Title :
Input-output systems robust nonlinear fault diagnosis
Author :
Vemuri, Arun T. ; Polycarpou, Marios M.
Author_Institution :
Dept. of Engine & Vehicle Res., Southwest Res. Inst., San Antonio, TX, USA
Abstract :
Describes a fault diagnosis algorithm for a class of nonlinear dynamic systems with modeling uncertainties when not all states of the system are measurable. The main idea behind this approach is to monitor the plant for any off-nominal system behavior due to faults utilizing a nonlinear online approximator with adjustable parameters. A nonlinear estimation model and learning algorithm are described so that the online approximator provides an estimate of the fault. The robustness, sensitivity, stability and performance properties of the nonlinear fault diagnosis scheme are rigorously established under certain assumptions on the failure type
Keywords :
adaptive control; approximation theory; fault diagnosis; learning (artificial intelligence); nonlinear dynamical systems; parameter estimation; uncertain systems; input-output systems; learning algorithm; modeling uncertainties; nonlinear dynamic systems; nonlinear estimation model; nonlinear online approximator; off-nominal system behavior; performance properties; robust nonlinear fault diagnosis; sensitivity; stability; Condition monitoring; Ear; Engines; Fault detection; Fault diagnosis; Nonlinear dynamical systems; Redundancy; Robust stability; Robustness; Uncertainty;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.609662
