Title :
Automated fault detection in nonlinear systems using an OLA method combined with TAF-MFNN
Author :
Zhou, Jing ; Huang, Xinhan ; Liu, Jing ; Wang, Min
Author_Institution :
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan
Abstract :
This paper presents a robust fault detection (FD) scheme for detecting and approximating state faults occurring in a class of nonlinear dynamical systems. In the presence of a failure, the values exported by the on-line approximator (OLA), are used as an estimate of the real nonlinear fault function. The general inspiration for constructing OLA model in FD is based on the radial basis function (RBF) neural network technology. Here we adopt a novel tunable activation function multi-layer forward neural network (TAF-MFNN) to construct the OLA due to its strong learning capability is proposed in this paper, and a systematic procedure for constructing nonlinear estimation algorithms is developed. Eventually, the simulation studies are used to illustrate the results.
Keywords :
fault diagnosis; nonlinear dynamical systems; radial basis function networks; OLA method; RBF neural network technology; TAF-MFNN; automated fault detection; multilayer forward neural network; nonlinear dynamical systems; nonlinear fault function; online approximator; radial basis function; robust fault detection; Analytical models; Fault detection; Function approximation; Multi-layer neural network; Neural networks; Neurons; Noise robustness; Nonlinear systems; State estimation; Uncertainty; TAF-MFNN; adaptive learning scheme; fault detection; nonlinear estimator; on-line approximator;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593088
