One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

翻译：现代故障分类系统的重要特征之一是能够针对前所未见的故障类型发出预警信号。本研究探讨了基于深度神经网络的故障分类器对未知故障的检测能力。具体而言，我们提出了一种方法论，说明在获得故障分类标签时，如何利用这些标签提升未知故障检测性能，同时保持模型性能不受影响。为此，我们建议在训练过程中采用软标签技术来改进当前最优的深度新型故障检测方法，并提出用于在线新型故障检测的层级一致性检测统计量。最后，我们在热钢轧制过程检测图像中验证了新型故障检测性能的提升，该结果在多个场景和基准检测方法中均得到良好复现。