Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring

Abstract Severe blade vibrations may reduce the useful life of the high-speed blade. Nowadays, non-contact measurement using blade tip-timing (BTT) technology is becoming promising in blade vibration monitoring. However, blade tip-timing signals are typically under-sampled. How to extract characteristic features of unknown multi-mode blade vibrations by analyzing these under-sampled signals becomes a big challenge. In this paper, a novel BTT analysis method for reconstructing unknown multi-mode blade vibration signals is proposed. The method consists of two key steps. First, a sparse representation (SR) mathematical model for sparse blade tip-timing signals is built. Second, a multi-mode blade vibration reconstruction algorithm is proposed to solve this SR problem. Experiments are carried out to validate the feasibility of the proposed method. The main advantage of this method is its ability to reconstruct unknown multi-mode blade vibration signals with high accuracy. The minimal requirements of probe number are also presented to provide guidelines for BTT system design.

[1]  Satish Nagarajaiah,et al.  Structural damage identification via a combination of blind feature extraction and sparse representation classification , 2014 .

[2]  Philippe Voinis,et al.  Modal parameter identification of mistuned bladed disks using tip timing data , 2008 .

[3]  Jie Chen,et al.  Theoretical Results on Sparse Representations of Multiple-Measurement Vectors , 2006, IEEE Transactions on Signal Processing.

[4]  Jan R. Wright,et al.  BLADE-TIP TIMING MEASUREMENT OF SYNCHRONOUS VIBRATIONS OF ROTATING BLADED ASSEMBLIES , 2002 .

[5]  Craig Lawson,et al.  Tubomachinery blade vibration amplitude measurement through tip timing with capacitance tip clearanc , 2005 .

[6]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.

[7]  Huibin Lin,et al.  Fault feature extraction of rolling element bearings using sparse representation , 2016 .

[8]  Yongchao Yang,et al.  Output-only modal identification by compressed sensing: Non-uniform low-rate random sampling , 2015 .

[9]  Yongmin Yang,et al.  A Non-Uniformly Under-Sampled Blade Tip-Timing Signal Reconstruction Method for Blade Vibration Monitoring , 2015, Sensors.

[10]  Ping Feng,et al.  Spectrum-blind minimum-rate sampling and reconstruction of multiband signals , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[11]  Grigorios Dimitriadis,et al.  Multiple Frequency Analysis Methods for Blade Tip-Timing Data Analysis , 2004 .

[12]  D Knappett,et al.  Blade tip timing and strain gauge correlation on compressor blades , 2008 .

[13]  Minh N. Do,et al.  A Theory for Sampling Signals from a Union of Subspaces , 2022 .

[14]  Gotzon Aldabaldetreku,et al.  An Optical Fiber Bundle Sensor for Tip Clearance and Tip Timing Measurements in a Turbine Rig , 2013, Sensors.

[15]  Hu Zheng,et al.  Blade damage prognosis based on kernel principal component analysis and grey model using subsampled tip-timing signals , 2014 .

[16]  Régis Lengellé,et al.  Nonintrusive turbomachine blade vibration measurement system , 2007 .

[17]  Zheng Hu,et al.  Non-contact crack detection of high-speed blades based on principal component analysis and Euclidian angles using optical-fiber sensors , 2013 .

[18]  Mehmet Imregun,et al.  An improved single-parameter tip-timing method for turbomachinery blade vibration measurements using optical laser probes , 1996 .

[19]  Jan R. Wright,et al.  A class of methods for the analysis of Blade Tip Timing Data from bladed assemblies undergoing simultaneous resonances. Part I: Theoretical Development , 2007 .

[20]  Richard H. Sherman,et al.  Chaotic communications in the presence of noise , 1993, Optics & Photonics.

[21]  K. S. Chana,et al.  The Use of Eddy Current Sensors for the Measurement of Rotor Blade Tip Timing: Sensor Development and Engine Testing , 2008 .

[22]  Y. Bresler.  Spectrum-blind sampling and compressive sensing for continuous-index signals , 2008, 2008 Information Theory and Applications Workshop.

[23]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[24]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[25]  M.Zielinski,et al.  Noncontact Blade Vibration Measurement System for Aero Engine Application , 2005 .

[26]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[27]  P. Tappert,et al.  Health monitoring and prognostics of blades and disks with blade tip sensors , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[28]  Yongchao Yang,et al.  Output-only modal identification with limited sensors using sparse component analysis , 2013 .

[29]  Xinpeng Zhang,et al.  A bearing fault diagnosis method based on the low-dimensional compressed vibration signal , 2015 .