to restrict the increase of the
computational burden, which is also combined with KD to alleviate catastrophic
forgetting. Additionally, we adopt the cosine classifier to mitigate the
adverse impact of class imbalance. We conduct extensive experiments on
simulated and real-world industrial processes under different imbalance ratios.
Experimental results show that our SCLIFD outperforms the existing methods by a
large margin.
