Please use this identifier to cite or link to this item:
|Title:||Automated fault detection and diagnosis in mechanical systems||Authors:||Huang, S.N.
Neural networks (NNs)
|Issue Date:||Nov-2007||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 7, 2020
WEB OF SCIENCETM
checked on Mar 30, 2020
checked on Mar 31, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.