@article {3077,
	title = {Temporal Neuro-Fuzzy systems in fault diagnosis and prognosis, February ISSN/ISBN 1974-9821/1974-983X},
	journal = {IREMOS International Review on Modelling and Simulations. },
	volume = {vol.4},
	number = { N{\textdegree}1},
	year = {2011},
	abstract = {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 ".   },
	url = {http://www.msc-les.org/proceedings/emss/2011/EMSS2011_11.pdf},
	author = {Mahdaoui Rafik and Mouss Med Djamel and Chouhal Ouahiba}
}
