• Title, Summary, Keyword: Wear Debris

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A study on automatic wear debris recognition by using particle feature extraction (입자 유형별 형상추출에 의한 마모입자 자동인식에 관한 연구)

  • ;;;Grigoriev, A.Y.
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • pp.314-320
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    • 1998
  • Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

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A Study on Automatic wear Debris Recognition by using Particle Feature Extraction (입자 유형별 형상추출에 의한 마모입자 자동인식에 관한 연구)

  • ;;;A. Y. Grigoriev
    • Tribology and Lubricants
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    • v.15 no.2
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    • pp.206-211
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    • 1999
  • Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

A Study on Statistical Classification of Wear Debris Morphology

  • Cho, Unchung
    • KSTLE International Journal
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    • v.2 no.1
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    • pp.35-39
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    • 2001
  • In this paper, statistical approach is undertaken to investigate the classification of wear debris which is the key function of objective assessment of wear debris morphology. Wear tests are run to produce various kinds of wear debris. The images of wear debris from wear tests are captured with image acquisition equipment. By thresholding, two-dimensional binary images of wear debris are made and, then, morphological parameters are used to quantify the images of debris. Parametric and nonparametric discriminant method are employed to classify wear debris into predefined wear conditions. It is demonstrated that classification accuracy of parametric and nonparametric discriminant method is similar. The selected use of morphological parameters by stepwise discriminant analysis can generally improve the classification accuracy of parametric and nonparametric discriminant method.

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A Study on Friction Coefficient Prediction of Hydraulic Driving Members by Neural Network (신경회로망에 의한 유압구동 부재의 마찰계수 추정 에 관한 연구)

  • 김동호
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.12 no.5
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    • pp.53-58
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    • 2003
  • Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters 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 resented how the neural network recognize wear debris on driving condition.

Study of Identification of Lubricant Condition for Hydraulic Member (유압구동 부재의 마찰 상태 식별에 관한 연구)

  • Gang, In-Hyeok;Ryu, Mi-Ra;Park, Jae-Sang;Park, Heung-Sik
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • pp.193-199
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    • 2002
  • Analyzing working conditions with shape characteristics of wear debris in a lubricated machine, it can be effect on diagnosis of hydraulic machining system. And it can be recognized that results are processed threshold images of wear debris. But, in order to predict and estimate a working condition of lubricated machine, it is need to analysis a shape characteristic of wear debris and to identify. Therefor, If shape characteristics of wear debris are identified by computer image analysis and the neural network, it is possible to find the cause and effect of wear condition. In this stud)r, wear debris in the lubricant oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative value of shape characteristic of wear debris are calculated by the digital image processing. This morphological information are studied and identified by tile artificial neural network. The purpose of this study is to apply morphological characteristic of wear debris to prediction and estimation of working condition in hydraulic machining systems.

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Application of Neural Network to Prediction and estimation of Rolling Condition for Hydraulic members (유압구동부재의 구름운동상태 예지 및 판정을 위한 신경 회로망의 적용)

  • 조연상;김동호;박흥식;전태옥
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.646-649
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    • 2002
  • It can be effect on diagnosis of hydraulic machining system to analyze working conditions with shape characteristics of wear debris in a lubricated machine. But, in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefor, if shape characteristics of wear debris is identified by computer image analysis and the neural network, it is possible to find the cause and effect of moving condition. In this study, wear debris in the lubricant oil are extracted by membrane filter, and the quantitative value of shape characteristics of wear debris we calculated by the digital image processing. This morphological informations are studied and identified by the artificial neural network. The purpose of this study is In apply morphological characteristics of wear debris to prediction and estimation of working condition in hydraulic driving systems.

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

  • Park, Heung-Sik;Seo, Young-Baek;Kim, Dong-Ho;Kang, In-Hyuk
    • 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.

Shape Study of Wear Debris in Oil-Lubricated System with Neural Network

  • Park, Heung-Sik;Seo, Young-Baek;Cho, Yon-Sang
    • KSTLE International Journal
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    • v.2 no.1
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    • pp.65-70
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    • 2001
  • The wear debris is fall off the moving surfaces in oil-lubricated systems and its morphology is directly related to the damage and failure to the interacting surfaces. The morphology of the wear particles are therefore directly indicative of wear processes occurring in tribological system. The computer image processing and artificial neural network was applied to shape study and identify wear debris generated from the lubricated moving system. In order to describe the characteristics of various wear particles, four representative parameter (50% volumetric diameter, aspect, roundness and reflectivity) from computer image analysis for groups of randomly sampled wear particles, 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 parameters 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. We discuss how these approach can be applied to condition diagnosis of the oil-lubricated tribological system.

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Identification of Friction Condition with Neural Network (신경회로망에 의한 마찰상태의 식별)

  • 조연상;서영백;박흥식;전태옥
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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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.

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Quantitative Analysis of Wear Debris for Surface Modification Layer by Ferrography (Ferrography에 의한 표면개질층의 마모분 정량분석)

  • 오성모;이봉구
    • Tribology and Lubricants
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    • v.15 no.3
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    • pp.265-271
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    • 1999
  • Wherever there are rotating equipment and contact between surface, there is wear and the generation of wear particles. The particles contained in the lubricating oil carry detailed and important information about the condition monitoring of the machine. This information may be deduced from particle shape, composition, size distribution, and concentration. Therefore, This paper was undertaken to Ferrography system of wear debris generated from lubricated moving machine surface. The lubricating wear test was performed under different experimental conditions using the Falex wear test of Pin and V-Block type by Ti(C, N) coated. It was shown from the test results that wear particle concentration (WPC) and wear severity Index( $I_{S}$), size distribution in normal and abnormal wear have come out all the higher value by increases sliding friction time. Wear shape is observed on the Ferrogram it was discovered a thin leaf wear debris as well as ball and plate type wear particles. This kind of large wear shape have an important effect not only metals damage, but also seizure phenomenon.