<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahdaoui Rafik</style></author><author><style face="normal" font="default" size="100%">Mouss Leila Hayet</style></author><author><style face="normal" font="default" size="100%">Chouhal Ouahiba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Temporal Neuro-Fuzzy systems in fault diagnosis and prognosis, February, ISSN/ISBN 1974-9821/1974-983X</style></title><secondary-title><style face="normal" font="default" size="100%">IREMOS International Review on Modelling and Simulations.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.msc-les.org/proceedings/emss/2011/EMSS2011_11.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>