Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2009.2015078
Title: Fault detection and diagnosis based on modeling and estimation methods
Authors: Huang, S. 
Tan, K.K. 
Keywords: Fault detection
Neural networks (NNs)
Nonlinear model
Issue Date: 2009
Citation: Huang, S., Tan, K.K. (2009). Fault detection and diagnosis based on modeling and estimation methods. IEEE Transactions on Neural Networks 20 (5) : 872-881. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2009.2015078
Abstract: This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme. © 2009 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/56022
ISSN: 10459227
DOI: 10.1109/TNN.2009.2015078
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

39
checked on Oct 11, 2018

WEB OF SCIENCETM
Citations

37
checked on Oct 2, 2018

Page view(s)

22
checked on Aug 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.