Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
권호 표지

The Development of Diesel Engine Room Fault Diagnosis System Using a Correlation Analysis Method

상관분석법에 의한 선박기관실 고장진단 시스템 개발

  • 김영일 (소나테크(주)) ;
  • 오현경 (한국해양대학교 대학원 제어계측공학과) ;
  • 유영호 (한국해양대학교 IT공학부)
  • Published : 2006.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

Keywords

References

  1. MAN B&W Diesel A/S, Project Guide and S80MC/S80MCE, Copenhagen Denmark, pp. 1.1-1.20, 1990
  2. New Sulzer Diesel Ltd., Engine Selection and Project Manual, RTA84 (C,T,M), Winterthur Switzerland, pp. 1-9, 1994
  3. 中野克彦, 德岡哲夫, '大型低速 ディゼル機關の現狀と展望', 日本舶用機關學會誌, Vol.29, No.12, pp. 892-899, 1994
  4. 本村 收, 月祐之, '大型低速 ディゼル機關の 現狀と展望-三菱UE形 機關', 日本舶用機關學會誌, Vol. 29, No. 12, pp. 900-903, 1994
  5. 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
  6. 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
  7. M. M. Polycarpou and A. T. Vemuri, 'Learning methodology for failure detection and accommodation,' IEEE Contr. Syst. Mag., pp. 16-24, 1995
  8. 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
  9. 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
  10. 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
  11. 천행춘, '신경회로망과 퍼지추론을 이용한 선박디젤기관의 고장진단 예측시스템,' 한국해양대학교 공학박사 학위논문, 2, 2003