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

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]

In the present work, a comprehensive drain current model including the interfacial hot-carrier degradation effects for undoped symmetric gate stack double gate (GSDG) MOSFET and the expressions of transconductance and drain conductance have been obtained. Exploiting this new device model, we have found that the incorporation of a high-k layer between oxide region and gate metal leads to drain current enhancement, improved output conductance, increased transconductance parameter and enhanced interfacial hot-carrier immunity. The proposed model has been validated by comparing the analytical I-V characteristics with 2-D numerical results. The obtained results may provide a theoretical basis and physical insights for multigate MOSFET-based circuits design including the hot-carrier degradation effects. (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an manufacturing systems. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker , cement factory of Ain Touta " Batna, Algeria ".

This work appears in man-machine interface. Our goal is to study the integration of Arabic language in Mobile Phone, in order to achieve a man-machine interface Arabic. The correct display of Arabic character is essential in a MMI. Since Arabic characters change their forms according to their position in a word, then it is necessary to make a contextual analysis on every word, to find the correct form of each character. The transformation of two or more characters in one form, demand special treatment, as in the case of Arabic ligature LAM-ALEF. The Arabic language has a different direction of writing in relation to other languages embedded in mobile phone, which requires finding an algorithm that provides a bidirectional display of SMS messages. These messages may contain characters from different direction, from right to left, left to right or characters that have no direction. It allows you to make the message understandable.

Dans cet article, nous présentons un nouvel
algorithme pour réduire la dimension de vecteur d’état de
fonctionnement d’un système industriel. Notre algorithme
permet de sélectionner un sous-ensemble de paramètres qui
offre une détection plus rapide de dysfonctionnement et une
bonne qualité de classification. Cet algorithme est basé sur le
comportement observé chez les fourmis réelles. Nous montrons
ici que l’émergence des déplacements et les interactions des
fourmis permet de trouver un ensemble réduit de paramètres
qui caractérisent le fonctionnement d’un système industriel
dynamique et complexe. L’algorithme offre aussi la possibilité
d’utiliser des bases de données de grandes tailles. Les
expériences effectuées sur les bases de données Iris et Vehicle
montrent que notre algorithme fournit de très bons résultats.
MOTS-CLES: Colonie de fourmis, Classification, Diagnostic
industriel, Sélection de paramètres, Système complexe et dynamique.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria

This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. 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 a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.