Journal of Applied Mathematics

Fault Detection and Diagnosis in Process Data Using Support Vector Machines

Fang Wu, Shen Yin, and Hamid Reza Karimi

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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 T 2 , 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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J. Appl. Math., Volume 2014 (2014), Article ID 732104, 9 pages.

First available in Project Euclid: 2 March 2015

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Wu, Fang; Yin, Shen; Karimi, Hamid Reza. Fault Detection and Diagnosis in Process Data Using Support Vector Machines. J. Appl. Math. 2014 (2014), Article ID 732104, 9 pages. doi:10.1155/2014/732104.

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