SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control. (September 2022)

• Record Type: Journal Article
• Authors: Pulsipher, Joshua L.; Coutinho, Luke D.J.; Soderstrom, Tyler A.; Zavala, Victor M.
• Is Part Of: Journal of process control. Volume 117(2022)
• Page Start: 78
• Page End: 97
• Publication Date: 2022-09
• Subjects: Computer vision -- Novelty detection -- Deep learning -- Process control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.07.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments. Highlights: Present novelty detection framework for convolutional neural net (CNN) sensors. CNN sensors enable incorporation of image data into control architectures. Novelty framework enables robust predictions by CNN sensors.