ScholarWorks@숭실대학교

초록

The use of regenerative thermal oxidizers (RTOs), which reduce hazardous air pollution and save energy, has increased with the rapid growth of industrial technology. Therefore, detecting and explaining anomalies in RTOs have become important. To accurately detect anomalies in RTOs, it is required to apply reconstruction-based anomaly detection (AD) models, which is currently proposed as a main AD research area. However, traditional explainable artificial intelligence (XAI) cannot explain reconstruction-based AD models to identify main facilities in RTOs. To address this problem, we developed a method to improve the accuracy of XAI in explaining reconstruction-based AD models. Specifically, we first grouped the variables based on correlation and the clustering analysis. We then calculated the impact of each group on normal/abnormal events in terms of the maximum mean discrepancy and cosine similarity. Using the most influencing variables based on our method, XAI correctly identified the main variables without considering unnecessary variables. Experimental results on a real-world RTO dataset showed that our method improve the accuracy of XAI that determine the main variables compared to the traditional XAI.