• Title/Summary/Keyword: FDCC

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The Fault Diagnosis Method of Diesel Engines Using a Statistical Analysis Method (통계적 분석기법을 이용한 디젤기관의 고장진단 방법에 관한 연구)

  • Kim, Young-Il;Oh, Hyun-Kyung;Yu, Yung-Ho
    • Journal of Advanced Marine Engineering and Technology
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    • v.30 no.2
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    • pp.247-252
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    • 2006
  • Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

The Fault Diagnosis Method of Diesel Engines Using a Statistical Analysis Method (통계적분석기법을 이용한 디젤기관의 고장진단 방법에 관한 연구)

  • Kim, Young-Il;Oh, Hyun-Gyeong;Cheon, Hang-Chun;Yu, Yung-Ho
    • Proceedings of the Korean Society of Marine Engineers Conference
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    • 2005.06a
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    • pp.281-286
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    • 2005
  • Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

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