Enhancing Sparse Decomposition Based Blade Vibration Parameter Identification

doi:10.3901/JME.2019.19.019

Abstract

Abstract: Blade tip timing is an engine blade monitoring method proposed in recent years, compared with the traditional strain gauge measurement method, it can not only simultaneously monitor the vibration state of all blades, but also will not affect the vibration condition of blades. However, due to the high undersampling characteristics of blade tip timing data, a blade parameter identification technology is proposed based on enhancing sparse decomposition. Sparse decomposition is a signal processing method that decomposes the signal into a sparse solution via a redundant dictionary. Enhancing sparse decomposition improves the traditional basic pursuit whose solution is not sparse enough, and the enhancing sparse decomposition method can effectively reduce the sampling rate. After the enhancing sparse optimization model is established, the original dual intra-point method is used to solve the optimization problem. The proposed blade parameter identification technology based on enhancing sparse decomposition is applied to different types of simulation data and rotor blade test rig to identify blades' parameter, the effectiveness of the algorithm has been verified.

Key words: blade tip timing, enhancing sparse decomposition, undersampling, parameter identification

CLC Number:

TH17

WU Shuming, HU Haifeng, ZHAO Zhibin, YANG Zhibo, YANG Laihao, TIAN Shaohua, CHEN Xuefeng. Enhancing Sparse Decomposition Based Blade Vibration Parameter Identification[J]. Journal of Mechanical Engineering, 2019, 55(19): 19-27.

References

[1] WITO M,ZIEJA M. High sensitive methods for fatigue detection[J]. Journal of Konbin,2011,1(17):307-326.
[2] ZABLOTSKY I E,KOROSTELEV J A,LEBEDEV A V,et al. Vibrator indicator for turboengine rotor blading[Z]. Google Patents,1969.
[3] 欧阳涛. 旋转叶片振动性能参数测试技术研究[D]. 天津:天津大学,2008. OUYANG Tao. Rotating blade vibration parameters identification research[D]. Tianjin:University of Tianjin,2008.
[4] HEATH S. A new technique for identifying synchronous resonances using tip-timing[C]//ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition,June 7-10,1999,Indianapolis,Indiana,USA:ASME,1999:V004T04A024-V004T04A024.
[5] CARRINGTON I B,WRIGHT J R,COOPER J E,et al. A comparison of blade tip timing data analysis methods[J]. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering,2001,215(5):301-312.
[6] JOUNG K K,KANG S C,PAENG K S,et al. Analysis of vibration of the turbine blades using non-intrusive stress measurement system[C]//ASME 2006 Power Conference. American Society of Mechanical Engineers,May 2-4,2006,Atlanta,Georgia,USA,2006:391-397.
[7] GALLEGO J,DIMITRIADIS G,WRIGHT J R. A class of methods for the analysis of blade tip timing data from bladed assemblies undergoing simultaneous resonances-Part I:Theoretical development[J]. International Journal of Rotating Machinery,2007,2007(591):981-1077.
[8] KOMINSKY D. Differential focus blade clearance probe and methods for using same:U.S. Patent 8,164,761[P]. 2012-04-24.
[9] RZADKOWSKI R,ROKOCKI E,PIECHOWSKI L,et al. Analysis of middle bearing failure in rotor jet engine using tip-timing and tip-clearance techniques[J]. Mechanical Systems & Signal Processing,2016,76-77:213-227.
[10] LIN J,HU Z,CHEN Z S,et al. Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring[J]. Mechanical Systems & Signal Processing,2016,81:250-258.
[11] CHEN S S,DONOHO D L,SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review,2001,43(1):129-159.
[12] WAGNER A,WRIGHT J,GANESH A,et al. Toward a practical face recognition system:Robust alignment and illumination by sparse representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(2):372-386.
[13] XU Y,ZHU Q,WANG Y,et al. From the idea of "sparse representation" to a representation-based transformation method for feature extraction[J]. Neurocomputing,2013,113:168-176.
[14] FAROUK R M. Image denoising based on sparse representation and non-negative matrix factorization[J]. Life Science Journal,2012,9(1):337-341.
[15] ZHAN X,ZHANG R,YIN D,et al. SAR image compression using multiscale dictionary learning and sparse representation[J]. IEEE Geoscience & Remote Sensing Letters,2013,10(5):1090-1094.
[16] WANG J,SHE M,NAHAVANDI S,et al. Human identification from ECG signals via sparse representation of local segments[J]. IEEE Signal Processing Letters,2013,20(10):937-940.
[17] WU C,JIAN L,PENG F,et al. Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition[J]. Chinese Journal of Mechanical Engineering,2013,26(4):831-838.
[18] MAOLIN L I. Sensitive feature extraction of machine faults based on sparse representation[J]. Journal of Mechanical Engineering,2013,49(1):73.
[19] CANDES E J,WAKIN M B,BOYD S P. Enhancing sparsity by reweighted l1 minimization[J]. Journal of Fourier Analysis & Applications,2008,14(5-6):877-905.
[20] DONG W,ZHANG L,SHI G,et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2011,20(7):1838.
[21] 张晗,杜朝辉,方作为,等. 基于稀疏分解理论的航空发动机轴承故障诊断[J]. 机械工程学报,2015,51(1):97-105. ZHANG Han,DU Zhaohui,FANG Zuowei,et al. Sparse decomposition based aero-engine's bearing fault diagnosis[J]. Journal of Mechanical Engineering,2015,51(1):97-105.
[22] 郭金库. 信号稀疏表示理论及其应用[M]. 北京:科学出版社,2013. GUO Jinku. Signal sparse representation theory and application[M]. Beijing:Science Press,2013.
[23] MALLAT S G,ZHANG Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Trans. on Signal Processing,1993,41(12):3397-3415.
[24] TROPP J A,GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[25] AlKARAKI J N,KAMAL A E. Routing techniques in wireless sensor networks:A survey[M]. New York:IEEE Press,2004.
[26] BOYD S P,VANDENBERGHE,FAYBUSOVICH. Convex optimization[J]. IEEE Transactions on Automatic Control,2006,51(11):1859.
[27] CANDES E J,TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[28] AFONSO M V,BIOUCAS-DIAS J M,FIGUEIREDO M A T. An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[M]. London:IEEE Press,2011.
[29] KIM S J,KOH K,LUSTIG M,et al. An interior-point method for large-scale l 1-regularized least sequares[J]. IEEE Journal on Selected Topics in Signal Processing,2007,1(4):606-617.
[30] NOCEDAL J,WRIGHT S J. Numerical optimization[M]. London:Springer,1999.
[31] DIAMOND D H,HEYNS P S. A novel method for the design of proximity sensor configuration for rotor blade tip timing[J]. Journal of Vibration and Acoustics,2018,140(6):61003.
[32] PAN M,YANG Y,GUAN F,et al. Sparse representation based frequency detection and uncertainty reduction in blade tip timing measurement for multi-mode blade vibration monitoring[J]. Sensors,2017,17(8):1745.