[KSCI] Korea Science Citation Index Service

A Study on Recognition of Friction Condition for Hydraulic Driving Members using Neural Network

Park, Heung-Sik (Faculty of Mechanical Industrial system Engineering)
Seo, Young-Baek (Faculty of Mechanical Industrial system Engineering)
Kim, Dong-Ho (Computer Aided Mechanical Engineering)
Kang, In-Hyuk (Graduate School, Department of Mechanical Engineering)

Publication Information

KSTLE International Journal / v.3, no.1, 2002 , pp. 54-59 More about this Journal

Abstract

It can be effective on failure diagnosis of oil-lubricated tribological system to analyze operating conditions with morphological characteristics of wear debris in a lubricated machine. And it can be recognized that results are processed threshold images of wear debris. But it is needed to analyse and identify a morphology of wear debris in order to predict and estimate a operating condition of the lubricated machine. If the morphological characteristics of wear debris are identified by the computer image analysis and the neural network, it is possible to recognize the friction condition. In this study, wear debris in the lubricating oil are extracted from membrane filter (0.45 ${\mu}m$ ) and the quantitative value fur shape parameters of wear debris was calculated through the computer image processing. Four shape parameters were investigated and friction condition was recognized very well by the neural network.

Keywords

Wear debris; shape parameters; computer image analysis; neural network;

Citations & Related Records

Reference

1	H. S. Park, 'Wear Debhs Analysis of the Machine Lubricating Surface by the Neural Network,' KSTLE, Vol. 11, No. 3,pp. 24-30, 1995
2	T. M. Hunt, 'Condition Monitoring of Mechanical and Plant,'Chapman & Hall, pp. 48-53, 1995
3	J. Sugimura and Y. Yamamoto, 'Wear Debris Identification with Neural Network,' JSME (C), Vol. 61, No. 509, pp.4055-4060, 1995 DOI
4	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
5	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
6	W. Uedelhoven and M. Franzl, 'The Use of AutomatedImage Analysis for the Study of Wear Particles in OilLubricated Tribological System,' Wear, 142, pp. 107-113, 1991 DOI ScienceOn