TITLE:
A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis
AUTHORS:
Rafik Mahdaoui, Leila Hayet Mouss
KEYWORDS:
TSK Neuro-Fuzzy Systems; Faults Diagnosis; Fault Prognosis
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.5 No.7,
June
15,
2012
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.