基于改进字典学习的单通道振动信号盲源分离算法
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摘要
单一传感器采集到的机械信号可能是多个激振源的叠加,难以进行有效分离。针对单通道盲源分离问题提出了基于改进字典学习的单通道振动信号盲源分离算法。首先利用移不变字典学习算法学习信号中的移不变基函数,重构基函数得到反映信号时频域特征的移不变分量,然后利用自适应模糊C均值聚类算法及局部最大值检测算法对得到的各个移不变分量的包络谱提取关键点并聚类,最后将聚类后的移不变分量进行叠加得到源信号的估计。仿真数据的实验表明,算法在噪声环境下具有一定的鲁棒性,同时将该算法应用到实测某型直升机振动信号分离中,进一步验证了该算法的实际价值。
Aiming at the problem of vibration signals collected by a single sensor possible to be a sum of several sources and hard to be separated,a method based on improved dictionary learning was proposed for single channel blind source separation of vibration signals. Firstly,the shift invariant dictionary learning algorithm was utilized to learn shift invariant base functions in signals. Then,shift invariant components( SIC) to reflect signals' features in time domain and frequency one were obtained by reconstructing base functions. An adaptive fuzzy C-means clustering algorithm and the local maximum detection method were utilized to extract key points on envelope spectrum of each SIC obtained and these points were clustered. Finally,the clustered SICs were superimposed,respectively to acquire estimations of source signals. The tests of simulation data demonstrated that the proposed method has certain robustness in the presence of noise; it is used to conduct a certain type helicopter vibration signals separation,and verify its actual application value.
Aiming at the problem of vibration signals collected by a single sensor possible to be a sum of several sources and hard to be separated,a method based on improved dictionary learning was proposed for single channel blind source separation of vibration signals. Firstly,the shift invariant dictionary learning algorithm was utilized to learn shift invariant base functions in signals. Then,shift invariant components( SIC) to reflect signals' features in time domain and frequency one were obtained by reconstructing base functions. An adaptive fuzzy C-means clustering algorithm and the local maximum detection method were utilized to extract key points on envelope spectrum of each SIC obtained and these points were clustered. Finally,the clustered SICs were superimposed,respectively to acquire estimations of source signals. The tests of simulation data demonstrated that the proposed method has certain robustness in the presence of noise; it is used to conduct a certain type helicopter vibration signals separation,and verify its actual application value.
引文
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[3]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,47(4):12-17.WU Wenfeng,CHEN Xiaohu,SU Xunjia.Blind source separation of single channel mechanical signal based on empirical mode decomposition[J].Journal of Mechanical Engineering,2011,47(4):12-17.
[4]刘佳,杨士莪,朴胜春.基于EEMD的地声信号单通道盲源分离[J].哈尔滨工程大学学报,2011,32(2):194-201.LIU Jia,YANG Shi’e,PIAO Shengchun.The single channel seismic-acoustic signal blind-source separation method based on EEMD[J].Journal of Harbin Engineering University,2011,32(2):194-201.
[5]LI Y,AMARI S,CICHOCKI A,et al.Underdetermined blind source separation based on sparse representation[J].IEEE Transactions on Signal Processing,2006,54(2):423-437.
[6]YU K,YANG K,BAI Y.Estimation of modal parameters using the sparse component analysis based underdetermined blind source separation[J].Mechanical Systems&Signal Processing,2014,45(2):302-316.
[7]SMITH E C,LEWICKI M S.Efficient auditory coding[J].Nature,2006,439(7079):978-982.
[8]TANG Haifeng,CHEN Jin,DONG Guangming.Sparse representation based latent components analysis for machinery weak fault detection[J].Mechanical Systems and Signal Processing,2014,46:373-388.
[9]苗中华,周广兴,刘海宁,等.基于稀疏编码的振动信号特征提取算法与实验研究[J].振动与冲击,2014,33(15):76-81.MIAO Zhonghua,ZHOU Guangxing,LIU Haining,et al.Tests and feature extraction algorithm of vibration signals based on sparse coding[J].Journal of Vibration and Shock,2014,33(15):76-81.
[10]PATI Y C,REZAIIFAR R,KRISHNAPRASAD P S.Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C]∥Signals,Systems and Computers,1993.1993 Conference Record of The Twenty-Seventh Asilomar Conference on.IEEE,1993:40-44.
[11]SZLAM A,KAVUKCUOGLU K,LECUN Y.Convolutional matching pursuit and dictionary training[J].Computer Science,2010(10):1-7.
[12]BLUMENSATH T,DAVIES M.Shift-invariant sparse coding for single channel blind source separation[J].SPARS,2005(5):75-78.
[13]GROSSE R,RAINA R,KWONG H,et al.Shift-Invariant Sparse Coding for Audio Classification[C]∥Ron Parr,Linda van der Gaag.Conference on Uncertainty in AI.Vancouver:AUAI Press,2007.
[14]SOUMI G,DUBEY S K.Comparative analysis of k-means and fuzzy c-means algorithms[J].International Journal of Advanced Computer Science&Applications,2013,4(4):34-39.(下转第109页)
[15]HU Chunzhi,YANG Qiang,HUANG Miaoying,et al.Sparse component analysis-based underdetermined blind source separation for bearing fault feature extraction in wind turbine gearbox[J].IET Renewable Power Generation,2017,11(3):330-337.
[16]HAVENS T C,BEZDEK J C,LECKIE C,et al.Fuzzy cmeans algorithms for very large data[J].IEEE Transactions on Fuzzy System,2012,20(6):1130-1146.
[17]朱会杰,王新晴,芮挺,等.基于移不变稀疏编码的单通道机械信号盲源分离[J].振动工程学报,2015,28(4):625-632.ZHU Huijie,WANG Xinqing,RUI Ting,et al.Shift invariant sparse coding for blind source separation of single channel mechanical signal[J].Journal of Vibration Engineering,2015,28(4):625-632.
[18]SAWALHI N,RANDALL R.Simulating gear and bearing interations in the presence of faults:Part 1.The combined gear bearing dynamic model and the simulation of localized bearing faults[J].Mechanical Systems and Signal Processing,2008,22(8):1924-1951.