Title :
Automated Fault Detection and Diagnosis in Mechanical Systems
Author :
Huang, S.N. ; Tan, K.K. ; Lee, T.H.
Author_Institution :
Nat. Univ. of Singapore, Singapore
Abstract :
In this work, a fault detection method is developed based on a neural network (NN) learning model. The robust observer is designed for monitoring fault, without NN learning, when the system of concern is operating in the normal healthy mode. By comparing appropriate states with their signatures, the fault diagnosis can be carried out and the NN learning is then triggered to identify the fault function.
Keywords :
control engineering computing; fault diagnosis; learning (artificial intelligence); monitoring; observers; robots; NN learning model; automated fault detection; automated fault diagnosis; fault monitoring; mechanical systems; neural network; robotic systems; robust observer design; Expert systems; Fault detection; Fault diagnosis; Mechanical systems; Neural networks; Observers; Robots; Robustness; State estimation; Symmetric matrices; Fault detection; neural networks (NNs); nonlinear model;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2007.900623
