Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

Article Preview

Abstract:

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1738-1744

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] LI Wen-feng, WANG Yong-sheng, YANG Ji-ming, WANG Su, Noise characteristics analysis of aero-engine testing signals, Journal of Aerospace Power. 20 (2005) 900-904.

Google Scholar

[2] D. L. Donoho, Unconditional Bases are Optimal Bases for Data Compression and for Statistical Estimation, Department of Statistics, Stanford University, Stanford, California, (1993).

DOI: 10.1006/acha.1993.1008

Google Scholar

[3] KANG Wei-xin, PENG Xi-yuan, The construction and application of wavelet function for pile foundation detection, Electric Machines and Control. 12 (2008) 357-360.

Google Scholar

[4] Coifman R, Wickerhauser M V, Entropy based algorithms for best basis selection, IEEE Transactions on IT. 38 (1992) 713-718.

DOI: 10.1109/18.119732

Google Scholar

[5] Donoho D L, De-noising by soft-thresholding, IEEE Transactions on Information Theory. 41(1995)613-627.

DOI: 10.1109/18.382009

Google Scholar

[6] DONG Y S, Y IX M. Wavelet de-nosing based on four improved function for threshold estimation, Journal of Math. 26 (2006) 473- 477.

Google Scholar

[7] ZHOU Mi, LI Zun-zun, GENG Guo-hua, Research of image denoising method based on wavelet threshold, Computer Technology and Development. 18 (2008) 22-24.

Google Scholar

[8] WANG Bing, CHEN Yu, Denoise method of improved threshold for signal excited by embedded piezoelectric sound source, Chinese Journal of Sensors and Actuators. 26 (2013) 1409-1413.

Google Scholar

[9] ZANG Xian-feng, ZHANG Zheng-dao, Optimal threshold for signal denoising based on wavelet entropy, Journal of Jiangnan University (Natural Science Edition). 8 (2009) 267-270.

Google Scholar

[10] Zhao Kai, LI Ben-wei, Study on the Evaluation of Engine Performance Based on Hybrid Optimization Algorithm, in: Jinsong Wang, Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II, Northwestern Polytechnical University, Xi'an, 2014, pp.489-497.

DOI: 10.1007/978-3-642-54233-6_54

Google Scholar

[11] XIAO Fang-yu, TANG Wei, FU Na, Wavelet based de-nosing self-optimizing method, Signal Processing. 28 (2012) 577-586.

Google Scholar

[12] Wei Fang, Jun Sun, Yanrui Ding, Xiaojun Wu and Wenbo Xu, A Review of Quantum-behaved Particle Swarm Optimization, IETE Technical Review. 27 (2010) 336-348.

DOI: 10.4103/0256-4602.64601

Google Scholar