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| dc.creator | Gabel, S. | |
| dc.date.accessioned | 2010-06-23T15:10:04Z | |
| dc.date.available | 2010-06-23T15:10:04Z | |
| dc.date.issued | 2003-05 | |
| dc.identifier.other | ESL-IE-03-05-25 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/91032 | |
| dc.description.abstract | A neural network approach is employed for estimating key efficiency parameters in a gas turbine engine. The concept is demonstrated within a limited operating region for a given engine. The neural network is developed to estimate certain unmeasurable parameters in a first-principles mathematical model of the engine. The network is trained using data derived from measured data taken on an auxiliary power unit (APU) engine (from an aircraft application). A discussion of the neural network development and its application to on-line fault detection in an industrial gas turbine engine is also presented. The technique could be used for condition-based maintenance or to monitor the energy efficiency of an industrial gas turbine. | en |
| dc.language.iso | en_US | |
| dc.publisher | Energy Systems Laboratory (http://esl.tamu.edu) | |
| dc.subject | Neural Networks | en |
| dc.title | Using Neural Networks | en |
| dc.type | Presentation | en |
This item appears in the following Collection(s)
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IETC - Industrial Energy Technology Conference
Industrial Energy Technology Conference