Journal of Shanghai Jiaotong University ›› 2016, Vol. 50 ›› Issue (05): 757-763.

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Separation of Weak MultiSource Fault Acoustic Emission Signals Based on Wavelet Packet and Independent Component Analysis

WANG Xianghong1a,1b,YIN Dong1a,HU Hongwei1a,MAO Hanling2   

  1. (1a. Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, 1b. Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science & Technology, Changsha 410004, China; 2. School of Mechanical Engineering Guangxi University, Nanning 530003, China)
  • Received:2015-04-23 Online:2016-05-28 Published:2016-05-28

Abstract: Abstract: Multisource fault signals (such as crack and friction signals) produced from rotating machinery are difficult to detect and separate; therefore, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and independent component analysis (ICA) was proposed. The wavelet packet technology was used to reduce the noise outside the frequency band of the linear mixed signals. The signals were decomposed by db2 wavelet into five layers while the signals with the frequency band from 62.5 to 187.5 kHz were reserved. Then, the mixed signals were separated by using the FastICA algorithm. Finally, the shrinkage function was used to reduce the noise in the frequency band. By extracting the noisy weak signals with different input SNRs, the results show that this method can effectively extract the crack and the friction signals with the input SNR higher than -15 dB. Their output SNRs are -1.31 and -1.36 dB and the correlation coefficients are 0.62 and 0.63, respectively, which are higher than those obtained by using the method combined WPA and FastICA and only FastICA algorithm. The SNRs are (-1.74 and -2.06 dB) and (-4.57,-4.31 dB) and correlation coefficients are (0.59,0.59) and (0.17,0.19) for the combined method and FastICA method, respectively. Thus, the method is very suitable for extraction and separation of multisource weak signals.

Key words: Key words: multi-source separation, wavelet packet analysis (WPA), independent component analysis (ICA), de-noising

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