• Title/Summary/Keyword: Machine Condition Diagnosis

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A Study on the Correlation of Condition Monitoring Parameters of Functional Machine Failures. (기계시스템 파손에 따른 상태진단 파라미터의 상관관계 해석에 관한 연구)

  • 장래혁;강기홍;공호성;최동훈
    • Tribology and Lubricants
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    • v.18 no.4
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    • pp.285-290
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    • 2002
  • Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

A Study on the Correlation of Condition Monitoring Parameters of Functional Machine Failures. (기계시스템 파손에 따른 상태진단 파라미터의 상관관계 해석에 관한 연구)

  • 장래혁;강기홍;공호성;최동훈
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • 2001.11a
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    • pp.252-259
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    • 2001
  • Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

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Adaptive Maintenance Using Machine Condition Diagnosis Technique (설비진단기술를 활용한 적응보전)

  • 송원섭;강인선
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.17 no.30
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    • pp.73-79
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    • 1994
  • This paper propose Adaptive Maintenance as a new type of maintenance for machine failures which are unpredictable. A purpose of adpative maintenance is to decrease inconsistency. In order to pick up some of problems the traditional maintenance policy, We discussed Time Based Maintenance(TBM) and Condition Based Maintenance(CBM) with Bath-Tub Curve. By using Machine Condition Diagnosis Technique (CDT), Monitored condition maintenance deals with the dynamic decision making for diagnosis procedures at maintenance and caution level. Adaptive Maintenance is a powerful tool for Total Production Maintenance(TPM).

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Analysis of Wear Debris for Machine Condition Diagnosis of the Lubricated Moving Surface (기계윤활 운동면의 작동상태 진단을 위한 마멸분 해석)

  • Seo, Yeong-Baek;Park, Heung-Sik;Jeon, Tae-Ok
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.21 no.5
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    • pp.835-841
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    • 1997
  • Microscopic examination of the morphology of wear debris is an accepted method for machine condition and fault diagnosis. However wear particle analysis has not been widely accepted in industry because it is dependent on expert interpretation of particle morphology and subjective assessment criteria. This paper was undertaken to analyze the morphology of wear debris for machine condition diagnosis of the lubricated moving surfaces by image processing and analysis. The lubricating wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in paraffine series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) 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 in current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model (결함 데이터를 필요로 하지 않는 연속 은닉 마르코프 모델을 이용한 새로운 기계상태 진단 기법)

  • Lee, Jong-Min;Hwang, Yo-Ha
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.21 no.2
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    • pp.146-153
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    • 2011
  • Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

Applicaion of Neural Network for Machine Condition Monitoring and Fault Diagnosis (기계구동계의 손상상태 모니터링을 위한 신경회로망의 적용)

  • 박흥식;서영백;조연상
    • Tribology and Lubricants
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    • v.14 no.3
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    • pp.74-80
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    • 1998
  • The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

설비진단기술을 이용한 CBM 활용에 관한 연구

  • Gang In-Seon
    • Proceedings of the Society of Korea Industrial and System Engineering Conference
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    • 2002.05a
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    • pp.403-412
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    • 2002
  • Machine condition diagnosis is the technique to perceive the machine errors and the abrasion online without overhaul. We need the steps to predict the life span and reliability of a machine for the abrasion as with perceiving the degree of the abrasion of certain machine parts to make errors. In this study we deals with the methods to check and manage periodically and to configure the judgement criteria for the state of a machine. For the applications of CDT(Condition Diagnosis Technique) we also suggest the methods to check comparing the measured vibration values with the absolute criteria and to check the abnormality by vibration level.

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Implementation of an Integrated Machine Condition Monitoring Algorithm Based on an Expert System (전문가시스템을 기반으로 한 통합기계상태진단 알고리즘의 구현(I))

  • 장래혁;윤의성;공호성;최동훈
    • Tribology and Lubricants
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    • v.18 no.2
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    • pp.117-126
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    • 2002
  • Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

Defect Identification through Frequency Analysis of Vibration -In Case of Rotary Machine_ (진동의 주파수분석을 통한 결함 식별 - 회전기계를 중심으로-)

  • Jeong, Yoon-Seong;Wang, Gi-Nam;Kim, Gwang-Sub
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.11
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    • pp.82-90
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    • 1995
  • This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.

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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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