Journal of Nature Science and Sustainable Technology ISSN: 1933-0324 Volume 9, Number 1 © 2015 Nova Science Publishers, Inc.
DEFECT CLASSIFICATION IN NONLINEAR SYSTEMS USING SELF-ORGANIZING MAPS B. Boudebbouz1,, I. Manssouri1,, Y. Ghanou2,, and T. Manssouri3,≠ 1
Laboratory of Mechanics, Mechatronics and Control, Moulay Ismail University, Meknes, Morocco 2 High School of Technology, University of Moulay Ismail Meknes, Morocco 3 Geo-Engineering and Environment Laboratory, Faculty of Science, University of Moulay Ismail Meknes, Morocco
ABSTRACT The present work describes a new approach to the detection and diagnosis of functional defects within a nonlinear system highly unstable. The highly unstable nonlinear system is a distillation column of methylcyclohexane from a toluene / methylcyclohexane, which mass composition was defined to 23% in methylcyclohexane. The latter allows the partial separation of the more volatile compound methylcyclohexane content in the liquid mixture. The originality of this work lies in the application of Kohonen network Self Organizing Map as a classification tool in unsupervised learning of normal and abnormal modes of a nonlinear system highly unstable. The performances of the classifier based on Kohonen neural networks (Self Organizing Map) are considerably superior to other classic statistical methods.
Keywords: fault detection and diagnosis, self organizing map, neural networks, clustering, distillation column.non linear system
1. INTRODUCTION The supervision of industrial processes can increase the productivity and reduce maintenance costs by improving the availability of production systems. These processes are likely to evolve under two types of modes: normal mode and abnormal modes. An abnormal mode of operation corresponds to a malfunctioning of the system leading to partially or completely non fulfillment of the task according to which these processes are designed.
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