Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2007.900623
Title: Automated fault detection and diagnosis in mechanical systems
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Keywords: Fault detection
Fault diagnosis
Neural nets
Neural networks (NNs)
Nonlinear model
Nonlinear systems
Issue Date: Nov-2007
Source: Huang, S.N.,Tan, K.K.,Lee, T.H. (2007-11). Automated fault detection and diagnosis in mechanical systems. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 37 (6) : 1360-1364. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2007.900623
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. © 2007 IEEE.
Source Title: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
URI: http://scholarbank.nus.edu.sg/handle/10635/55166
ISSN: 10946977
DOI: 10.1109/TSMCC.2007.900623
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