International Journal of Mathematics and Computers in Simulation


ISSN: 1998-0159
Volume 12, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 12, 2018


Title of the Paper: Comparison of Different Approaches to Continuous-Time System Identification from Sampled Data

 

Authors: Martin Tuma, Pavel Jura

Pages: 9-13

Abstract: This article deals with different approaches to continuous-time system identification from sampled data. Continuous-time system identification is important problem in control theory. Continuous time models provide many advantages against discrete time models because of better physical insight into the system properties. The traditional approach with least squares method with state variable filters is presented. Two alternative approaches to continuous-time identification are proposed. The generalized Laguerre functions method and the method based on least squares estimation with numerical solution of differential equation are introduced. These three different approaches to continuous-time system identification from sampled data are compared on the example. It is shown that proposed alternative methods can give better results in terms of relative root mean square error of the outputs of the identified systems than the least squares method with state variable filters.


Title of the Paper: Multiclass SVM Bearing Fault Diagnosis of Induction Motors Using Hilbert Huang Transform

 

Authors: Mohamed Nacer Saadi, Messaoud Boukhenaf, Abdelghani Redjati, Noureddine Guersi

Pages: 1-8

Abstract: Fault detection is a major challenge for asynchronous motor maintenance. Bearing defects are the most important defects that can occur in theseIn this context, we propose a new approach using Hilbert Huang transform-based stator current analysis (HHT) and multi-class support vector (MSVM) machines for the diagnosis of these failures.Experimental data, obtained from the stator current of the asynchronous motor subjected to various loads in the healthy and faulty cases of the bearings, are analyzed and classified. The applied MSVM classifier is able to identify the type of faulty bearing and our experimental results demonstrate the effectiveness of the proposed method.