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Fault diagnosis of rotary kiln using SVM and binary ACO

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Abstract

This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms.

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Authors and Affiliations

  1. Laboratory of Automation & Production Engineering, University of Batna, 1 Rue Chahid Bokhlof, Batna, Algeria

    Ouahab Kadri, Leila Hayet Mouss & Mohamed Djamel Mouss

Authors
  1. Ouahab Kadri
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  2. Leila Hayet Mouss
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  3. Mohamed Djamel Mouss
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Corresponding author

Correspondence to Ouahab Kadri.

Additional information

This paper was recommended for publication in revised form by Editor Sung-Lim Ko

Ouahab Kadri received his magister degree from the Department of Computer Science, University of Batna, Algeria, in 2004. He is currently an assistant professor at the University of Khenchela, Algeria. He is currently a Doctoral student in the Department of Industrial Engineering, University of Batna, Algeria. His current research interests include evolutionary computation, artificial intelligence, etc.

Leila Hayet Mouss was born in Batna, Algeria, in 1954. She received the B.Sc. degree in Electrical Engineering, in 1979, from the National Polytechnic School of Algiers, Algeria; the M.Sc. degree in Electrical and Computer Engineering, in 1982, from the ENSERB, France; and finally the Ph.D. degree also in Electrical and Computer Engineering, in 1985, Bordeaux University, France. After graduation, she joined the University of Batna, Algeria, where she is an Associate Professor of Electrical and Computer Engineering. Pr. Mouss is a member of New York Science Academy. She is the head of Automatic and Computer Integrated Manufacturing Laboratory. Pr. Mouss current research interests include industrial Diagnosis of production system using the artificial intelligence techniques in the LAP Lab (Laboratoire d’Automatique et Productique) at Batna, Algeria.

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Kadri, O., Mouss, L.H. & Mouss, M.D. Fault diagnosis of rotary kiln using SVM and binary ACO. J Mech Sci Technol 26, 601–608 (2012). https://doi.org/10.1007/s12206-011-1216-z

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  • DOI: https://doi.org/10.1007/s12206-011-1216-z

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