Advertisement

Condition Monitoring of Diesel Engines

Chapter
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Abstract

The engine is the heart of the ship; and the lubricant is the lifeblood of the engine. Wear is one of the main causes that lead to engine failures. It is desirable to avoid engine breakdowns for reasons of safety and economy. This has led to an increasing interest in engine condition monitoring and performance modeling so as to provide useful information for maintenance decision.

Keywords

Acoustic Emission Diesel Engine Condition Monitoring Control Chart Wear Particle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

22.7 References

  1. Anderson DN, Hubert CJ, Johnson JH, (1983) Advances in quantitative analytical ferrography and the evaluation of a high gradient magnetic separator for the study of diesel engine wear: Wear 90(2): 297–333CrossRefGoogle Scholar
  2. Blischke WR, Murthy DNP, (2000) Reliability: modeling, prediction, and optimization. John Wiley, New YorkzbMATHGoogle Scholar
  3. Douglas RM, Steel JA, Reuben RL, (2006) A study of the tribological behaviour of piston ring/cylinder liner interaction in diesel engines using acoustic emission. Tribology International 39(12): 1634–1642CrossRefGoogle Scholar
  4. Fisher RA, (1970) Statistical Methods for Research Workers. Oliver and Boyd, EdinburghGoogle Scholar
  5. Goode KB, Moore J, Roylance BJ, (2000) Plant machinery working life prediction method utilizing reliability and condition-monitoring data. Proceedings of the Institution of Mechanical Engineers Part E-Journal of Process Mechanical Engineering 214: 109–122CrossRefGoogle Scholar
  6. Gorin N, Shay G, (1997) Diesel lubricant monitoring with new-concept shipboard test equipment. TriboTest 3(4): 415–430CrossRefGoogle Scholar
  7. Grimmelius HT, Meiler PP, Maas HLMM, Bonnier B, Grevink JS, van Kuilenburg RF, (1999) Three state-of-the-art methods for condition monitoring. IEEE Transactions on Industrial Electronics 46(2): 407–416CrossRefGoogle Scholar
  8. Hargis SC, Taylor H, Gozzo JS, (1982) Condition monitoring of marine diesel engines through ferrographic oil analysis. Wear 90(2): 225–238CrossRefGoogle Scholar
  9. Hofmann SL, (1987) Vibration analysis for preventive maintenance: a classical case history. Marine Technology 24(4): 332–339Google Scholar
  10. Hojen-Sorensen PAdFR, de Freitas N, Fog T, (2000) On-line probabilistic classification with particle filters. Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop 1: 386–395Google Scholar
  11. Hountalasa DT, Kouremenosa AD, (1999) Development and application of a fully automatic troubleshooting method for large marine diesel engines. Applied Thermal Engineering 19(3): 299–324CrossRefGoogle Scholar
  12. Hubert CJ, Beck JW, Johnson JH, (1983) A model and the methodology for determining wear particle generation rate and filter efficiency in a diesel engine using ferrography. Wear 90(2): 335–379CrossRefGoogle Scholar
  13. Jakopovic J, Bozicevic J, (1991) Approximate knowledge in LEXIT, an expert system for assessing marine lubricant quality and diagnosing engine failures. Computers in Industry 17(1): 43–47CrossRefGoogle Scholar
  14. Jardine AKS, Ralston P, Reid N, Stafford J, (1989) Proportional hazards analysis of diesel engine failure data. Quality and Reliability Engineering International 5(3): 207–216CrossRefGoogle Scholar
  15. Jardine AKS, Lin D, Banjevic D, (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20(7): 1483–1510CrossRefGoogle Scholar
  16. Jiang R, Jardine AKS, (2006) Composite scale modeling in the presence of censored data. Reliability Engineering and System Safety 91(7): 756–764CrossRefGoogle Scholar
