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.

现代故障分类系统中的一个重要特征是在面对以前未见过的故障类型时能够警示系统。本文考虑了基于深度神经网络的故障分类器的未知故障检测能力。具体而言，我们提出了一种方法，即在可用的情况下，使用有关故障分类法（fault taxonomy）的标签来提高未知故障检测性能，而不损失模型性能。为实现这一目的，我们提出利用软标签技术来改进先进的深度新颖故障检测技术，并提出用于在线新颖故障检测的层次一致的检测统计量。最后，我们在热轧钢生产过程中的检测图像中展示了对新颖故障检测的提高性能，结果在多个场景和基准检测方法中得到了良好的复制。

基于深度学习分类器的图像新型故障检测与分级标签