TSOUTSANIS, Elias and MESKIN, Nader (2016). Forecasting the health of gas turbine components through an integrated performance-based approach. In: RAMUHALLI, Pradeep and LOWE, Ryan, (eds.) 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). Institute of Electrical and Electronics Engineers Inc..
Full text not available from this repository.Abstract
In this study, we present an integrated method for detecting and forecasting the health of gas turbine components as degraded over time. An advanced model-based real time performance adaptation approach is developed for detecting the degradation of engine components via a dynamic engine model that is built in Simulink. The detected health parameters of the engine component are then implemented in a discrete window-based analysis by a regression method in order to forecast their evolution. The proposed approach is tested for an engine with increased flexibility that characterizes modern gas turbine operations. The results demonstrate the promising capabilities of our advanced proposed method for accurate and efficient detection and forecast of the health of gas turbine compressors as degraded over time. © 2016 IEEE.
Item Type: | Book Section |
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Additional Information: | Paper originally presented at the IEEE Conference on Prognostics and Health Management, PHM 2016, 20-22 June 2016, Ottawa, ON, Canada. Cited By 1. Conference Code:123316. INSPEC Accession Number: 16230023 |
Uncontrolled Keywords: | Compressibility of gases; Engines; Forecasting; Gas compressors; Gas turbines; Health; Regression analysis; Systems engineering, Advanced modeling; Efficient detection; Engine components; Gas turbine compressors; Health parameters; Increased flexibility; Performance based approach; Real time performance, Turbine components |
Departments - Does NOT include content added after October 2018: | Faculty of Science, Technology and Arts > Department of Engineering and Mathematics |
Identification Number: | https://doi.org/10.1109/ICPHM.2016.7542829 |
Depositing User: | Elias Tsoutsanis |
Date Deposited: | 17 Jan 2018 14:56 |
Last Modified: | 18 Mar 2021 16:31 |
URI: | https://shura.shu.ac.uk/id/eprint/16177 |
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