Research Papers

Tennessee Eastman Process Diagnosis Based on Dynamic Classification With SVDD

[+] Author and Article Information
Foued Theljani

National Engineering School of Tunis,
University of Tunis El Manar,
BP 37,
Le Belvedere 1002, Tunisia
e-mail: foued.theljani@enit.rnu.tn

Kaouther Laabidi, Moufida Ksouri

National Engineering School of Tunis,
University of Tunis El Manar,
BP 37,
Le Belvedere 1002, Tunisia

Salah Zidi

Lille 1 University - Science and Technology,
Villeneuve d'Ascq 59650,
Lille, France

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 29, 2013; final manuscript received April 22, 2015; published online June 4, 2015. Assoc. Editor: Srinivasa M. Salapaka.

J. Dyn. Sys., Meas., Control 137(9), 091006 (Sep 01, 2015) (10 pages) Paper No: DS-13-1421; doi: 10.1115/1.4030429 History: Received October 29, 2013; Revised April 22, 2015; Online June 04, 2015

The support vector domain description (SVDD) is an efficient kernel method inspired from the SV machine (SVM) by Vapnik. It is commonly used for one-classification problems or novelty detection. The training algorithm solves a constrained convex quadratic programming (QP) problem. This assumes prior dense sampling (offline training) and it requires large memory and enormous amounts of training time. In this paper, we propose a fast SVDD dedicated for multiclassification problems. The proposed classifier deals with stationary as well as nonstationary (NS) data. The principle is based on the dynamic removal/insertion of informations according to adequate rules. To ensure the rapidity of convergence, the algorithm considers in each run a limited frame of samples for the training process. These samples are selected according to some approximations based on Karush–Kuhn–Tucker (KKT) conditions. An additional merge mechanism is proposed to avoid local optima drawbacks and improve performances. The developed method is assessed on some synthetic data to prove its effectiveness. Afterward, it is employed to solve a diagnosis problem and faults detection. We considered for this purpose a real industrial plant consisting in Tennessee Eastman process (TEP).

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Fig. 1

Novel detection for drifting Gaussian distribution

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Fig. 2

Novel detection for drifting banana distribution

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Fig. 3

Cardinality variation of the training set and the SVs set associated to the Gaussian distribution

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Fig. 4

Cardinality variation of the training set and the SVs set associated to the banana distribution

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Fig. 5

Two drifting clusters: (a) without merge mechanism and (b) with merge mechanism

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Fig. 6

A diagram of the TEP simulator

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Fig. 7

Detection provided by NS-SVDD based on the computation of the decision function score




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