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Language: korean

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received December 11, 2007
Accepted February 5, 2008

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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데이터 기반 이상진단법을 위한 화학공정의 조업모드 판별

Operation Modes Classification of Chemical Processes for History Data-Based Fault Diagnosis Methods

이창준 고재욱¹ 이기백^2†

Chang Jun Lee Jae Wook Ko¹ Gibaek Lee^2†

서울대학교 화학생물공학부, 151-742 서울시 신림동 산 56-1 ¹광운대학교 화학공학과, 139-701 서울시 노원구 월계동 447-1 ²충주대학교 화공생물공학과, 380-702 충북 충주시 대학로 72

Department of Chemical and Biological Engineering, Seoul National University, San 56-1, Shilim-dong, Gwanak-gu, Seoul 151-742, Korea ¹Department of Chemical Engineering, Kwangwoon University, 447-1 Wolgye-dong, Nowon-gu, Seoul 139-701, Korea ²Department of Chemical and Biological Engineering, Chungju National University, 72 Daehak-ro, Chungju, Chungbuk 380-702, Korea

glee@cjnu.ac.kr

Korean Chemical Engineering Research, April 2008, 46(2), 383-388(6), NONE Epub 29 May 2008

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Abstract

화학공정의 안전하고 효율적인 운전에 관심이 커지면서 공정이상의 원인을 조기에 진단하기 위한 다양한 이상진단 방법이 연구되어 왔다. 최근에는 통계적 모델 등 정량적 데이터에 기반한 이상진단방법이 많이 연구되고 있으나, 특정 조업영역에서 얻어진 통계적 모델을 다른 조업영역에 적용하면 오진단이 많아지게 된다. 따라서 공정특성상 다양한 조업영역이 존재하는 화학공정에 데이터기반 방법론을 적용하기에는 어려움이 있어 화학공정의 조업영역 판별법이 요구 되고 있다. 이 연구에서는 유클리드 거리(Euclidean distance), FDA(Fisher's discriminant analysis), PCA(principal component analysis)의 통계모델과 이 모델들에 공정변수의 동특성을 반영한 모델을 제안하였다. 6개의 조업모드를 가진 TE(tennessee eastman) 공정에 대한 사례연구를 통해 동특성을 반영한 PCA 모델의 성능이 가장 우수함을 확인하였다.

The safe and efficient operation of the chemical processes has become one of the primary concerns of chemical companies, and a variety of fault diagnosis methods have been developed to diagnose faults when abnormal situations arise. Recently, many research efforts have focused on fault diagnosis methods based on quantitative history data-based methods such as statistical models. However, when the history data-based models trained with the data obtained on an operation mode are applied to another operating condition, the models can make continuous wrong diagnosis, and have limits to be applied to real chemical processes with various operation modes. In order to classify operation modes of chemical processes, this study considers three multivariate models of Euclidean distance, FDA (Fisher's Discriminant Analysis), and PCA (principal component analysis), and integrates them with process dynamics to lead dynamic Euclidean distance, dynamic FDA, and dynamic PCA. A case study of the TE (Tennessee Eastman) process having six operation modes illustrates the conclusion that dynamic PCA model shows the best classification performance.

Keywords

Fault Diagnosis Operation Mode Classification Principal Component Analysis Fisher Discriminant Analysis Euclidean Distance Tennessee Eastman Process

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