  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.
