Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Fault Detection Method for Multivariate Process using ICA

독립성분분석을 이용한 다변량 공정에서의 고장탐지 방법

  • Jung, Seunghwan (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Minseok (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Lee, Hansoo (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Jonggeun (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Sungshin (Department of Electrical and Computer Engineering, Pusan National University)
  • Received : 2019.12.13
  • Accepted : 2020.01.01
  • Published : 2020.02.29
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Abstract

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

대규모 발전소나 화학공정과 같은 다변량 공정은 매우 위험한 환경에서 운전되기 때문에 고장이 발생하면 심각한 인적·물적 손실이 발생할 수 있다. 따라서 시스템의 고장을 사전에 탐지할 수 있는 온라인 모니터링 기술이 필수적이다. 본 논문에서는 세 가지의 다른 다변량 공정 데이터에 ICA를 적용하여 고장탐지를 수행하였고, PCA와 성능을 비교하였다. ICA 기반의 고장탐지 절차는 크게 오프라인 과정과 온라인 과정으로 나뉜다. 오프라인 과정에서는 시스템이 정상일 때 계측된 데이터를 이용하여 고장판별을 위한 문턱 값을 설정한다. 그리고 온라인 과정에서는 실시간으로 계측되는 질의벡터에 대한 통계량을 계산한 후, 계산된 통계량과 사전에 정의된 문턱 값과 비교하여 고장을 판별한다. 본 논문에서 이용한 세 가지의 다변량 공정 데이터에 실험한 결과, ICA 기반 고장탐지 방법이 시스템의 고장을 사전에 탐지하였고, PCA 보다 우수한 고장탐지 성능을 보여주었다.

Keywords

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