|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]

Improved adaptive filter and its applicationin acoustic emission signals()

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

2019 1
Research Field:
Materials Sciences and Engineering
Publishing date:


Improved adaptive filter and its applicationin acoustic emission signals
Wang Jiajun Xu Feiyun
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
acoustic emission adaptive filtering envelope demodulation least mean square(LMS)algorithm variable iteration step
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.


[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.


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