Title :
A New FDD Algorithm of a Class of Nonlinear Non-Gaussian Stochastic Systems
Author :
Zhou, Jinglin ; Wang, Hong ; Zhou, Donghua
Author_Institution :
Tsinghua Univ., Beijing
fDate :
May 30 2007-June 1 2007
Abstract :
A new fault detection and diagnosis (FDD) algorithm for general nonlinear stochastic systems is proposed by using the optimal probability density function (PDF) tracking filtering. The fault is detected through a determinate threshold rather than an experiential threshold. Moreover, an adaptive fault diagnosis method is also provided to estimate the size of the fault. Specially, to give facilities for practical application, a time-varying threshold (TVT), which can be determined beforehand, is presented. Simulations are included to show the effectiveness of the proposed algorithm under the missing measurements and encouraging results have been obtained via comparison to existing detection algorithms.
Keywords :
Gaussian processes; fault diagnosis; nonlinear control systems; probability; stochastic systems; tracking filters; fault detection-diagnosis algorithm; nonlinear nonGaussian stochastic systems; optimal probability density function tracking filtering; time-varying threshold; Analytical models; Automatic control; Automation; Fault detection; Fault diagnosis; Filtering algorithms; Nonlinear control systems; Optimal control; Power system modeling; Stochastic systems;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0817-7
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376333
