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Efficient Generation of Engine Representative Tip Timing Data Based on a Reduced Order Model for Bladed Rotors

  • In modern compressors the assessment of blade vibration levels as well as health monitoring of the components are fundamental tasks. Traditionally, this assessment is done by the application of strain gauges to some blades of the assembly. In contrast to strain gauges, blade tip timing (BTT) offers a contactless monitoring of all blades of a rotor and there is no need of a telemetry system. A major issue in the interpretation of BTT data is the heavily undersampled nature of the signal. Usually, newly developed BTT algorithms are tested with sample data created by simplified structural models neglecting many of the uncertainties and disturbing influences of real applications. This work focuses on the creation of simulated BTT datasets as close as possible to real case measurements. For this purpose a subset of nominal system modes (SNM) representation of a compressor rotor is utilized. This model is able to include a large number of features present in real measurements, such as mistuning, static blade deflections due to centrifugalIn modern compressors the assessment of blade vibration levels as well as health monitoring of the components are fundamental tasks. Traditionally, this assessment is done by the application of strain gauges to some blades of the assembly. In contrast to strain gauges, blade tip timing (BTT) offers a contactless monitoring of all blades of a rotor and there is no need of a telemetry system. A major issue in the interpretation of BTT data is the heavily undersampled nature of the signal. Usually, newly developed BTT algorithms are tested with sample data created by simplified structural models neglecting many of the uncertainties and disturbing influences of real applications. This work focuses on the creation of simulated BTT datasets as close as possible to real case measurements. For this purpose a subset of nominal system modes (SNM) representation of a compressor rotor is utilized. This model is able to include a large number of features present in real measurements, such as mistuning, static blade deflections due to centrifugal loads, aerodynamic damping and multiple mode resonances. Additionally, manufacturing deviations of the blade geometry, probe positioning errors in the BTT system and noise in the time of arrivals (TOAs) are captured by the BTT simulation environment. The main advantage of the created data is the possibility to steadily increase the signal complexity. Starting with a “perfect” signal the simulation environment is able to add different uncertainties one after the other. This allows the assessment of the influence of different features occurring in real measurements on the performance and accuracy of the analysis algorithms. Finally, a comparison of simulated BTT data and real data acquired from a rig test is shown to validate the presented approach of BTT data generation. Copyright © 2018 by Rolls-Royce Deutschland Ltd & Co KGshow moreshow less

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Metadaten
Author: Felix Figaschewsky, Benjamin Hanschke, Arnold Kühhorn
DOI:https://doi.org/10.1115/GT2018-76342
ISBN:978-0-7918-5115-9
Title of the source (English):ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, Volume 7C: Structures and Dynamics, Oslo, Norway, June 11–15, 2018
Publisher:ASME
Place of publication:New York, NY
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2018
Tag:Engines; Rotors
Number of pages:12
Article number:GT2018-76342
Faculty/Chair:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Strukturmechanik und Fahrzeugschwingungen
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