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2014 Fault Detection and Diagnosis in Process Data Using Support Vector Machines
Fang Wu, Shen Yin, Hamid Reza Karimi
J. Appl. Math. 2014: 1-9 (2014). DOI: 10.1155/2014/732104


For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA T2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP) are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.


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Fang Wu. Shen Yin. Hamid Reza Karimi. "Fault Detection and Diagnosis in Process Data Using Support Vector Machines." J. Appl. Math. 2014 1 - 9, 2014.


Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 1267.70010
Digital Object Identifier: 10.1155/2014/732104

Rights: Copyright © 2014 Hindawi


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