A Unified Approach for the Quantitative Comparison of Strain Gauge and Blade Tip-Timing Measurement Results for Blade Vibration in Turbomachinery

涡轮机械 刀(考古) 振动 应变计 结构工程 声学 机械工程 工程类 物理
作者
Markus Schafferus,Marios Sasakaros,Manfred Wirsum
出处
期刊:Journal of engineering for gas turbines and power [ASM International]
卷期号:148 (4)
标识
DOI:10.1115/1.4069622
摘要

Abstract Accurate measurement of blade vibrations in turbomachinery is crucial for precise service life assessment. The most commonly employed techniques are strain gauge (SG) and blade tip-timing (BTT). While BTT has gained prominence over the past decades due to its nonintrusive nature, it is limited by undersampled data, leading to uncertainties when applied to impellers with unknown vibration frequencies. To compensate for this limitation, BTT is often accompanied by SG for vibration frequency identification. However, a quantitative comparison between these methods is challenging due to differences in the measured quantities, sampling resolution, and evaluation procedures. This paper addresses the discrepancies between SG and BTT measurements and proposes a unified methodology for their quantitative correlation. A numerical modal analysis is conducted to derive mode-dependent conversion factors (CF) between the SG measurements and the tangential deflections captured by BTT. To ensure accurate time-domain comparisons between the measurement techniques, the methodology incorporates the synchronization of their evaluation windows and validates them by comparing vibration properties such as amplitude, resonance frequency, and damping. Additionally, the uncertainty of the actual SG position and orientation as well as the correction of the axial BTT measurement plane are meticulously analyzed to refine the CF. The proposed methodology is validated using experimental data from a radial impeller acquired from an exhaust turbocharger test bench. These results not only improve the reliability and precision of vibration analysis in turbomachinery but also offer a comprehensive framework for future applications in blade health monitoring.
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