Advertisement

Independent Component Analysis of Excavator Noise

  • Guohao Zhang
  • Qiangen Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

To identify excavator noise sources, an acoustic camera was used to acquire sound signals, and FastICA was applied to separate the signals. For strong background noise and echoic interference, the noise separation model was built based on FastICA algorithm in frequency-domain, then principle frequencies were obtained. To find the corresponding parts of these frequencies, modal analysis of major surface parts of the diesel was run in Ansys, and the modal analysis results were compared with principle frequencies. Research shows that ICA can effectively separate excavator sound signals contaminated by strong background noise and echoic interference; and the surface noise radiation sources such as cylinder block, cylinder head and valve cover were found by comparing component principle frequencies and modal analysis results.

Keywords

Excavator independent component analysis modal analysis convolutive mixture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, J.H., Yu, Y.L., Han, B.: IdentiFastICAtion of Diesel Front Sound Source Based on Continuous Wavelet Transform. Chinese Journal of Mechanical Engineering. 17(2), 268–271 (2004)CrossRefGoogle Scholar
  2. 2.
    Hao, Z.Y., Han, J.: IdentiFastICAtion of Diesel Front Sound Source Based on Contnuous Wavelet transform. Journal of Zhejiang University SCIENCE 5(9), 1069–1075 (2004)CrossRefGoogle Scholar
  3. 3.
    Wu, X., He, J.J., Jin, S.J.: Blind Separation of Speech Signals Based on Wavelet Transform and Independent Component Analysis. Transactions of Tianjin University 16(2), 123–128 (2010)CrossRefGoogle Scholar
  4. 4.
    Rennie, S.J., Arabi, P., Frey, B.J.: Variational Probabilistic Speech Separation Using Microphone Arrays. IEEE Transactions on Audio, Speech and Language Processing 15(1), 135–149 (2007)CrossRefGoogle Scholar
  5. 5.
    Long, F., He, J.S., Ye, X.Y.: Discriminant Independent Component Analysis as A Subspace Representation. Journal of Electronics 23(1), 103–106 (2006)Google Scholar
  6. 6.
    Lu, W., Yu, X.C.: Small Target Extraction Based on Independent Component Analysis for Hyperspectral Imagery. GEO-Spatial Information Science 9(2), 103–107 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zheng, C.H., Huang, D.S., Kong, X.Z.: Gene Expressio Data classifastication using Consensus Independent Component Analysis. Genomics Proteomics & Bioinformatics 6(2), 74–82 (2008)CrossRefGoogle Scholar
  8. 8.
    Park, H., Shekhar, D.C., Oh, S.: A Filter Bank Approach to Independent Component Analysis for Convolved Mixtures. Neurocomputing 69, 2065–2077 (2006)CrossRefGoogle Scholar
  9. 9.
    Hyvarinen, A.: Fast and Robust Fixed-point Algorithm for Independent Component Analysis. IEEE Trans. on Neural Network 10(3), 626–634 (1999)CrossRefGoogle Scholar
  10. 10.
    Back, A.D., Tsoi, A.C.: Blind Deconvolution of Signals using A Complex Recurrent Network. Neural Networks for Signal Processing 4, 565–574 (1994)Google Scholar
  11. 11.
    Smaragdis, P.: Blind Separation of Convolved Mixtures in The Frequency Domain. Neurocomputing 2, 21–34 (1998)CrossRefGoogle Scholar
  12. 12.
    Hyvarinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4-5), 411–430 (2000)CrossRefGoogle Scholar
  13. 13.
    Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, pp. 152–153. Johm Wiley & Sons, M. New York (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guohao Zhang
    • 1
    • 2
  • Qiangen Chen
    • 1
    • 2
  1. 1.National Key Laboratory of High performance and Complex ManufacturingChina
  2. 2.Department of Mechanical and Electrical EngineeringCentral South UniversityChangshaChina

Personalised recommendations