The long use of a system causes its degradation. Hence, the maintenance activity is required in order to keep and improve the efficiency in the system. With the rapid development in networking technology, a need appears to change the manufacturing strategies. These new technologies improve the maintenance process, and establish remote maintenance (tele-maintenance, e-maintenance and m-maintenance). These kinds of maintenance try to provide personnel maintenance with the right information at the suitable time, which makes information available, anywhere and anytime. Our proposition is the use of mobile agent technology to reduce the maintenance costs and solve the problem of the unavailability of an expert in all phases of condition-based maintenance (CBM) strategy. The mobile agent technology overcomes a lot of problems and there is not much work that has used this technology. We have also used the web services (WS) to insure interoperability between machines and to support interaction over the network. Our approach gives great support to the maintenance engineer as it facilitates the access to decision-making support, work order, etc. which are available in the device like smartphone. This paper presents the development of a mobile maintenance support system based on mobile agent technology. The proposed system, the web and agent technology as well as remote communication were tested successfully.

The long use of a system causes its degradation. Hence, the maintenance activity is required in order to keep and improve the efficiency in the system. With the rapid development in networking technology, a need appears to change the manufacturing strategies. These new technologies improve the maintenance process, and establish remote maintenance (tele-maintenance, e-maintenance and m-maintenance). These kinds of maintenance try to provide personnel maintenance with the right information at the suitable time, which makes information available, anywhere and anytime. Our proposition is the use of mobile agent technology to reduce the maintenance costs and solve the problem of the unavailability of an expert in all phases of condition-based maintenance (CBM) strategy. The mobile agent technology overcomes a lot of problems and there is not much work that has used this technology. We have also used the web services (WS) to insure interoperability between machines and to support interaction over the network. Our approach gives great support to the maintenance engineer as it facilitates the access to decision-making support, work order, etc. which are available in the device like smartphone. This paper presents the development of a mobile maintenance support system based on mobile agent technology. The proposed system, the web and agent technology as well as remote communication were tested successfully.

In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences.

This study propose a novel method for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling. The proposed approach is inspired by the behavior of the human body. We have implemented a problem-solving approach for optimizing the processing time, methods based on Métaheuristiques. This hybridization is between a Multi agent system and inspirations of the human body, especially artificial immune system. The effectiveness of our approach has been demonstrated repeatedly in this study. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

In this paper, we proposed a new mathematical model of a faulty photovoltaic generator operation. It presents its behavior, when it's subjected to the open-circuit and the short-circuit faults at its basic components as: cells, bypass diodes and blocking diodes. Such kind of modeling will allow developing fault detection and diagnosis methods. Indeed, the proposed model will be used to set normal and fault operation conditions database, which will facilitate learning and classifications phases.

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.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

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 network 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.

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.

In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.

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 properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) 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.