The Identification of Engine Noise Source Based on Simulink Blind Sources Separation

Jun Wen Gao; Yan Ming Quan; Yong Man Lin

doi:10.4028/www.scientific.net/AMR.328-330.2134

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 328-330The Identification of Engine Noise Source Based on...

The Identification of Engine Noise Source Based on Simulink Blind Sources Separation

Abstract:

AC algorithm of blind sources has been applied to the identification of engine noise sources. However, the testing platform based on the blind separation theory is difficult to actualize some experiments and obtain data in the real environment, or to obtain these requires high cost. Therefore, this paper proposes a new approach to construct an experimental testing platform-Sinmulink Model so as to identify engine surface noise sources. This model is applied to the test of the experimental data. These experiments demonstrate that Simulink model is efficient to identify engine noise source and the testing data accord with that made by the former platform. It is obvious that Simulink model, to some degree, can be used to replace engine surface noise sources testing platform.

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Periodical:

Advanced Materials Research (Volumes 328-330)

Pages:

2134-2139

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.328-330.2134

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Online since:

September 2011

Authors:

Jun Wen Gao, Yan Ming Quan, Yong Man Lin

Keywords:

Blind Source Separation (BSS), Engine Noise Source, Mixed Signal, Simulink

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References

[1] X.R. Chen. Z.Y. Hao and C. Yang: Automotive Engineering, Vol. 32 (2010) No. 1, p.41. (In Chinese).

Google Scholar

[2] J. Liang. W.X. Fan and F. P. Zhang: Noise and Vibration Control, Vol. 28 (2008) No. 6, p.32.

Google Scholar

[3] Z.Y. Hao, Y.H. Liu and F.R. B: Transactions CSICE, Vol. 22 (2004) No. 2, p.150. (In Chinese).

Google Scholar

[4] X.C. Lu and Z. Huang: Transactions of the Chinese Society of Agricultural Michinery, Vol. 35 (2004) No. 5, p.51. (In Chinese).

Google Scholar

[5] Y. Tan, and J. Wang: IEEE Transactions on Neural Network, Vol. 12 (2001) No. 1, p.124.

Google Scholar

[6] M. Solazzi and A. Uncini: IEEE Transactions on Circuits and Systems-I, Vol. 51 (2004) No. 4, p.817.

Google Scholar

[7] T. Brookings, S. Ortigueb, S. Graftonb: Neuro Imgae. Vol. 44 (2009) No. 2, p.411.

Google Scholar

[8] A. Cichocki, R.E. Bogner and L. Moszczynski: Digital Signal Processing, Vol. 7 (1997) No. 1, p.80.

Google Scholar

[9] W.H. Guan and Y.M. Lin: Vehicle Engine, Vol. 5 (2006) No. 5, p.48. (In Chinese).

Google Scholar