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A Study on Recognition of Operating Condition for Hydraulic Driving Members

Park, Heung-Sik (Division of Mechanical, Industrial System Engineering, Dong-A University)
Kim, Young-Hee (Division of Metallurgical and Materials, Chemical Engineering, Dong-A University)
Kim, Dong-Ho (Graduate School, Department of Mechanical Engineering, Dong-A University)
Cho, Yon-Sang (Graduate School, Department of Mechanical Engineering, Dong-A University)
Park, Jae-Sang (Graduate School, Department of Mechanical Engineering, Dong-A University)

Publication Information

International Journal of Precision Engineering and Manufacturing / v.4, no.6, 2003 , pp. 44-49 More about this Journal

Abstract

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45 $\mu\textrm{m}$ ), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

Keywords

Wear debris; Image processing; Shape parameters; Computer image analysis; Neural network;

Citations & Related Records

Reference

1	B. J. Roylance, I. A. Albidewi and M. S. Laghari, 'Computer-Aided Vision Engineering (CAVE)- Quantification of Wear Particle Morphology,' Lubr. Eng, 50, pp. 111-116, 1993
2	T. Sato, O. Ikeda, T. Hatsuzawa and M. Linzer, 'Real Time Evaluation of Wear Particle using Electro Forced Rotation and Laser Scattering,' Wear, Vol.15, pp.273-285, 1987
3	J. Sugimura and Y. Yamamoto, 'Wear Debris Identification with Neural Network,' JSME(C), Vol.61, No.509, pp.4055-4060, 1995 DOI
4	Lee, S. S. 'Development of In-process Condition Monitoring System on Turning Process using Artificial Neural Network.,' KSMTE, Vol. 7, No. 3, pp.14-21, 1998
5	Park, H. S. 'Wear Debris Analysis of the Machine Lubricating Surface by the Neural Network,' KSTLE, Vol. 11, No.3, pp.24-30, 1995
6	T. M. Hunt, 'Condition Monitoring of Mechanical and Plant,' Chapman & Hall, pp.48-53, 1995
7	Seo, Y. B., Park, H. S., Jun, T. 0. and Lee, K. Y. 'Image Analysis of Wear Debris on Operating Condition of the Lubricated Moving Surface,' J. of KSPE, Vol. 14, No. 5, pp.143-149, 1997
8	W. Uedelhoven and M. Franzl, 'The Use of Automated Image Analysis for the Study of Wear Particles in OiI-Lubricated Tribological System, Wear, 142, pp.107-113, 1991 DOI ScienceOn