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Aggoune L, Chetouani Y, Raïssi T. Fault detection in the distillation column process using Kullback Leibler divergence. ISA Trans. 2016;63:394-400doi: 10.1016/j.isatra.2016.03.006.
Aggoune, L., Chetouani, Y., & Raïssi, T. (2016). Fault detection in the distillation column process using Kullback Leibler divergence. ISA transactions, 63394-400. https://doi.org/10.1016/j.isatra.2016.03.006
Aggoune, Lakhdar, et al. "Fault detection in the distillation column process using Kullback Leibler divergence." ISA transactions vol. 63 (2016): 394-400. doi: https://doi.org/10.1016/j.isatra.2016.03.006
Aggoune L, Chetouani Y, Raïssi T. Fault detection in the distillation column process using Kullback Leibler divergence. ISA Trans. 2016 Jul;63:394-400. doi: 10.1016/j.isatra.2016.03.006. Epub 2016 Mar 25. PMID: 27020311.
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ISA Trans. 2016 Jul;63:394-400. doi: 10.1016/j.isatra.2016.03.006. Epub 2016 Mar 25.

Fault detection in the distillation column process using Kullback Leibler divergence.

ISA transactions

Lakhdar Aggoune, Yahya Chetouani, Tarek Raïssi

Affiliations

  1. Laboratoire d'Automatique de Sétif, Département d'Electrotechnique, Université de Sétif 1, Cité Maabouda, Route de Béjaia, 19000 Sétif, Algeria; Université de Rouen, Département Génie Chimique, Rue Lavoisier, 76821 Mont Saint Aignan Cedex, France; Conservatoire National des Arts et Métiers, Département EASY, Cedric-laetitia, 292, Rue St-Martin, case 2D2P10, 75141 Paris Cedex 03, France. Electronic address: [email protected].
  2. Laboratoire d'Automatique de Sétif, Département d'Electrotechnique, Université de Sétif 1, Cité Maabouda, Route de Béjaia, 19000 Sétif, Algeria; Université de Rouen, Département Génie Chimique, Rue Lavoisier, 76821 Mont Saint Aignan Cedex, France; Conservatoire National des Arts et Métiers, Département EASY, Cedric-laetitia, 292, Rue St-Martin, case 2D2P10, 75141 Paris Cedex 03, France.

PMID: 27020311 DOI: 10.1016/j.isatra.2016.03.006

Abstract

Chemical plants are complex large-scale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the black-box modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model is firstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators.

Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Keywords: Dynamic processes; Fault detection; Kullback Leibler divergence; NARMAX model; Safety

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