  17. Johnson JH, Hubert CJ, (1983) An overview of recent advances in quantitative ferrography as applied to diesel engines. Wear 90(2): 199–219CrossRefGoogle Scholar
  18. Liu Y, Liu Z, Xie Y, Yao Z, (2000) Research on an on-line wear condition monitoring system for marine diesel engine. Tribology International 33(12): 829–835CrossRefGoogle Scholar
  19. Logan KP, (2005) Operational Experience with Intelligent Software Agents for Shipboard Diesel and Gas Turbine Engine Health Monitoring. 2005 IEEE Electric Ship Technologies Symposium: 184–194Google Scholar
  20. Lu S, Lu H, Kolarik WJ, (2001) Multivariate performance reliability prediction in real-time. Reliability Engineering and System Safety 72: 39–45CrossRefGoogle Scholar
  21. Moubray J, (1997) Reliability-centred maintenance. Butterworth-Heinemann, Oxford.Google Scholar
  22. Murthy DNP, Xie M, Jiang R, (2003) Weibull Models, Wiley.Google Scholar
  23. Pontoppidan NH, Larsen J, (2003) Unsupervised condition change detection in large diesel engines. 2003 IEEE XI11 Workshop On Neural Networks For Signal Processing: 565–574Google Scholar
  24. Priha I, (1991) FAKS—an on-line expert system based on hyperobjects. Expert Systems with Applications 3(2): 207–217CrossRefGoogle Scholar
  25. Raadnui S, Roylance BJ, (1995) Classification of wear particle shape. Lubrication Engineering 51(5): 432–437Google Scholar
  26. Roylance BJ, Albidewi IA, Laghari MS, Luxmoore AR, Deravi F, (1994) Computer-aided vision engineering (CAVE): Quantification of wear particle morphology. Lubrication Engineering 50(2): 111–116Google Scholar
  27. Roylance BJ, Raadnui S, (1994) Morphological attributes of wear particles — their role in identifying wear mechanisms. Wear 175(1–2): 115–121CrossRefGoogle Scholar
  28. Saranga H, (2002) Relevant condition-parameter strategy for an effective condition-based maintenance. Journal of Quality in Maintenance Engineering 8(1): 92–105CrossRefGoogle Scholar
  29. Scherer M, Arndt M, Bertrand P, Jakoby B, (2004) Fluid condition monitoring sensors for diesel engine control. Sensors, 2004. Proceedings of IEEE 1: 459–462CrossRefGoogle Scholar
  30. Sharkey AJC (2001) Condition monitoring, diesel engines, and intelligent sensor processing. Intelligent Sensor Processing, A DERA/IEE Workshop on: 1/1–1/6Google Scholar
  31. Sun C, Pan X, Li X, (1996) The application of multisensor fusion technology in diesel engine oil analysis. Signal Processing, 1996., 3rd International Conference on 2:1695–1698Google Scholar
  32. Tang T, Zhu Y, Li J, Chen B, Lin R, (1998) A fuzzy and neural network integrated intelligence approach for fault diagnosing and monitoring. UKACC International Conference on Control 2: 975–980CrossRefGoogle Scholar
  33. Wang HF, Wang JP, (2000) Fault diagnosis theory: method and application based on multisensor data fusion. Journal of Testing and Evaluation 28(6): 513–518CrossRefGoogle Scholar
  34. Wu X, Chen J, Wang W, Zhou Y, (2001) Multi-index fusion-based fault diagnosis theories and methods. Mechanical Systems and Signal Processing 15(5): 995–1006CrossRefGoogle Scholar
  35. Zhang H, Li Z, Chen Z, (2003) Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines. Tribology International 36(10): 753–756CrossRefGoogle Scholar
  36. Zhao C, Yan X, Zhao X, Xiao H, (2003) The prediction of wear model based on stepwise pluralistic regression. In: Proceedings of International Conference on Intelligent Maintenance Systems (IMS), Xi’an, China: 66–72Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Reliability and Maintenance LaboratoryChangsha University of Science and TechnologyChina
  2. 2.Reliability Engineering InstituteWuhan University of TechnologyChina

Personalised recommendations