Abstract
Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.
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Jiang, Q., Yan, X., Lv, Z. et al. Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure. Korean J. Chem. Eng. 30, 1181–1186 (2013). https://doi.org/10.1007/s11814-013-0034-7
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DOI: https://doi.org/10.1007/s11814-013-0034-7