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Centroid-based sifting for empirical mode decomposition 被引量：1

Centroid-based sifting for empirical mode decomposition
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摘要 A novel sifting method based on the concept of the 'local centroids' of a signal is developed for empirical mode decomposition (EMD), with the aim of reducing the mode-mixing effect and decomposing those modes whose frequencies are within an octave. Instead of directly averaging the upper and lower envelopes, as suggested by the original EMD method, the proposed technique computes the local mean curve of a signal by interpolating a set of 'local centroids', which are integral averages over local segments between successive extrema of the signal. With the 'centroid'-based sifting, EMD is capable of separating intrinsic modes of oscillatory components with their frequency ratio ν even up to 0.8, thus greatly mitigating the effect of mode mixing and enhancing the frequency resolving power. Inspection is also made to show that the integral property of the 'centroid'-based sifting can make the decomposition more stable against noise interference. A novel sifting method based on the concept of the ＇local centroids＇ of a signal is developed for empirical mode decomposition （EMD）, with the aim of reducing the mode-mixing effect and decomposing those modes whose frequencies are within an octave. Instead of directly averaging the upper and lower envelopes, as suggested by the original EMD method, the proposed technique computes the local mean curve of a signal by interpolating a set of ＇local centroids＇, which are integral averages over local segments between successive extrema of the signal. With the ＇centroid＇-based sifting, EMD is capable of separating intrinsic modes of oscillatory components with their frequency ratio v even up to 0.8, thus greatly mitigating the effect of mode mixing and enhancing the frequency resolving power. Inspection is also made to show that the integral property of the ＇centroid＇-based sifting can make the decomposition more stable against noise interference.
作者 Hong HONG Xin-long WANG Zhi-yong TAO Shuan-ping DU
机构地区 Key Laboratory of Modern Acoustics and Institute of Acoustics State Key Laboratory of Ocean Acoustics
出处 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2011年第2期88-95,共8页 浙江大学学报C辑（计算机与电子（英文版）
基金 Project supported by the National Natural Science Foundation of China (No. 10574070) the State Key Laboratory Foundation of China (No. 9140C240207060C24)
关键词 SIFTING Empirical mode decomposition (EMD) Mode mixing effect Frequency resolution Local centroids Noise resistance Sifting, Empirical mode decomposition （EMD）, Mode mixing effect, Frequency resolution, Localcentroids, Noise resistance
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献18

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同被引文献21

  • 1YAN Zhi-guo,WANG Zhi-zhong,REN Xiao-mei.Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification[J].Journal of Zhejiang University-Science A(Applied Physics & Engineering),2007,8(8):1246-1255. 被引量：5
  • 2Shang B L, Xia Y, Zhang Z R et al. Fault diagnosis of dieselvalve by using GA algorithm[J]. Transactions ofCSICE, 2000, 18(4): 419-422(in Chinese).
  • 3Wang C D, Zhang Y Y, Zhong Z Y. Fault diagnosis fordiesel valve trains based on time-frequency images[J].Mechanical Systems and Signal Processing, 2008, 22(8):1981-1993.
  • 4Jiang Y H, Tang B P, Qin Y et al. Feature extractionmethod of wind turbine based on adaptive Morlet waveletand SVD[J]. Renewable Energy, 2011, 36(8): 2146-2153.
  • 5Cheng J S, Yu D J, Tang J S et al. Application of SVM andSVD technique based on EMD to the fault diagnosis of therotating machinery[J]. Shock and Vibration, 2009, 16(1):89-98.
  • 6Yang Y, Cheng J S, Zhang K. An ensemble local meandecomposition method and its application to local rubimpactfault diagnosis of the rotor systems[J].Measurement, 2012, 45(3): 561-570.
  • 7Smith J S. The local mean decomposition and its applicationto EEG perception data[J]. Journal of the Royal SocietyInterface, 2005, 2(5): 443-454.
  • 8Park C, Looney D, Hulle M M. The complex local meandecomposition[J]. Neurocomputing, 2011, 74(6): 867-875.
  • 9Chen B J, He Z J, Chen X F et al. A demodulating approachbased on local mean decomposition and its applicationsin mechanical fault diagnosis[J]. Measurement Science& Technology, 2011, 22(5): 55704-55716.
  • 10Zhang J H, Liu Y. Bearing fault diagnosis based on improvedLMD[C]. In: Proceedings of the 2011 InternationalConference on Transportation and Mechanical &Electrical Engineering. Changchun, China, 2011. 2544-2547.

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Journal of Zhejiang University-Science C(Computers and Electronics)
2011年 第2期

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