Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
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The Fault Diagnosis Method of Diesel Engines Using a Statistical Analysis Method

통계적 분석기법을 이용한 디젤기관의 고장진단 방법에 관한 연구

  • 김영일 (소나테크(주)) ;
  • 오현경 (한국해양대학교 대학원 제어계측공학과) ;
  • 유영호 (한국해양대학교 IT공학부)
  • Published : 2006.03.01
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Abstract

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.

Keywords

References

  1. J. Wagner and R. Shoureshi, 'A Failure isolation strategy for thermofluid system diagnostics,' ASME J. Eng. for Industry, vol. 115, pp. 459-465, 1993 https://doi.org/10.1115/1.2901790
  2. R. Isermann, 'Process fault detection based on modeling and estimation methods - a survey,' Automatica, vol. 20, no. 4, pp.387-404, 1984 https://doi.org/10.1016/0005-1098(84)90098-0
  3. M. M. Polycarpou and A. T. Vemuri, 'Learning methodology for failure detection and accommodation,' IEEE Contr. Syst. Mag., pp.16-24, 1995
  4. J. C. Hoskins and D. M. Himmelblau, 'Artificial neural network models of knowledge representation in chemical engineering,' Computers Chem. Engng., vol. 12, no. 9, pp. 881-890, 1988 https://doi.org/10.1016/0098-1354(88)87015-7
  5. V.Venkatasubramanian, R.Vaidyanathan, and Y. Yamamoto, 'Process fault detection and diagnosis using neural networks -steady state processes,' Computers Chem. vol. 14, no. 7, pp. 669-712, 1990
  6. E. Eryurek and B.R. Upadhyaya, 'Sensor validation for power plants using adaptive back propagation neural network,' IEEE Trans. Nuclear Science, vol. 37, no. 2, pp.1040-1047, 1990 https://doi.org/10.1109/23.106752
  7. T.Sorsa, H. N. Koivo, and H. Koivisto, 'Neural netwoks in process fault diagnosis,' IEEE Trans. Syst., Man and Cybern.,vol. 21, no. 4, pp. 815-825, 1991 https://doi.org/10.1109/21.108299
  8. M. A. Karmer and J.A. Leonard, 'Diagnosis using backpropagation neural networks-analysis and criticism,' Computers Chem. Engng., vol. 14, no. 12, pp. 1323-1338, 1990 https://doi.org/10.1016/0098-1354(90)80015-4