Automatique
Modélisation et Commande Vectorielle d’une Machine Asynchrone à Sept phase
In this work, we present the modeling and vector control of seven-phase induction machine, regularly shifted them in space. The modeling is based on the method of diagonalization of matrices inductors to magnetically decouple phases. Secondly, the vector control used is intended to obtain the excellent performance of the DC machine Voir les détails
Mots clés : Seven-phase Asynchronous machine, diagonalization, vector control
Simulation of the variation in temperaturein a material without and with default
In this article our principal study is the simulation ofthe variation in temperature in a solid material in absence and inpresence the default, in particular a fracture on the level of surfaceof material, and to see how the default influences on heat transferin a solid. The simulation is made by FLUENT software whichpermits us to solve the energy equation by finite volumes method. Voir les détails
Mots clés : heat transfer; simulation; material; default
Détection et Classification de Defaults dans les Machines Tournantes par l’Analyse en Composante Principale Multi-Echelle
L’analyse en composantes principales à multi-échelles (MSPCA) et le classificateur Neuro logique floue sont considérés parmi les méthodes modernes, et qui est utilisée dans nombreux applications de classifications. D’entre eux une nouvelle application dans les machines tournantes, le diagnostic des defaults est proposé. Le modèle ACP à multi-échelle est construit par la variance des cinq niveaux de décomposition en ondelette. Comme un vecteur de caractéristiques, ces caractéristiques sont entrainées par le classificateur neuro flou pour diagnostiquer le défaut. La performance de la méthode sera discutée, et les résultants montrent l’efficacité de l’approche proposée. Voir les détails
Mots clés : ACP multi échelle, Classifieur Neuro flou, diagnostique de défaut
Task Performance Evaluation for Industrial Robots
Integrated multi-modal Supervisory Control Systems(ISCS) are a new generation of complex and synergistic Human-Machine Interaction Systems (HMIS). This paper deals withmulti-modal interaction and control applied to HRS. A taskperformance evaluation technique dedicated for multi-modalinteraction and control is proposed. It enables comparison oftask performance carried out by using different selection ofcontrol modes or by different operators. Objective andsubjective performance measures are defined. Based on theanalytical hierarchy process method (AHP) which takes intoaccount qualitative and quantitative attributes and criteria.Experimental results have been carried out and somepreliminary results will be presented concerning industrialmanipulators. Voir les détails
Mots clés : Task performance evaluation, Robotics, human factors, Analytical Hierarchy Process, Adaptive supervisory control, Multi-modal interaction, Graphical-user interface.
Surveillance de machines tournantes par la transformée en ondelettes
Les systèmes industriels sont devenus de plus en plus complexes. Leur surveillance est primordiale en raison de la demande d’un rendement élevé, d’une grande sûreté et d’une bonne fiabilité. Diverses techniques de surveillance ont été développées telles que l’analyse des forces dynamiques, la tribologie, l’analyse des vibrations et les techniques non destructives des structures et des machines tournantes. La surveillance par l’analyse des vibrations est essentielle, du fait que les vibrations sont porteuses d’informations qui caractérisent l’état de fonctionnement de certains composants ou éléments mécaniques de la machine. Elle permet de mettre en évidence un grand nombre de défaillances causant une diminution de la durée de vie de la machine telles que le défaut de balourd ou d’équilibrage des parties tournantes, un mauvais alignement d’une ligne d’arbre, des engrenages usés ou endommagés, roulements défectueux, les frottements, etc. La surveillance des vibrations est généralement réalisée par l’analyse de signaux recueillis sur la machine en fonctionnement. Ces mesures, qui représentent en fait, certains éléments de la machine, s’avèrent difficiles à lire à cause de la nature des signaux et de bruit qu’elles peuvent contenir. Toutefois, il existe des méthodes de caractérisation, les plus communes se basent sur les significations de l’amplitude du signal telles que la valeur crête, la valeur crête à crête, la valeur efficace, le kurtosis et le facteur de crête. En effet, ces paramètres sont très utiles pour la détection, par contre ils sont insuffisants pour une surveillance complète. Actuellement et suite au développement des techniques de traitement du signal, il est devenu possible d’extraire l’information utile à partir des données vibratoires. Dans le domaine de la surveillance des vibrations, l’analyse des signaux stationnaires a été en grande partie basée sur l’analyse spectrale ou la transformée de Fourier (TF). En pratique, la plupart des signaux vibratoires sont de type non stationnaire. La représentation spectrale devient limitée et ainsi, toutes les méthodes, basées sur la TF ont plus ou moins, les mêmes limites. Afin de procéder à un diagnostic correct, il est utile de pousser les investigations en utilisant des techniques plus adaptées telle que la Transformée en Ondelettes (TO). Dans ce travail, la TO est présentée en utilisant des mesures réelles acquises par le biais des accéléromètres piézoélectriques, afin d’obtenir l’information la plus détaillée des données mesurées. Voir les détails
