Gear Fault Detection and Diagnosis Based on its Fabrication Material

Shu Feng Ai

doi:10.4028/www.scientific.net/AMR.321.140

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 321Gear Fault Detection and Diagnosis Based on its...

Gear Fault Detection and Diagnosis Based on its Fabrication Material

Abstract:

A novel application of Hilbert-Huang transform method to fault diagnosis of gear crack is presented. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the empirical mode decomposition. Then the Hilbert transform is applied to each intrinsic mode function. Therefore, the time-frequency distribution is obtained.

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Periodical:

Advanced Materials Research (Volume 321)

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140-145

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.321.140

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Online since:

August 2011

Authors:

Shu Feng Ai

Keywords:

Fault Diagnosis, Gear, Hilbert-Huang Transform (HHT), Signal Processing

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References

[1] L. Cohen, Time-frequency analysis, Prentice-Hall, Englewood Cliffs, NJ, (1995).

Google Scholar

[2] J. Lin and L. Qu, Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, Journal of Sound and Vibration, Vol. 234, pp.135-148, (2000).

DOI: 10.1006/jsvi.2000.2864

Google Scholar

[3] W.J. Staszewski, Wavelet based compression and feature selection for vibration analysis, Journal of Sound and Vibration, Vol. 211, pp.736-760, (2000).

DOI: 10.1006/jsvi.1997.1380

Google Scholar

[4] C. James Li and Jun Ma, Wavelet decomposition of vibration for detection of bearing-localized defects, NDT&E International, Vol. 30, pp.143-149, (1997).

DOI: 10.1016/s0963-8695(96)00052-7

Google Scholar

[5] S. Prabhakar, A.R. Mohanty and A. S Sekhar, Application of discrete wavelet transform for detection of ball bearing race fault, Tribology International, Vol. 3, pp.793-800, (2002).

DOI: 10.1016/s0301-679x(02)00063-4

Google Scholar

[6] W.J. Wang and P.D. Mcfadden, Application of orthogonal wavelet to early gear damage detection, Mechanical Systems and Signal Processing, Vol. 9, pp.497-507, (1995).

DOI: 10.1006/mssp.1995.0038

Google Scholar

[7] W.J. Staszewski, K. Worden and G.R. Tomlinson, The-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition, Mechanical Systems and Signal Processin, Vol. 11, pp.673-692, (1997).

DOI: 10.1006/mssp.1997.0102

Google Scholar

[8] L. Galleani and L. Cohen, The Wigner distribution for classical system, Physics Letters A , Vol. pp.149-155, (2002).

Google Scholar

[9] G. Matz and F. Hlawatsch, Wigner distribution (nearly) everywhere: time-frequency analysis of signals, systems, random process, signal spaces, and frames, Signal Processing, Vol. 83, pp.1355-1378, (2003).

DOI: 10.1016/s0165-1684(03)00086-0

Google Scholar

[10] F. Hlawatsch and W. Kozek, The Wigner distribution of a linear signal space, IEEE transaction on signal process, Vol. 41 , pp.1248-1258, (1993).

DOI: 10.1109/78.205727

Google Scholar

[11] N . E Huang, Z. Shen and S.R. Long et al, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceeding of Royal Society London, Series A Vol. 454, pp.903-995, (1998).

DOI: 10.1098/rspa.1998.0193

Google Scholar

[12] N.E. Huang, Z. Shen, S.R. Long. A new view of nonlinear water waves: The Hilbert spectrum, , Annual Review of Fluid Mechanics, Vol. 31, pp.417-457, (1999).

DOI: 10.1146/annurev.fluid.31.1.417

Google Scholar