본문 바로가기
  • Home

Analysis of detected anomalies in VOC reduction facilities using deep learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(4), pp.13-20
  • DOI : 10.9708/jksci.2023.28.04.013
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 16, 2023
  • Accepted : April 20, 2023
  • Published : April 28, 2023

Min-Ji Son 1 Myung Ho Kim 1

1숭실대학교

Accredited

ABSTRACT

In this paper, the actual data of VOC reduction facilities was analyzed through a model that detects and predicts data anomalies. Using the USAD model, which shows stable performance in the field of anomaly detection, anomalies in real-time data are detected and sensors that cause anomalies are searched. In addition, we propose a method of predicting and warning, when abnormalities that time will occur by predicting future outliers with an auto-regressive model. The experiment was conducted with the actual data of the VOC reduction facility, and the anomaly detection test results showed high detection rates with precision, recall, and F1-score of 98.54%, 89.08%, and 93.57%, respectively. As a result, averaging of the precision, recall, and F1-score for 8 sensors of detection rates were 99.64%, 99.37%, and 99.63%. In addition, the Hamming loss obtained to confirm the validity of the detection experiment for each sensor was 0.0058, showing stable performance. And the abnormal prediction test result showed stable performance with an average absolute error of 0.0902.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.