Study on Method to Extract Feature Parameter of Wear Particle Group in Ferrographic Image for the Diesel Engine Lubricants

Hong Zhi Wang; Guo Bin Li; De Lin Guan

doi:10.4028/www.scientific.net/AMM.55-57.1530

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 55-57Study on Method to Extract Feature Parameter of...

Study on Method to Extract Feature Parameter of Wear Particle Group in Ferrographic Image for the Diesel Engine Lubricants

Abstract:

According to the consistency between multi-scale decompositions and self-similarity in both wavelet transform and fractal theory, a new method has been developed to extract the feature parameter of wear particle group on the ferrographic image for diesel engine lubricants. The algorithm of minutiae extraction have been carried out by wavelet transform approach and the fractal dimension, and then the feature parameter can be obtained for the wear particle group on the ferrographic image. The fractal dimension D reflects the ferrographic image character in the scale and the amount of wear particle group, which can be used as a comprehensive feature parameter. The metal-ceramic nano-lubricant, which has been applied in the wear test of cylinder and piston-ring material from MAN B&WS50MC marine diesel engine, represent that the wear is speedup suitably, and then the fractal dimension D has consistency with the results of ferrographic analysis.

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Periodical:

Applied Mechanics and Materials (Volumes 55-57)

Pages:

1530-1534

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.55-57.1530

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Online since:

May 2011

Authors:

Hong Zhi Wang, Guo Bin Li, De Lin Guan

Keywords:

Diesel Engine, Ferrographic Analysis, Fractal Theory, Lubricant, Wavelet Transform (WT), Wear Particle Group

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References

[1] P. Podsiadlo and G.W. Stachowiak: Scale-invariant analysis of wear particle surface morphology II. Fractal dimension. Wear . Vol. 242(2000), pp.180-188.

DOI: 10.1016/s0043-1648(00)00417-8

Google Scholar

[2] . Podsiadlo and G.W. Stachowiak: Scale-invariant analysis of wear particle surface morphology III. Pattern recognition. Wear . Vol. 242 (2000), pp.189-201.

DOI: 10.1016/s0043-1648(00)00418-x

Google Scholar

[3] B.B. Mandelbrot: The Fractal Geometry of Nature. New York: Freeman, 1982, 123~128.

Google Scholar

[4] J. Lopez：3D fractal-based characterization for engineering surface topography. International Journal of Machine Tools and Manufacture, Vol. 35（1995）, p.211~218.

Google Scholar

[5] S. Mallat and W.L. Hwang: singularity Detection and Processing with Wavelet. IEEE Trans on IT. Vol. 38(2) (1992), p.617~643.

Google Scholar

[6] Keesok J. Han and Ahmed H. Tewfik. Hybrid Wavelet Transform Filter for Image Recovery. IEEE Transcation on Image Processing. Vol. 1 (1998), p.540~544.

DOI: 10.1109/icip.1998.723558

Google Scholar

[7] X. C, ZHOU, H.L. XIAO and X.P. YAN: A New Comprehensive Feature Parameter for Wear Particle Image Analysis. Tribolgy. Vol. 22(2) (2002), p.138~141.

Google Scholar

[8] X. B Li, J. Ding and H.Q. Li: The wavelet estimation of Hurst coefficient in hydrological timeseries. Journal of Hydraulic Engineering. Vol. 24(8) (1999), p.21~25.

Google Scholar

[9] N. Jorge,O. Xavier: Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans on Geoscience and Remote Sensing. Vol. 4(1999), p.1204~1211.

DOI: 10.1109/36.763274

Google Scholar

[10] Z.Y. Lu,X.P. Yan and Y.F. Peng: Analysis of Typic Wear Particles based on Principal Component Classification. Tribology. Vol. 28(5) (2008), pp.453-456.

Google Scholar