In this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator's characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator's defaults.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent

his paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area.
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In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example

Dans cet article, nous présentons un nouvel algorithme pour la classification de différents états de fonctionnement et de dysfonctionnement d'un système industriel. Notre algorithme offre une bonne qualité de classification de données. Cet algorithme est basé sur Les Séparateurs à Vastes Marges (SVM). Il permet de classer des données non linéairement séparables. L'algorithme offre aussi la possibilité d'utiliser des bases de données de grandes tailles. Les expériences effectuées sur les bases de données issues d'un système de pasteurisation montrent que notre algorithme fournit de très bons résultats.

Dans cet article, nous présentons un nouvel algorithme pour la classification de différents états de fonctionnement et de dysfonctionnement d'un système industriel. Notre algorithme offre une bonne qualité de classification de données. Cet algorithme est basé sur Les Séparateurs à Vastes Marges (SVM). Il permet de classer des données non linéairement séparables. L'algorithme offre aussi la possibilité d'utiliser des bases de données de grandes tailles. Les expériences effectuées sur les bases de données issues d'un système de pasteurisation montrent que notre algorithme fournit de très bons résultats.

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.

Fault diagnosis and maintenance are considered as vital aspects in industrial process, therefore, fault diagnosis systems should support decision-making tools, new diagnosis approaches and technique. In this paper, we discuss the cooperation of agents and Web Services in order to create highly flexible and dynamic framework for manufacturing systems fault diagnosis based on three-tier architectures that can support a distributed application. We depict also how Web Services can be located and the provided services can be invoked.

Dans cet article, nous présentons un nouvel algorithme pour la classification de différents états de fonctionnement et de dysfonctionnement d'un système industriel. Notre algorithme offre une bonne qualité de classification de données. Cet algorithme est basé sur Les Séparateurs à Vastes Marges (SVM). Il permet de classer des données non linéairement séparables. L'algorithme offre aussi la possibilité d'utiliser des bases de données de grandes tailles. Les expériences effectuées sur les bases de données issues d'un système de pasteurisation montrent que notre algorithme fournit de très bons résultats.

In this paper, our aim is to present existing methods and techniques related to the artificial vision which is an active subject of research. In this work, we elaborate a recognition system of objects in an image for a sorting application. The corner stone of our work is based on the recognition and sorting of objects (geometrical shapes) in an image in artificial vision. The application of these images is achieved using a fixed camera having a regular and permanent field of vision with a consequent angle on a conveyer which holds the objects to be observed. The Objects' recognition process requires knowledge of our camera characteristics. In order to calibrate this camera, we propose to use a simple Web camera configured to get a photo of the conveyer's centre. The objective is to seek for objects in the images, which are considered as unitary and not sequences of images. In this case, the development of automatic methods is required to ensure the sorting rapidity but with additional complex processing to make it efficient. The analysis determines simultaneously both the recensement and arithmetic counting, then the detection of faults based mainly on the comparison of the object shape with the shape defined previously and using this operation the sorting is made. A complementary system equipped with sensors and programmable automata is used to eject each object into corresponding panel.

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural net-work architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln

In this work, we intend to determine a scheduling rule of maintenance work, which permits the optimization of the random tasks operation (corrective maintenance) while maintaining the works (preventive maintenance) yet scheduled within their temporal intervals. The principle of our approach consists of processing the problem in two stages: Resolution of the static problem for the projected scheduling of the tasks known a priori Resolution of the dynamic problem for random tasks scheduling This work has found a field of application within the maintenance company of the east cements factories, (SMCE) which is in charge of the five (05) east cements companies' maintenance.

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

The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems to control the difference shown in the control system using fuzzy regulators for the operation of the grinding without unnecessary stops, it also helps the operator to know the maintenance task to perform. In addition, regulators decentralization allows the availability of fuzzy control, even if one of the regulators is absent, it does not prevent the other to complete its task to control fineness, mill�s temperature and feed.

In this paper, our aim is to present existing methods and techniques related to the artificial vision which is an active subject of research. In this work, we elaborate a recognition system of objects in an image for a sorting application. The corner stone of our work is based on the recognition and sorting of objects (geometrical shapes) in an image in artificial vision. The application of these images is achieved using a fixed camera having a regular and permanent field of vision with a consequent angle on a conveyer which holds the objects to be observed. The Objects' recognition process requires knowledge of our camera characteristics. In order to calibrate this camera, we propose to use a simple Web camera configured to get a photo of the conveyer's centre. The objective is to seek for objects in the images, which are considered as unitary and not sequences of images. In this case, the development of automatic methods is required to ensure the sorting rapidity but with additional complex processing to make it efficient. The analysis determines simultaneously both the recensement and arithmetic counting, then the detection of faults based mainly on the comparison of the object shape with the shape defined previously and using this operation the sorting is made. A complementary system equipped with sensors and programmable automata is used to eject each object into corresponding panel

The continuing evolution of technology and human behavior puts the company in an uncertain and evolving environment. The company must be responsive and even proactive; therefore, control performance becomes increasingly difficult. Choosing the best method of ensuring control by the management policy of the company and its strategy is also a decision problem. The aim of this paper is the comparative study of three methods: the Balanced Scorecard, GIMSI and SKANDIAs NAVIGATOR for choosing the best method for ensuring the orderly following the policy of the company while maintaining its durability. Our work is divided into three parts. We firstly proposed original structural and kinetic metamodels for the three methods that allow an overall view of a method. Secondly, based on the three metamodels, we have drawn a generic comparison to analyze completeness of the method. Thirdly, we performed a restrictive comparison based on a restrictive set of criteria related to the same aspect example organizational learning, which is one of the bricks of knowledge management for a reconciliation to a proactive organization in an environment disturbed and uncertain, and the urgent needs. We note that we applied the three methods are applied in our precedent works. [1][23]