Vehicle health monitoring system using multiple-model adaptive estimation

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dc.contributor.advisorSyrmos, Vassilis L
dc.contributor.authorWang, Xudong
dc.contributor.departmentElectrical Engineering
dc.date.accessioned2009-03-06T19:38:42Z
dc.date.available2009-03-06T19:38:42Z
dc.date.graduated2003-12
dc.date.issued2003-12
dc.descriptionvii, 59 leaves
dc.description.abstractIn 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.urihttp://hdl.handle.net/10125/7051
dc.publisherUniversity of Hawaii at Manoa
dc.relationTheses for the degree of Master of Science (University of Hawaii at Manoa). Electrical Engineering; no. 3848
dc.rightsAll 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.urihttps://scholarspace.manoa.hawaii.edu/handle/10125/2047
dc.titleVehicle health monitoring system using multiple-model adaptive estimation
dc.typeThesis
dc.type.dcmiText
local.identifier.callnumberQ111 .H3 no. 3848
local.thesis.degreelevelMS

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