• Journal of Advanced Marine Engineering and Technology
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• v.23 no.1
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• pp.79-87
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• 1999

The most part of the faculty drop a trouble and damage of machine equipment even if whatever cause they break out take place at local and trifling place and the factor dominating their trouble is due to wear debris occurred in the lubricated machine moving surface. This study has been car-ried out to identify morphology of wear debris on the lubricated machine moving system by means of computer image analysis. Namely the wear debris contained in lubricating oil extracted from movable machine equipment will be filtered through membrane filter(void diameter 0.45${\mu}m$) and will be analyzed with its data information such as 50% volume diameter aspect roundness and reflectivity. Morphological characteristic of wear debris is easily distinguished by four shape parameters it is necessary to divide small class of every 100 wear debris in total wear particles in order to distinguish morphological characteristic of wear debris more easily by computer image analysis. We are sure that operation condition diagnosis of the lubricated machine moving surfaces is possible by computer image analysis.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.136-142
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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.

• Tribology and Lubricants
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• v.20 no.1
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• pp.21-26
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• 2004

The average frictional coefficient was used generally to analyze the working process of the lubricated machine parts. But it is difficult of taking the correct frictional coefficient because the average frictional coefficient of it is progressed always unstably with a large amplitude on working condition. If the correct analysis of frictional coefficient on working condition for the lubricated machine parts can be possible, it can be effect on condition diagnosis of lubricated machine system. This study was carried out to get the optimum working condition of the hydraulic actuator parts using statistical techniques. From this result, it has the lowest friction coefficient in case of applied load of 3 kg and sliding speed of 120 mm/sec.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2003.11a
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• pp.307-312
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• 2003

The average frictional coefficient was used generally to analyze the moving state of lubricated machine. But It is difficult of getting the correct friction coefficient because the average frictional coefficient of it is progressed always unstably with a large amplitute on driving condition. If correct analysis of frictional coefficient on working condition for lubricated machine can be possible, it can be effect on diagnosis of lubricated machine. The purpose of this study is carried out to get the working condition with a minimum frictional coefficient of transmission gear materials using statistical techniques.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2004.11a
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• pp.316-322
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• 2004

Wear volume was used generally to analyze the moving state of lubricated machine. But It is difficult of getting the correct wear volume because wear volume of it is progressed always unstably with a large amplitude on working condition. If correct analysis of wear volume on working condition for lubricated machine can be possible, it can be effect on diagnosis of failure condition. The purpose of this study is carried out to analysis friction factors affecting on wear volume for prediction of failure condition of alloy steel for machine structural use by design of experiment. The results show that the most important friction factors affecting on wear volume was applied load, neat sliding distance, sliding speed and materials.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.369-370
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• 2002

To clarify the tribological properties of biodegradable lubricating oils, the four-ball tests were carried out under dip-feed lubrication using a Soda-type four-ball machine. The test balls were lubricated with soybean oil, rapeseed oil, corn oil and turbine oil. From the tests, the coefficient of friction for all the test balls lubricated with biodegradable lubricating oils was lower than that for the test ball lubricated with turbine oil. Further, from the calculation of the pV value, it was clear that the seizure resistance for all the test balls lubricated with biodegradable lubricating oils was higher than that for the test ball lubricated with turbine oil.

• KSTLE International Journal
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• v.3 no.1
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• pp.54-59
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• 2002

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.

• 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.

• Tribology and Lubricants
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• v.12 no.3
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• pp.72-78
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• 1996

This paper was undertaken to analyze the morphology of wear debris generated from lubricated moving machine surfaces by image processing. The lubricati, ng wear test was performed under different experimental conditions using the wear test device made in our laboratory and wear test specimen of the pin on disk type wear rubbed in paraffme series base oil, by varying applied load, sliding distance. The four parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe the morphology have been developed and outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus to overcome many of the difficulties with current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.563-567
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• 1996

This paper was undertaken to analyze the morphology of wear debris generating from moving lubricated machine surfaces by image processing. The lubricating wear test was carried out under different experimentaal conditions using the wear test device was made in our laboritory and wear testing specimen of the pin on disk type wear rubbed in paraffine series base oil, byvarying applied load, sliding distance. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) to describe the morphology have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus overcoming many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitoring.