SCLIFD：有限故障数据下增量式故障诊断的有监督对比知识蒸馏
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
摘要：
智能故障诊断技术目前已经取得了长足的进步。然而，很少有工作解决有限故障数据下的类增量学习故障诊断，即，不平衡和长尾故障诊断带来了各种显著的挑战。最初，很难从有限的故障数据中提取有区别的特征。此外，一个经过良好训练的模型必须从头开始重新训练，才能从新的类别中对样本进行分类，从而造成很高的计算负担和时间消耗。此外，该模型在增量训练时可能遭受灾难性遗忘。最后，由于类不平衡，模型决策偏向于新类。这些问题会导致故障诊断模型的性能下降。针对上述问题，提出了一种有限故障数据下的增量式故障诊断监督对比知识提取（SCLIPD）框架，从三个方面扩展了经典增量式分类器和表示学习（iCaRL）框架.首先，我们采用有监督的对比知识提取（KD），以提高其表示学习能力，在有限的故障数据。此外，我们提出了一种新的优先级样本选择方法自适应羊群（AdaHerding），以限制计算负担的增加，这也是结合KD，以减轻灾难性遗忘。此外，我们采用余弦分类器，以减轻类不平衡的不利影响。我们进行了广泛的实验模拟和现实世界的工业过程中不同的不平衡比。实验结果表明，我们的SCLIFD优于现有的方法，由一个很大的利润。
英文摘要：
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.
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