Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels
This work addresses the need for robust fault detection in industrial processes, but it is incremental as it builds on existing deep learning techniques with specific enhancements.
The paper tackled the problem of detecting previously unseen fault types in industrial inspection systems by proposing a method that uses hierarchical fault taxonomy labels to improve detection performance without compromising model accuracy, achieving increased detection performance on hot steel rolling process images.
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.