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dc.contributor.advisor Syrmos, Vassilis L en_US
dc.contributor.author Wang, Xudong en_US
dc.date.accessioned 2009-03-06T19:38:42Z en_US
dc.date.available 2009-03-06T19:38:42Z en_US
dc.date.issued 2003-12 en_US
dc.identifier.uri http://hdl.handle.net/10125/7051 en_US
dc.description vii, 59 leaves en_US
dc.description.abstract In this thesis, we propose two failure detection and identification (FDI) approaches based on the multiple-model estimation algorithm to monitor the health of vehicles, specifically aircraft applications. They detect and identify failing components of the vehicle, and the system variations. The dynamics of the vehicle are modeled as a stochastic hybrid system with uncertainty-unknown model structure or parameters. FDI performance is evaluated for each approach. We demonstrate the reliability, validity of these approaches by applying them to simulate aircraft machinery experiencing component failures or structural variations. The approaches that we surveyed are: (i) Multiple-Hypothesis Kalman Filter, and (ii) Interacting Multiple-Model (IMM) Estimator. By coupling the fault detection and identification (FDI) scheme with the reconfigurable controller design scheme, a fault-tolerant control system based on the multiple-model estimation algorithm is defined. en_US
dc.publisher University of Hawaii at Manoa en_US
dc.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner. en_US
dc.rights.uri https://scholarspace.manoa.hawaii.edu/handle/10125/2047 en_US
dc.title Vehicle health monitoring system using multiple-model adaptive estimation en_US
dc.type Thesis en_US
dc.type.dcmi Text en_US
dc.contributor.department Electrical Engineering en_US
dc.description.degree MS en_US
dc.date.graduated 2003-12 en_US
local.identifier.callnumber Q111 .H3 no. 3848 en_US

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