VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring

Abstract

Process monitoring at industrial sites contributes to system stability by detecting and diagnosing unexpected changes in a system. Today, as the infrastructure of industrial sites is advancing because of the development of communication technology, vast amounts of data are generated, and the importance of a way to effectively monitor such data in order to diagnose a system is increasing daily. Because a method based on a deep neural network can effectively extract information from a large amount of data, methods have been proposed to monitor processes using such networks to detect system faults and abnormalities. Neural-network-based process monitoring is effective in detecting faults, but has difficulty in diagnosing because of the limitations of the black-box model. Therefore, in this paper we propose a process-monitoring framework that can detect and diagnose faults. The proposed method uses a class activation map that results from diagnosis of faults and abnormalities, and verifies the diagnosis by post-processing the class activation map. This improves the detection of faults and abnormalities and generates a class activation map that provides a more verified diagnosis to the end user. In order to evaluate the performance of the proposed method, we did a simulation using publicly available industrial motor datasets. In addition, after establishing a system that can apply the proposed method to actual manufacturing companies that produce sapphire nozzles, we carried out a case study on whether fault detection and diagnosis were possible.

Rethinking Robustness of Model Attributions

“…The explosive increase in the use of deep neural network (DNN)-based models for applications across domains has resulted in a very strong need to find ways to interpret the decisions made by these models (Gade et al 2020;Tang et al 2021;Yap et al 2021;Oviedo et al 2022;Oh and Jeong 2020). Interpretability is an important aspect of responsible and trustworthy AI, and model explanation methods (also known as attribution methods) are an important aspect of the community's efforts towards explaining and debugging real-world AI/ML systems.…”

Artificial Intelligence for Smart Manufacturing: Methods and Applications

“…It attempts to cover the issues related to key enabling technologies for smart manufacturing such as product quality inspection based on deep learning, remaining useful life prediction for predictive maintenance based on deep learning, Machine Vision Systems, intelligent recommender system, Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning, Real-Time and Explainable Process Monitoring, Intelligence-Driven Decision Support System. These contributions represent an advance in the state-of-the-art of key enabling technologies for smart manufacturing [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The richness and diverseness of the papers submitted to this Special Issue confirm the importance of applications of AI in Smart Manufacturing.…”