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The Use of Local Outlier Factor(LOF) for Improving Performance of Independent Component Analysis(ICA) based Statistical Process Control(SPC)  

Lee, Jae-Shin (서울대학교 산업공학과)
Kang, Bok-Young (서울대학교 산업공학과)
Kang, Suk-Ho (서울대학교 산업공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.36, no.1, 2011 , pp. 39-55 More about this Journal
Abstract
Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.
Keywords
Statistical Process Control(SPC); Local Outlier Factor(LOF); Independent Component Analysis(ICA); Process Monitoring; Fault Detection; Tennessee Eastman process;
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