A Variable Evolutionary Coefficients Adaptive Noise Cancellation Algorithm
Factors of fitness function using the reciprocal of the average energy of the output signal, constant cloning and mating coefficients all restrict the convergence and noise cancellation performances of the traditional evolutionary adaptive noise cancellation algorithm. In this paper, the variable coefficients evolutionary adaptive noise cancellation algorithm using dynamic fitness function is proposed. The mating coefficient is related to evolutionary generation and the cloning coefficient is determined by evolutionary generation and fitness value, and a dynamic fitness function is also employed. Results from simulation case and test datasets show that the proposed algorithm exhibits better convergence and noise cancellation performances than other similar algorithms.
1. National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083;
2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044
Abstract：Factors of fitness function using the reciprocal of the average energy of the output signal, constant cloning and mating coefficients all restrict the convergence and noise cancellation performances of the traditional evolutionary adaptive noise cancellation algorithm. In this paper, the variable coefficients evolutionary adaptive noise cancellation algorithm using dynamic fitness function is proposed. The mating coefficient is related to evolutionary generation and the cloning coefficient is determined by evolutionary generation and fitness value, and a dynamic fitness function is also employed. Results from simulation case and test datasets show that the proposed algorithm exhibits better convergence and noise cancellation performances than other similar algorithms.
[1] Lin J. Gearbox fault diagnosis using adaptive wavelet filter [J]. Mechanical Systems and Signal Processing, 2003, 17 (6): 1259-1269.
[2] Chiementin X, Bolaers F, Dron J P. Early detection of fatigue damage on rolling element bearings using adapted wavelet [J]. ASME Journal of Vibration and Acoustics, 2007, 129: 495-506.
[3] Panda G, Pradhan P M, Majhi B. IIR system identification using cat swarm optimization [J]. Expert Systems with Applications, 2011, 38: 12671-12683.
[4] Ibrahim G, Albarbar A, Abouhnik A, Shnibha R. Adaptive filtering based system for extracting gearbox condition feature from the measured vibrations [J]. Measurement, 2013, 46: 2029-2034.
[5] Zhou X J, Shao Y M. Adaptive noise cancellation based on beehive pattern evolutionary digital filter [J]. Mechanical Systems and Signal Processing, 2014, 42: 225-235.
[6]严保康,周凤星. 一种基于形态提升的自适应轴承微冲击提取方法[J]. 振动与冲击, 2013, 32(24): 198-203.
YAN Bao-kang, ZHOU Feng-xing. Adaptive weak impulse extraction method of rolling bearings based on morphological lifting wavelet [J]. Journal of Vibration and Shock, 2013, 32(24):198-203.
[7]阳子婧,蔡力钢,高立新.自适应冗余提升小波降噪分析及轴承故障识别应用[J]. 振动与冲击,2013,32(7): 54-57.
YANG Zi-jing, CAI Li-gang, GAO Li-xin. Adaptive redundant lifting wavelet denoising analysis and its application in bearing fault identification [J]. Journal of Vibration and Shock, 2013, 32(7): 54-57.
[8]杨辉跃, 涂亚庆, 张海涛,等. 振动信号频率跟踪的反馈修正自适应陷波器法[J]. 振动与冲击, 2014, 33(3):145-149.
YANG Hui-yue, TU Ya-qing, ZHANG Hai-tao, et al. Feedback corrected adaptive notch filter for vibration signal frequency tracking [J]. Journal of Vibration and Shock, 2014, 33(3): 145-149.
[9] Kawamata M, Abe M, Higuchi T. Evolutionary digital filters- adaptive digital filters based on evolutionary strategies of organisms[C]. Proceedings of International Workshop on Intelligent Signal Processing and Communication Systems, 1994: 263-268.
[10] 邵毅敏,周晓君,马祥华,等.进化论自适应滤波算法及降噪特性研究[J].振动与冲击, 2007, 26(12): 157-159, 166.
SHAO Yi-min, ZHOU Xiao-jun, MA Xiang-hua, et al. Noise cancellation using an evolutionary adaptive algorithm [J]. Journal of Vibration and Shock, 2007, 26(12): 157-159+166.
[11] Abe M, Kawamata M. Convergence behavior of evolutionary digital filters on a multi-peak surface[C]. In Proceedings of IEEE International Symposium on Circuits and Systems, 1996, 2: 185-188.
[12] Abe M, Kawamata M. Comparison of the convergence of IIR evolutionary digital filters and other adaptive digital filters on a multiple-peak surface [C]. Conference Record of the Thirty-First Asilomar Conference on Signal, Systems, and Computers, 1997, 2: 1674-1678.
[13] Sheen Y T. A complex filter for vibration signal demodulation in bearing defect diagnosis [J]. Journal of Sound and Vibration, 2004, 276: 105-119.
[14] Goldberg D E. Genetic algorithms in search, optimization and machine learning [M]. Massachusetts: Addison-Wesley Professional, 1989.
[15] http://csegroups.case.edu/bearingdatacenter/pages.