Mots clés : surveillance, machine tournante, vibrations, analyse spectrale, Transformée en ondelettes
Bearing fault diagnosis based on feature extraction and condition classification
Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of signal's time-varying statistical parameters and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, an 8-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach. Voir les détails
Mots clés : Condition monitoring, Discrete wavelet transform, Roller Bearing, Particle Swarm Optimization, Statistical parameters, Support vector machine
Wavelet Transform for Bearing Faults Diagnosis
Fault diagnosis is useful for ensuring the safe running of machines. Vibration analysis is one of the most important techniques for fault diagnosis of rotating machinery; as the vibration signal carries the dynamic information of the system. Many signal analysis methods are able to extract useful information from vibration data. In the present work, we are interested to the vibration signal analysis by the wavelet transform. The monitoring results indicate that the wavelet transform can diagnose the abnormal change in the measured data. Voir les détails
Mots clés : fault diagnosis; vibration analysis; rotating machinery; monitoring; wavelet transform
Adaptive support vector machine-based surface qualityevaluation and temperature monitoring. Applicationto billet continuous casting process
A method for surface quality evaluation and tem-perature monitoring of the billet in continuous casting isconsidered in this paper. This method uses the differencebetween the measured and the filtered temperature comput-ed using an adaptive support vector machine method. Thetemperature field, measured by an infrared camera, is affect-ed by an important noise called calamine (a metal oxidegenerated during the cooling process). The quality of thebillet ’ s surface temperature is connected to the secondarycooling behavior, and therefore an evaluation of the cala-mine effect is needed. Methods such as soft sensing andadaptive support vector machine are used for a global eval-uation of calamine intensity on the monitored area of thebillet in continuous casting. This kind of approaches isapplied in continuous casting process for constructing acomplementary condition monitoring system, which allowsan online calamine evaluation. Simulation results, based onthe measured surface temperature and the adaptive supportvector machine analysis, showed that this new combinedapproach is easily implementable and gives good resultswhen applied online. Voir les détails
Mots clés : Continuous casting . Surface billet casting . Infrared temperature measurement . Adaptive support vector machine (ASVM) . Process and quality evaluation
Inferential sensor – Based adaptive principal componentsanalysis for mechanical properties prediction and evaluation
This paper is concerned with a method for on-line quality prediction and evaluation ofmechanical properties in metal testing. This method uses an Adaptive Principal ComponentAnalysis (APCA) as a multi predictor of different sub-models defining the mechanical prop-erties such as constraints limits and elongation. The PCA technique, characterized by itsmultivariate component, is strongly recommended to model a multi-input-multi-outputsystem. The complex system is generally known as a non-linear and unsteady state pro-cess. The PCA method is a linear projection. To adapt it and to improve the prediction accu-racy, a variant of this method is considered based on iteratively using a specific algorithm.This kind of approaches is applied for constructing an inferential model, which allows areliable and accurate predictor. Simulation results, based on the measured and computeddata using the above-cited method, show that the proposed approach is easily implement-able and give an accurate prediction. Voir les détails
Mots clés : Soft sensor, Mechanical testing, Adaptive principal component analysis, Uncertainties evaluation
Task Performance Evaluation for Supervisory Control Systems
Integrated multi-modal Supervisory Control Systems (ISCS) are a new generation ofcomplex and synergistic Human-Machine Interaction Systems (HMIS). This paper deals withmulti-modal interaction and control applied to Human Robot Systems (HRS). A task performanceevaluation technique dedicated for multi-modal interaction and control is proposed. It enablescomparison of task performance carried out by using different selection of control modes or bydifferent operators. Objective and subjective performance measures are defined.Based on the Analytical Hierarchy Process method (AHP) which takes into account qualitative andquantitative attributes and criteria, a task performance evaluation technique has been proposed forsupervisory systems which enables multimodal interaction modes .Experimental results have been carried out and some preliminary results will be presentedconcerning parallel cable-based manipulators. Voir les détails
Mots clés : Task performance evaluation, human factors, Analytical Hierarchy Process, Adaptive supervisory control, Multi-modal interaction, Cable-based robots, Graphical-user interface.