A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis
- 1 Laboratoire d’Automatique et Productique (LAP), Université de Batna, Batna, Algérie
- 2 Centre Universitaire Khenchela, Khen- chela, Algérie
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.
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