Please use this identifier to cite or link to this item:
|Title:||Fault detection and diagnosis based on modeling and estimation methods|
|Authors:||Huang, S. |
Neural networks (NNs)
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 7, 2017
WEB OF SCIENCETM
checked on Nov 29, 2017
checked on Dec 11, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.