• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.83-90
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• 1998

The morphologies of the wear debris are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify friction condition from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction coefficient. It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss between the characteristic of wear debris and the friction coefficient and how the network determines difference in wear debris feature.

• Tribology and Lubricants
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• v.11 no.3
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.133-140
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• 1998

The neural network was applied to identify wear debris generated from the lubricated machine surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes, the four shape parameter(50％ volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the friction condition and materials very well by neural network.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.7
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• pp.1044-1051
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• 2009

During contact between surfaces, there is wear and the generation of wear. The particles contained in the lubricating oil carry detailed and important monitoring information about the condition of the machine. Therefore, this paper was undertaken for the Ferrography system of wear debris generated from a lubricated moving machine surface. The lubricating wear test was performed under different experimental conditions using the Falex wear test of the Pin and V-Block types by Ti(C,N) coated. It was shown from the test results that wear particle concentration(WPC), wear severity index(Is) and size distribution have come out all higher with increases in sliding friction time. With the Ferrogram thin leaf wear debris as well as ball and plate type wear particles were observed.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.10a
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• pp.135-144
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• 1997

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.6
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• pp.44-49
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• 2003

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.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.62-67
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• 2000

This paper was undertaken to do morphological analysis of wear debris for slipper-Pad of hydraulic rotary acuator. The lubricating wear test was performed under different experimental conditions using the wear test device and wear specimens of the pin on disk type was rubbed in paraffinic base oil by three kinds of lubricating materials, varying applied load, sliding distance. The four shape parameters(50% volumetric diameter, aspect, roundness and reflectivity) are used for morphological analysis of wear debris. The results showed that the four shape parameters of wear debris depend on a kind of the lubricating condition. It was capable of presuming wear volume for slipper-pad of hydraulic rotary acuator on driving time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.855-859
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• 2005

Carbon fiber polymeric composites have been widely used in bearing materials under high pressure without oil-lubrication due to their self-lubricating characteristics. However, the severe wear of carbon composite surface occurs due to the generation of wear debris when the pressure applied on the composite surface is higher than the critical value of composite surface. In this work, in order to remove wear debris continuously during sliding operation, composite specimens with many micro-grooves on their sliding surfaces were devised. To investigate the effect of wear debris on the tribological behavior of carbon/epoxy composites, dry sliding tests were performed with respect to applied pressure using the composite specimens with and without micro-grooves. From the measurement of friction coefficients and wear rates, a model for the effect of wear debris on the friction and wear of composites was proposed.