S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Industrial Engineering (산업공학과) Journal Papers (저널논문_산업공학과)
Integrating independent component analysis and local outlier factor for plant-wide process monitoring
- Lee, Jaeshin; Kang, Bokyoung; Kang, Suk Ho
- Issue Date
- ELSEVIER SCI LTD
- JOURNAL OF PROCESS CONTROL; Vol.21 7; 1011-1021
- Local outlier factor; Tennessee Eastman process; Process monitoring; Independent component analysis; Fault detection; Multivariate statistical process control
- We propose a novel process monitoring method integrating independent component analysis (ICA) and local outlier factor (LOF). LOF is a recently developed outlier detection technique which is a density-based outlierness calculation method. In the proposed monitoring scheme, ICA transformation is performed and the control limit of LOF value is obtained based on the normal operating condition (NOC) dataset. Then, at the monitoring phase, the LOF value of current observation is computed at each monitoring time, which determines whether the current process is a fault or not. The comparison experiments are conducted with existing ICA-based monitoring schemes on widely used benchmark processes, a simple multivariate process and the Tennessee Eastman process. The proposed scheme shows the improved accuracy over existing schemes. By adopting LOF, the monitoring statistic is computed regardless of data distribution. Therefore, the proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non-Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution. (C) 2011 Elsevier Ltd. All rights reserved.
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