Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/29466
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kadirkamanathan, Visakan | - |
dc.contributor.author | Li, P. | - |
dc.contributor.author | Jaward, M.H. | - |
dc.contributor.author | Fabri, Simon G. | - |
dc.date.accessioned | 2018-04-23T09:54:23Z | - |
dc.date.available | 2018-04-23T09:54:23Z | - |
dc.date.issued | 2002 | - |
dc.identifier.citation | Kadirkamanathan, V., Li, P., Jaward, M. H., & Fabri, S. G. (2002). Particle filtering-based fault detection in non-linear stochastic systems. International Journal of Systems Science, 33(4), 259-265. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/29466 | - |
dc.description.abstract | Much of the development in model-based fault detection techniques for dynamic stochastic systems has relied on the system model being linear and the noise and disturbances being Gaussian. Linearized approximations have been used in the non-linear systems case. However, linearization techniques, being approximate, tend to suer from poor detection or high false alarm rates. A novel particle filtering based approach to fault detection in non-linear stochastic systems is developed here. One of the appealing advantages of the new approach is that the complete probability distribution information of the state estimates from particle fillter is utilized for fault detection, whereas, only the mean and covariance of an approximate Gaussian distribution are used in a coventional extended Kalman filter-based approach. Another advantage of the new approach is its applicability to general non-linear system with non-Gaussian noise and disturbances. The eectiveness of this new method is demonstrated through Monte Carlo simulations and the detection performanc e is compared with that using the extended Kalman filter on a non-linear system. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Taylor & Francis | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Monte Carlo method | en_GB |
dc.subject | Nonlinear control theory | en_GB |
dc.subject | Stochastic control theory | en_GB |
dc.subject | Kalman filtering | en_GB |
dc.subject | Approximation theory | en_GB |
dc.title | Particle filtering-based fault detection in non-linear stochastic systems | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1080/00207720110102566 | - |
dc.publication.title | International Journal of Systems Science | en_GB |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Particle_filtering_based_fault_detection_in_non_linear_stochastic_systems.pdf Restricted Access | 235.47 kB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.