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research-article

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

[+] Author and Article Information
Felix Figaschewsky

Chair of Structural Mechanics and Vehicle Vibration Technology Brandenburg University of Technology Cottbus-Senftenberg D-03046 Cottbus, Germany
felix.figaschewsky@b-tu.de

Benjamin Hanschke

Chair of Structural Mechanics and Vehicle Vibration Technology Brandenburg University of Technology Cottbus-Senftenberg D-03046 Cottbus, Germany
benjamin.hanschke@b-tu.de

Arnold Kühhorn

Chair of Structural Mechanics and Vehicle Vibration Technology Brandenburg University of Technology Cottbus-Senftenberg D-03046 Cottbus, Germany
kuehhorn@b-tu.de

1Corresponding author.

ASME doi:10.1115/1.4040748 History: Received June 25, 2018; Revised June 28, 2018

Abstract

The assessment of vibration levels of rotating blades in turbomachinery is a fundamental task. Traditionally, this assessment is done by the application of strain gauges to some blades of the assembly. In contrast to strain gauges, 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 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 are captured by the BTT simulation environment. The main advantage of the created data is the possibility to steadily increase the signal complexity. 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.

Copyright (c) 2018 by ASME
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