• 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)

  • ;;;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 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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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 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.

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 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.

Operating Condition Diagnosis of the Lubricated Machine Moving Surface by Image Analysis (화상해석에 의한 기계윤할 운동면의 작동상태 진단)

  • 박흥식
    • 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.

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A Study on Recognition of Operating Condition for Hydraulic Driving Members (유압구동 부재의 작동조건 식별에 관한 연구)

  • 조연상;류미라;김동호;박흥식
    • 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.

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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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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