Compressed Sensing-based Blind Reconstruction of Multi-frequency Blade Vibration from Under-sampled BTT Signals

doi:10.3901/JME.2019.13.113

Abstract

Abstract: The rotated blade is the core component of aeroengine. However, the blades work in complex working conditions for a long time and are subjected to alternating stresses, which make balde vibrations and result in fatigue failure. Therefore, on-line vibration monitoring is of great engineering significance to operation safety for aeroengine. As the traditional contact strain method cannot measure vibration of all blades at the same time and the arrangement of wire is complex which may bring security risks, Blade tip-timing (BTT) technology is used for the blade real time online vibration monitoring. However, BTT signals are typically under-sampled signals by limited installation of BTT sensors. In addition, blade vibrations are always multi-frequency with less prior knowledge due to crack nonlinearity and complicated aerodynamic excitations. So it is a big challenge that unknowns multi-frequency blade vibrations are reconstructed from BTT under-sampled measurements. The compressed sensing (CS) theory is proposed to reconstruct unknown multi-band blade vibrations from under-sampled BTT measurements. Firstly, it is analyzed that multi-frequency blade vibrations are excited by aerodynamic excitations. Then, The CS model of BTT measurement is built and the MUSIC algorithm is selected to solve the model. Finally, numerical simulations and vibrations measurement of rotated blades by BTT method are used to validate the proposed method.

Key words: aeroengine, blade tip-timing, blade vibration monitoring, compressed sensing, under-sampled signal reconstruction

CLC Number:

V232

XU Hailong, YANG Yongmin, HU Haifeng, GUAN Fengjiao, CHEN Zhongsheng. Compressed Sensing-based Blind Reconstruction of Multi-frequency Blade Vibration from Under-sampled BTT Signals[J]. Journal of Mechanical Engineering, 2019, 55(13): 113-121.

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