|Table of Contents|

[1] Wang Jiajun, Xu Feiyun,. Improved adaptive filter and its applicationin acoustic emission signals [J]. Journal of Southeast University (English Edition), 2019, 35 (1): 43-50. [doi:10.3969/j.issn.1003-7985.2019.01.007]
Copy

Improved adaptive filter and its applicationin acoustic emission signals()
改进的自适应滤波算法及其在声发射信号中的应用
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
35
Issue:
2019 1
Page:
43-50
Research Field:
Materials Sciences and Engineering
Publishing date:
2019-03-30

Info

Title:
Improved adaptive filter and its applicationin acoustic emission signals
改进的自适应滤波算法及其在声发射信号中的应用
Author(s):
Wang Jiajun Xu Feiyun
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
王佳俊 许飞云
东南大学大学机械工程学院, 南京 211189
Keywords:
acoustic emission adaptive filtering envelope demodulation least mean square(LMS)algorithm variable iteration step
声发射 自适应滤波 包络解调 LMS算法 变步长
PACS:
TG115.28
DOI:
10.3969/j.issn.1003-7985.2019.01.007
Abstract:
In order to de-noise and filter the acoustic emission(AE)signal, the adaptive filtering technology is applied to AE signal processing in view of the special attenuation characteristics of burst AE signal. According to the contradiction between the convergence speed and steady-state error of the traditional least mean square(LMS)adaptive filter, an improved LMS adaptive filtering algorithm with variable iteration step is proposed on the basis of the existing algorithms. Based on the Sigmoid function, an expression with three parameters is constructed by function translation and symmetric transformation.As for the error mutation, e(k)and e(k-1)are combined to control the change of the iteration step. The selection and adjustment process of each parameter is described in detail, and the MSE is used to evaluate the performance. The simulation results show that the proposed algorithm significantly increases the convergence speed, reduces the steady-state error, and improves the performance of the adaptive filter. The improved algorithm is applied to the AE signal processing, and the experimental signal is demodulated by an empirical mode decomposition(EMD)envelope to obtain the upper and lower envelopes. Then, the expected function related to the AE signal is established. Finally, the improved algorithm is substituted into the adaptive filter to filter the AE signal. A good result is achieved, which proves the feasibility of adaptive filtering technology in AE signal processing.
为了对声发射信号进行降噪滤波处理, 针对突发性声发射信号特有的衰减特性, 将自适应滤波技术应用到声发射信号处理中.针对传统LMS自适应滤波器收敛速度与稳态误差的矛盾问题, 在对比研究现有算法的基础上, 提出一种改进的变步长LMS自适应滤波算法, 以Sigmoid函数为原型, 通过函数平移、对称变换构建含有3个参数的表达式, 同时针对误差突变的情况, 将前后误差e(k)与e(k-1)相乘来联合控制改变步长因子, 并阐述了各个参数的选取与调整过程, 以均方误差MSE为衡量指标来评价性能.仿真结果表明该算法显著提高了收敛速度, 降低了稳态误差, 使自适应滤波器的性能得到提升.把改进算法应用到声发射信号中, 将实验信号通过EMD包络解调获得其上下包络线, 再建立与声发射信号相关的期望函数, 最后代入自适应滤波器中进行滤波处理, 取得了很好的效果, 证明了自适应滤波技术在声发射信号处理中的可行性.

References:

