Although the morphology of wear debris generated in a machine has a direct relationship to wear processes and machine condition, studying wear particles for machine condition monitoring has not been widely applied in Industry as it is time consuming and requires certain expertise of analysts. To overcome these obstacles, automatic wear particle analysis and identification systems need to be developed. In this paper, laser scanning confocal microscopy has been used to obtain three-dimensional images of metallic wear particles. An analysis system has been developed and applied to study the boundary morphology and surface topography of the wear debris. After conducting the image analysis procedure and selecting critical criteria from dozens of available parameters, neural networks and grey systems have been investigated to classify unknown patterns using the numerical descriptors. It is demonstrated that the combination of the image analysis system and automatic classification systems has provided an automatic package for wear particle study which may be applied to industrial applications in the future.
Issue Section:
Research Papers
1.
Albidewi, A., et al., 1991, “Determination of Particle Shape by Image Analysis—The Basis for Developing an Expert System,” Condition Monitoring ’91: Proceedings of an International Conference on Condition Monitoring, pp. 411–435.
2.
Allred, L. G., and Kelly, G. E., 1990, “Supervised Learning Techniques for Backpropagation Networks,” Proceedings of Int. Joint Conf. on Neural Networks, pp. 721–728.
3.
Anamalay
R. V.
Kirk
T. B.
Panzera
D.
1995
, “Numerical Descriptors for the Analysis of Wear Surface Using Laser Scanning Confocal Microscopy
,” Wear
, Vol. 181–183
, pp. 111
–116
.4.
Bangchum, W., et al., 1989, “An Application of the Theory of Grey System to Fault Diagnosis of Rotating Machinery,” The 1989 ASME Design Technical Conference, 12th Biennial Conference on Mechanical Vibration and Noise, Montreal, Quebec, Canada, pp. 31–36.
5.
Beddow, J. B., 1984, Particle Characterization in Technology, Volume II, Morphological Analysis, Boca Raton, CRC Press.
6.
Cai
S.
Tong
G.
Gao
H.
1993
, Technical Note: “Grey System Theory Applied to Rock Mechanics
,” International Journal of Rock Mechanics and Mining Science and Geomechanics
, Vol. 30
, No. 4
, pp. 473
–478
.7.
Chowdhury, B. H., and Wang, K., 1996, “Fault Classification Using Kohonen Feature Mapping,” Proceedings of the International Conference on Intelligent Systems to Power Systems, ISAP, IEEE, pp. 194–198.
8.
Cliff, A. D., and Ord, J. K., 1973, Spatial Autocorrelation, Pion Limited.
9.
Delaney
P. M.
Harris
M. R.
King
R. G.
1994
, “Fiber-optic Laser Scanning Confocal Microscopy Suitable for Fluorescence Imaging
,” Applied Optics
, Vol. 33
, No. 4
, pp. 573
–577
.10.
Deng, J. L., 1987, The Theory of Grey Systems, Huazhong University of Science and Technology, China.
11.
Ding, G. J., Kuhnell, B. T., and Li, X. Q., 1992, “WEAREX PLUS: A Computer-Aided Wear Debris Analysis System,” The Bulletin of the CMCM Monash, Monash University, Vol. 2, No. 1, pp. 81–86.
12.
Dong
W. P.
Stout
K. J.
1995
, “Two-Dimensional Fast Fourier Transform and Power Spectrum for Surface Roughness in Three Dimensions
,” Proc. Instn. Mech. Engrs.
, Vol. 209
, pp. 381
–391
.13.
Fakhr, W., and Elmasry, M. I., 1990, “A Fast Learning Technique for the Multilayer Perceptron,” IJCNN, International Joint Conference on Neural Networks, IEEE, pp. 257–262.
14.
Hathaway
R. J.
Bezdek
J. C.
1994
, “NERF C-Means: Non-Euclidean Relational Fuzzy Clustering
,” Pattern Recognition
, Vol. 27
, No. 3
, pp. 429
–437
.15.
Huang
Y.
Wang
S.
1997
, “The Identification of Fuzzy Grey Prediction system by Genetic Algorithms
,” International Journal of Systems Science
, Vol. 28
, No. 1
, pp. 15
–24
.16.
Hush, D. R., and Horne, B. G., 1993, “Progress in Supervised Neural Networks,” IEEE Signal Processing Magazine, Jan., pp. 8–39.
