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A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

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DOI: 10.4236/jsea.2012.57055    4,070 Downloads   6,212 Views   Citations

ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Mahdaoui and L. Mouss, "A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis," Journal of Software Engineering and Applications, Vol. 5 No. 7, 2012, pp. 477-482. doi: 10.4236/jsea.2012.57055.

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