Self Organizing Analysis Platform for Wear Particle
Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
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 particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
[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 and 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 and M. S. Laghari, "Computer vision techniques for wear
debris analysis," in Int. J. 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 and V. C. Westcott, "A method for the study of wear
particles in lubricated oil," in Wear, vol. 21, 1972, pp. 27-42.
[8] T. P. Sperring and 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.
[9] M. S. Laghari and A. Boujarwah, "Wear particle identification using
image processing techniques," in ISCA 5th Int. Conf. on Intelligent
Systems, Reno, Nevada, 1996, pp. 26-30.
[10] Valdis Krebs, "Introduction to Social Network Analysis",
http://www.orgnet.com/sna.html (Accessed 15 January 2005).
[11] Leica Cambridge Ltd., Leica Q500MC Qwin User Manual, Leica
Cambridge Ltd., U.K., 1994.
[12] H. Xu, A. R. Luxmoore and F. Deravi, "Comparison of shape features for
the classification of wear particles," in Engineering Applications of
Artificial Intelligence, vol. 10, no. 5, 1997, pp. 485-493.
[13] S. Raadnui, Wear Particle Characterization Utilizing Computer Image
Analysis, Ph.D. Thesis, Dept. Mech. Eng., University of Wales, Swansea,
1996.
[14] B. J. Roylance, "Wear debris analysis for condition monitoring," in
INSIGHT 36, vol. 8, 1994, pp. 606-610.
[15] M. S. Laghari, "Recognition of texture types of wear particles," in Int. J.
of Neural Comp. & Applications, vol. 12, 2003, pp. 18-25.
[16] D. Hammerstrom, "Neural networks at Work", IEEE Spectrum, pp. 26-
32, June 1993.
[17] 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.
[18] 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.
[19] Aleksander et all, An Introduction to Neural Computing, Chapman and
Hall 1990.
[20] M. Smith, P. King, "Incrementally Visualizing Criminal Networks",
Proceedings of International Conference on Information Visualization,
pp. 76-81, London, 2002.
[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 and 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 and M. S. Laghari, "Computer vision techniques for wear
debris analysis," in Int. J. 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 and V. C. Westcott, "A method for the study of wear
particles in lubricated oil," in Wear, vol. 21, 1972, pp. 27-42.
[8] T. P. Sperring and 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.
[9] M. S. Laghari and A. Boujarwah, "Wear particle identification using
image processing techniques," in ISCA 5th Int. Conf. on Intelligent
Systems, Reno, Nevada, 1996, pp. 26-30.
[10] Valdis Krebs, "Introduction to Social Network Analysis",
http://www.orgnet.com/sna.html (Accessed 15 January 2005).
[11] Leica Cambridge Ltd., Leica Q500MC Qwin User Manual, Leica
Cambridge Ltd., U.K., 1994.
[12] H. Xu, A. R. Luxmoore and F. Deravi, "Comparison of shape features for
the classification of wear particles," in Engineering Applications of
Artificial Intelligence, vol. 10, no. 5, 1997, pp. 485-493.
[13] S. Raadnui, Wear Particle Characterization Utilizing Computer Image
Analysis, Ph.D. Thesis, Dept. Mech. Eng., University of Wales, Swansea,
1996.
[14] B. J. Roylance, "Wear debris analysis for condition monitoring," in
INSIGHT 36, vol. 8, 1994, pp. 606-610.
[15] M. S. Laghari, "Recognition of texture types of wear particles," in Int. J.
of Neural Comp. & Applications, vol. 12, 2003, pp. 18-25.
[16] D. Hammerstrom, "Neural networks at Work", IEEE Spectrum, pp. 26-
32, June 1993.
[17] 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.
[18] 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.
[19] Aleksander et all, An Introduction to Neural Computing, Chapman and
Hall 1990.
[20] M. Smith, P. King, "Incrementally Visualizing Criminal Networks",
Proceedings of International Conference on Information Visualization,
pp. 76-81, London, 2002.
@article{"International Journal of Information, Control and Computer Sciences:49207", author = "Qurban A. Memon and Mohammad S. Laghari", title = "Self Organizing Analysis Platform for Wear Particle", abstract = "Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
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 particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.", keywords = "Neural Network, Relationship Measurement, Selforganizing Clusters, Wear Particle Analysis.", volume = "1", number = "6", pages = "1513-4", }