17.
Kirk, T. B., 1994, “Numerical Characterisation of Wear Debris for Machine Condition Monitoring,” International Tribology Conference, AUSTRIB’94, Perth, Australia, 5-8 Dec, pp. 803–807.
18.
Kirk
T. B.
Stachowiak
G. W.
Batchelor
A. W.
1991
, “Fractal Parameters and Computer Image Analysis Applied to Wear Particles Isolated by Ferrography
,” Wear
, Vol. 145
, pp. 347
–365
.19.
Kosko, B., 1992, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ.
20.
Larsson
J. E.
Persson
P.
1991
, “An Expert System Interface for an Identification Program
,” Automatica
, Vol. 27
, No. 6
, pp. 919
–930
.21.
Luo
M.
Kuhnell
B. T.
1991
, “Forecasting Machine Condition Using Grey-System Theory
,” Condition Monitoring and Diagnostic Technology
, Vol. 1
, No. 3
, pp. 102
–105
.22.
Lyman, C. E. et al., 1990, Scanning Electron Microscopy, X-Ray Microanalysis, and Analytical Electron Microscopy, Plenum Press, New York.
23.
Myshkin
N. K.
1997
, “Classification of Wear Debris Using a Neural Network
,” Wear
, Vol. 203–204
, pp. 658
–662
.24.
Naval Air Engineering Center, 1982, Wear Particle Atlas, Report NAEC-92-163.
25.
Nigrin, A., 1993, Neural Networks for Pattern Recognition, Massachusetts Institute of Technology.
26.
Peng
Z.
Kirk
T. B.
Xu
Z. L.
1997
a, “The Development of Three Dimensional Imaging Techniques of Wear Particle Analysis
,” Wear
, Vol. 203–204
, pp. 418
–424
.27.
Peng
Z.
Kirk
T. B.
1997
b, “2-Dimensional Fast Fourier Transform and Power Spectrum for Wear Particle Analysis
,” Tribology International
, Vol. 30
, No. 8
, pp. 583
–590
.28.
Peng, Z., and Kirk, T. B., 1997c, “Numerical Characterisation of Surface Textures of Wear Particles for Machine Condition Monitoring,” presented on (CM)2 Forum, Queensland, Australia, 1–3 Oct.
29.
Peng, Z., and Kirk, T. B., 1997d, “Computer Image Analysis of Wear Particles in Three-Dimensions for Machine Condition Monitoring,” Wear (submitted).
30.
Podsiadlo
P.
Stachowiak
G. W.
1997
, “Characterization of Surface Topography of Wear Particles by SEM Stereoscopy
,” Wear
, Vol. 206
, pp. 39
–52
.31.
Russ, J. C., 1990, Computer-Assisted Microscopy: The Measurement and Analysis of Images, Plenum Press.
32.
Sherrington
I.
Smith
E. H.
1988
, “Fourier Models of the Surface Topography of Engineering Components
,” Surface Topography
, Vol. 1
, pp. 11
–25
.33.
Stachowiak, G. W., 1997, “Numerical Characterization of Wear Particle Morphology and Angularity of Particles and Surfaces,” The First World Tribology Congress, 8–12 Sept., 1991, London, pp. 371–389.
34.
Stevens, J. K., Mills, L. R., and Trogadis, J. E., Three-Dimensional Confocal Microscopy: Volume Investigation of Biological Systems, Academic Press, 1994.
35.
Stout, K. J., et al., 1994, “The Development of Methods for the Characterisation of Roughness in 3 Dimensions,” Commission of the European Communities.
36.
Thomas
A. D. H.
Davies
T.
Luxmoore
A. R.
1991
, “Computer Image Analysis for Identification of Wear Particles
,” Wear
, Vol. 142
, pp. 213
–226
.37.
Tsao
E. C.
Bezdek
J. C.
Pal
N. R.
1994
, “Fuzzy Kohonen Clustering Networks
,” Pattern Recognition
, Vol. 27
, No. 5
, pp. 754
–764
.38.
Xu, Z. L., 1996, “Three Dimensional Analysis of Wear Particle Morphology,” Master thesis, Monash University.
This content is only available via PDF.
Copyright © 1999
by The American Society of Mechanical Engineers
You do not currently have access to this content.
