Vehicle health monitoring system using multiple-model adaptive estimation

dc.contributor.advisor Syrmos, Vassilis L
dc.contributor.author Wang, Xudong
dc.contributor.department Electrical Engineering
dc.date.accessioned 2009-03-06T19:38:42Z
dc.date.available 2009-03-06T19:38:42Z
dc.date.graduated 2003-12
dc.date.issued 2003-12
dc.description vii, 59 leaves
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.
dc.identifier.uri http://hdl.handle.net/10125/7051
dc.publisher University of Hawaii at Manoa
dc.relation Theses for the degree of Master of Science (University of Hawaii at Manoa). Electrical Engineering; no. 3848
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.
dc.rights.uri https://scholarspace.manoa.hawaii.edu/handle/10125/2047
dc.title Vehicle health monitoring system using multiple-model adaptive estimation
dc.type Thesis
dc.type.dcmi Text
local.identifier.callnumber Q111 .H3 no. 3848
local.thesis.degreelevel MS
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