Building Relationship Network for Machine Analysis from Wear Debris Measurements

Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear debris analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self-organizing maps. This is achieved using relationship measurements among corresponding attributes of various measurements for wear debris. Finally, visualization technique is proposed that helps the viewer in understanding and utilizing these relationships that enable accurate diagnostics.

Authors:

• Qurban A Memon
• Mohammad S. Laghari

Keywords:

• Relationship Network
• Relationship Measurement
• Self-organizing Clusters
• Wear Debris Analysis
• Kohonen Network

References:

[1] B. J. Roylance and T. M. Hunt, Wear Debris Analysis, Coxmoor
Publishing, Oxford, 1999.
[2] H. P. Jost, "Tribology - Origin and Future," in Wear, vol. 139, 1990, pp.
1-17.
[3] T. M. Hunt, Handbook of Wear Debris Analysis and Particle Detection
in Fluids, Elsevier Science, London, New York, 1993.
[4] T. P. Sperring, B. J. Roylance, "Some recent development in the use of
quantitative procedures for performing wear debris analysis," JOAP
International Condition Monitoring Conference, Mobile, Al., 2000, pp.
205-210.
[5] G. A. Khuwaja, M. S. Laghari, "Computer vision techniques for wear
debris analysis," in International Journal of Computer Applications in
Technology, Vol. 15, No. 1/2/3, 2002, pp. 70-78.
[6] Wang J., Chen D., Kong X., "A web based remote intelligent expert
system for Ferrography diagnosis", Key Engineering materials Vols.
245-246 (2003), pp. 367-372.
[7] W. W. Seifert, V. C. Westcott, "A method for the study of wear debris in
lubricated oil," in Wear, Vol. 21, 1972, pp. 27-42.
[8] Valdis Krebs, "Introduction to Social Network Analysis",
http://www.orgnet.com/sna.html; (Accessed 15 January 2005).
[9] M. S. Laghari, A. Boujarwah, "Wear particle identification using image
processing techniques," in ISCA 5th Int. Conf. on Intelligent Systems,
Reno, Nevada, 1996, pp. 26-30.
[10] Leica Cambridge Ltd., Leica Q500MC Qwin User Manual, Leica
Cambridge Ltd., U.K., 1994.
[11] H. Xu, A. R. Luxmoore and F. Deravi, "Comparison of shape features
for the classification of wear debris," in Engineering Applications of
Artificial Intelligence, vol. 10, no. 5, 1997, pp. 485-493.
[12] S. Raadnui, Wear Particle Characterization Utilizing Computer Image
Analysis, Ph.D. Thesis, Dept. Mech. Eng., University of Wales,
Swansea, 1996.
[13] B. J. Roylance, "Wear debris analysis for condition monitoring," in
INSIGHT 36, Vol. 8, 1994, pp. 606- 610.
[14] M. S. Laghari, "Recognition of texture types of wear debris," in Int. J. of
Neural Comp. & Applications, vol. 12, 2003, pp. 18-25.
[15] D. Hammerstrom, "Neural networks at Work", IEEE Spectrum, pp. 26-
32, June 1993.
[16] S. Przylucki, W. Wojcik, K. Plachecki, T. Golec, "An analysis of selforganization
process for data classification in multisensor systems",
Proceedings of SPIE Vol. 5124, September 2003, pp. 325-332.
[17] T. Otto, A. Meyer-Baese, M. Hurdal, D. Sumners, D. Auer, A.
Wismuller, "Model-free functional MRI analysis using cluster-based
methods", Proceedings of SPIE Vol. 5103, August 2003, p. 17-24.
[18] Aleksander et all, An Introduction to Neural Computing, Chapman and
Hall 1990.
[19] M. Smith, P. King, "Incrementally Visualizing Criminal Networks",
Proceedings of International Conference on Information Visualization,
pp. 76-81, London, 2002.