화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Chemical Engineering Research, Vol.46, No.2, 233-247, April, 2008
Export Citation
공정 모니터링 기술의 최근 연구 동향
Recent Research Trends of Process Monitoring Technology: State-of-the Art
유창규†, 최상욱1, 이인범2
  • 경희대학교 환경응용화학대학 그린에너지센터, 446-701 경기도 용인시 기흥구 서천동 1
  • 1삼성전자 반도체사업 메모리부, 445-701 경기도 화성시 반월동 산16
  • 2포항공과대학교 화학공학과, 794-784 경북 포항시 남구 효자동 산31
ChangKyoo Yoo†, Sang Wook Choi1, and In-Beum Lee2
  • College of Environmental and Applied Chemistry Green Energy Center, Kyung Hee University, 1 Seocheon-dong,Giheung-gu, Yongin, Gyeonggi 446-701, Korea
  • 1Memory Division, Semiconductor Business, Samsung Electronics Co., LTD., San 16, Banwol-dong, Hwasung, Gyeonggi 445-701, Korea
  • 2Department of Chemical Engineering, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk 790-784, Korea
E-mail:
초록
공정 모니터링 기술은 공정 내에서 일어나는 예상치 못한 조업변화 및 이상을 조기에 감지하고 조업 이상에 영향을 끼친 근본 원인을 밝혀내어 제거해 줌으로써 공정의 안정적인 조업과 양질의 제품생산의 기반을 제공하여 준다. 데이터에 기반한 통계적 공정 모니터링 방법은 양질의 공정 데이터만 주어진다면 통계적 처리를 접목하여 비교적 쉽게 모니터링을 할 수 있고 공정의 데이터 분석에 이용할 수 있는 도구를 얻을 수 있다는 장점이 있다. 그러나 실제 공정에서는 비선형성, non-Gaussianity, 다중 운전모드, 공정상태변화로 인해 기존의 다변량 통계적 방법을 이용한 공정 모니터링 기법은 비효율적이거나, 공정 감시 성능의 저하, 종종 신뢰할 수 없는 결과를 야기한다. 이러한 경우 기존의 방법으로는 더이상 공정을 정확히 감시할 수 없기 때문에 최근에 많은 새로운 방법들이 개발 되었다. 본 총설에서는 이러한 단점을 보안하기 위해 최근 주목할 만한 연구결과인 공정 비선형성을 고려한 커널주성분분석(kernel principle component analysis) 모니터링 기법, 주성분분석 모델 조합을 이용한 다중모델(mixture model) 모니터링 기법, 공정 변화를 고려한 적응모델(adaptive model) 모니터링 기법, 그리고 센서 이상진단과 보정의 이론과 응용결과에 대하여 소개한다.
Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.
Keywords:Adaptive Model;Fault Detection And Diagnosis;Gaussian Mixture Model;Kernel Principle Component Analysis (KPCA);Process Monitoring;Nonlinear Model;Sensor Fault Detection and Validation;Statistical Monitoring;Principal Component Analysis (PCA)
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