Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
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Model Parameter Based Fault Detection for Time-series Data

시계열을 따르는 공정데이터의 모델 모수기반 이상탐지

  • 박시저 (고려대학교 산업경영공학과) ;
  • 박정술 (고려대학교 산업경영공학과) ;
  • 김성식 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2011.10.24
  • Accepted : 2011.12.06
  • Published : 2011.12.31
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Abstract

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

본 연구에서는 시계열 공정데이터 관리를 위한 모델모수 기반 이상 탐지방법을 제안한다. 일반적인 공정관리에 널리 쓰이는 전통적인 통계적 관리기법의 관리도(SPC chart)는 측정되는 데이터가 특정 분포를 따르며 상관관계가 없는 상황을 가정한다. 따라서 공정데이터 형태가 시계열데이터와 같이 특정분포를 따르지 않고, 자기상관관계를 갖는다면 전통적인 관리도로는 관리에 한계를 보인다. 본 연구는 시계열을 따르는 공정의 이상을 탐지를 위한 MPBC(Model Parameter Based Control-chart) 방법을 제안한다. 제안된 MPBC는 시계열공정을 모델링하고, 모델모수의 변화를 감지하여 공정의 이상을 탐지하는 방법이다. 시계열 공정은 ARMA(p,q) 모델을 가정하며, RLS(Recursive Least Square)를 이용하여 시계열 모델의 모수를 추정하고, 추정된 모수를 $K^2$관리도로 관리한다. 제안된 방법은 기존 알고리즘과 비교하여 시계열 공정 변화 탐지에 우수한 성능을 보였으며 시계열 데이터에 있어서 보다 효율적인 공정관리 방향을 제시한다.

Keywords

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