[1] Shen G T. Acoustic emission technology and application[M]. Beijing: Science Press, 2015:1-3.(in Chinese)
[2] Geng R S, Shen G T, Liu S F. An overview on the development of acoustic emission signal processing and analysis technique[J]. NondeStructive Testing, 2002, 24(1):23-28.
[3] Li M Y. Acoustic emission detection and signal processing [M].Beijing: Science Press, 2010:101-104.(in Chinese)
[4] Zhao X W, Ren B. Acoustic emission signal noise reduction based on the wavelet theory[J]. Electronic Science and Technology, 2013, 26(4): 119-123. DOI:10.3969/j.issn.1007-7820.2013.04.034. (in Chinese)
[5] Zhou J, Shuang H J, Liu L C. Application of wavelet transform in acoustic emission signal denoising[J]. Computer Application of Petrolerm, 2013(1):44-46.
[6] Xi J H, Xu N. De-noising of acoustic emission signals based on the combination of morphological filtering and EEMD[J]. Manufacturing Technology & Machine Tool, 2016(12): 70-75. DOI:10.19287/j.cnki.1005-2402.2016.12.013. (in Chinese)
[7] Zhang X T, Tang L W, Wang P, et al. De-noising method of acoustic emission signal based on improved MCKD [J]. Machine Design and Research, 2015, 31(1):70-73, 77.(in Chinese)
[8] Zhao C H. Adaptive signal processing technology [M]. Beijing: Beijing Institute of Technology Press, 2009:17-51.(in Chinese)
[9] Djendi M, Bounif A. Performance analysis of under-modeling stereophonic acoustic echo cancellation by adaptive filtering LMS algorithm[J]. Computers & Electrical Engineering, 2012, 38(6):1579-1594.
[10] Zhu Z, Gao X, Cao L L, et al. Analysis on the adaptive filter based on LMS algorithm[J]. OptikInternational Journal for Light and Electron Optics, 2016, 127(11): 4698-4704. DOI:10.1016/j.ijleo.2016.02.005.
[11] Kay S M. Fundamentals of statistical signal processing[M].Englewood Cliffs:PTR Prentice Hall, 1993:154-165.
[12]Diniz P S R. Adaptive filtering:Algorithms and practical implementation[M].Beijing:Publishing House of Electronics Industry, 2014.57-58
[13] Qin J F, Ouyang J Z. A novel variable step size LMS adaptive filtering algorithm based on sigmoid function[J]. Journal of Data Acquisition & Processing, 1997, 12(3): 171-174. DOI:10.16337/j.1004-9037.1997.03.003. (in Chinese)
[14] Zhang Y H, Yang H Y, Song Z G. Design of adaptive filter based on the normalized LMS Algorithm[J]. Journal of Jishou University(Natural Science Edition), 2012, 33(2):80-83.(in Chinese)
[15] Paleologu C, Benesty J, Ciochină S. Adaptive filtering for the identification of bilinear forms[J]. Digital Signal Processing, 2018, 75: 153-167. DOI:10.1016/j.dsp.2018.01.010.
[16] Batista E L O, Seara R. A fully LMS/NLMS adaptive scheme applied to sparse-interpolated Volterra filters with removed boundary effect[J]. Signal Processing, 2012, 92(10): 2381-2393. DOI:10.1016/j.sigpro.2012.02.011.
[17] Chen Y, Tian J P, Liu Y P. New variable step size LMS adaptive filtering algorithm[J]. Electronic Measurement Technology, 2015, 38(4): 27-31. DOI:10.19651/j.cnki.emt.2015.04.007. (in Chinese)
[18] Wu Z H, Wang F. ReLMS: Study of variable step size LMS algorithm based on the residual error[J]. Journal of Nanchang Hangkong University(Natural Sciences), 2017, 31(2): 34-38. DOI:10.3969/j.issn.1001-4926.2017.02.006. (in Chinese)
[19] Zhang H M, Han W G. A new variable step LMS algorithm and its application[J]. Chinese Journal of Scientific Instrument, 2015, 36(8): 1822-1830. DOI:10.3969/j.issn.0254-3087.2015.08.018. (in Chinese)
[20] Lü G Q, Duan H J. An improved variable step-size LMS adaptive harmonic detection algorithm for active power filters[J]. Power System Protection and Control, 2016, 44(7): 96-101. DOI:10.7667/PSPC150846. (in Chinese)
[21] Huang N E. New method for nonlinear and nonstationary time series analysis: Empirical mode decomposition and Hilbert spectral analysis[C]//Proc SPIE 4056, Wavelet Applications VII. Orlando, FL, USA, 2000: 197-210. DOI:10.1117/12.381681.
[22] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London A, 1998, 454(1971):903-995.

Memo

Memo:
Biographies: Wang Jiajun(1993—), male, graduate; Xu Feiyun(corresponding author), male, doctor, professor, fyxu@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.51575101).
Citation: Wang Jiajun, Xu Feiyun.Improved adaptive filter and its application in acoustic emission signals[J].Journal of Southeast University(English Edition), 2019, 35(1):43-50.DOI:10.3969/j.issn.1003-7985.2019.01.007.
Last Update: 2019-03-20