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


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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