This work proposes a method for input and output sensor fault diagnosis of an industrial processes using identified fuzzy models. In particular, the presented technique concerns the identification of a piecewise affine fuzzy system based on Takagi-Sugeno models. The process under investigation may, in fact, be represented as a composition of several Takagi-Sugeno models selected according to the process operating conditions. This work also addresses a method for the identification of the local Takagi-Sugeno models from a sequence of noisy measurements acquired from the real process. The fault detection scheme adopted to generate residuals uses the Takagi-Sugeno fuzzy model. The developed technique is finally applied for the fault diagnosis of input and output sensors of a real sugar cane crushing mill.
Fuzzy Model Identification for the Fault Diagnosis of a Real Sugar Cane Crushing Process
SIMANI, Silvio
2004
Abstract
This work proposes a method for input and output sensor fault diagnosis of an industrial processes using identified fuzzy models. In particular, the presented technique concerns the identification of a piecewise affine fuzzy system based on Takagi-Sugeno models. The process under investigation may, in fact, be represented as a composition of several Takagi-Sugeno models selected according to the process operating conditions. This work also addresses a method for the identification of the local Takagi-Sugeno models from a sequence of noisy measurements acquired from the real process. The fault detection scheme adopted to generate residuals uses the Takagi-Sugeno fuzzy model. The developed technique is finally applied for the fault diagnosis of input and output sensors of a real sugar cane crushing mill.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
