The use of optimal estimation techniques in the analysis of gas turbines

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dc.contributor.advisor Singh, R.
dc.contributor.author Provost, M. J.
dc.date.accessioned 2008-07-09T08:01:04Z
dc.date.available 2008-07-09T08:01:04Z
dc.date.issued 1994-12
dc.identifier.uri http://hdl.handle.net/1826/2703
dc.description.abstract This thesis discusses several methods that can be used to analyse gas turbines, based on an optimal estimation algorithm called the Kalman Filter. These techniques overcome the difficulties of more 'traditional' analysis methods, which can give misleading results because they do not explicitly consider the possibility of measurement error. An enhancement to the Kalman Filter (the 'Concentrator') is presented, which overcomes the Kalman Filter's tendency to 'smear' the effects of genuine changes in a small number of component changes and/or sensor biasses over the whole set of changes and biasses being considered. To complement this, methods of optimising some of the statistical inputs to the Kalman Filter in order to improve the ability of the 'Concentrator' to carry out the required analysis are discussed. These are based on analytical methods developed to determine the sensitivity of the Kalman Filter to its inputs. Techniques are also presented for determining the gas-path measurements in a gas turbine that are needed to enable the required analysis of component changes and/or sensor biasses to be performed, including determination of both possible measurement redundancy and the ability of a set of measurements to successfully differentiate between all the component changes and sensor biasses being sought. A recursive algorithm for time series analysis (the Smoothing/Trending Algorithm) is also presented. This produces, for each point in a time series, best estimates of the underlying levels and trends (rates of change of level) of the process generating the observations. A method of combining the 'Concentrator' and the Smoothing/Trending Algorithm is also presented, which reduces the effects of sensor noise on the analysis of component changes and sensor biasses from time series data. Many types of prime movers and process plant could be effectively analysed using the methods described in this thesis. en_UK
dc.language.iso en en_UK
dc.publisher Cranfield University en_UK
dc.rights © Cranfield University 1994. All rights reserved. No part of this publication may be reproduced without written permission of the copyright holder. en_UK
dc.title The use of optimal estimation techniques in the analysis of gas turbines en_UK
dc.type Thesis or dissertation en_UK
dc.type.qualificationlevel Doctoral en_UK
dc.type.qualificationname PhD en_UK


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