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Modelling of turbine blade vibrations via computational intelligence methods
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Heat and Power Technology.
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2017 (English)In: 12th European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2017, KTH Royal Institute of Technology, 2017Conference paper (Refereed)
Abstract [en]

A method for modelling turbomachine blade vibration events is proposed, based on computational intelligence algorithms. The method utilises steady thermodynamic data and blade tip-timing data to identify high amplitude vibration events and to draw underlying relationships between steady-thermodynamic input channels and resultant blade motion characteristics. Several computational studies probe specific process aspects in order to improve model prediction accuracy and several methods of data-feature reduction are established to further enhance vibration predictions. Overall, the study shows promise of what prediction capabilities can be achieved with seemingly limited instrumentation. Drawbacks in matters of tip-timing interpretation, quality/quantity of data and process limitations are discussed. Consequential future objectives are outlined to envisage onward predictive accuracy.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017.
Keyword [en]
Blade tip timing, Blade vibration prediction, Computational intelligence, Health monitoring
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-211881Scopus ID: 2-s2.0-85021839560OAI: oai:DiVA.org:kth-211881DiVA: diva2:1131645
Conference
12th European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2017, Quality Hotel GlobeStockholm, Sweden, 3 April 2017 through 7 April 2017
Note

QC 20170815

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2017-08-15Bibliographically approved

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