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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
22.7 References
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): 297–333
Blischke WR, Murthy DNP, (2000) Reliability: modeling, prediction, and optimization. John Wiley, New York
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): 1634–1642
Fisher RA, (1970) Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh
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: 109–122
Gorin N, Shay G, (1997) Diesel lubricant monitoring with new-concept shipboard test equipment. TriboTest 3(4): 415–430
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): 407–416
Hargis SC, Taylor H, Gozzo JS, (1982) Condition monitoring of marine diesel engines through ferrographic oil analysis. Wear 90(2): 225–238
Hofmann SL, (1987) Vibration analysis for preventive maintenance: a classical case history. Marine Technology 24(4): 332–339
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: 386–395
Hountalasa DT, Kouremenosa AD, (1999) Development and application of a fully automatic troubleshooting method for large marine diesel engines. Applied Thermal Engineering 19(3): 299–324
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): 335–379
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): 43–47
Jardine AKS, Ralston P, Reid N, Stafford J, (1989) Proportional hazards analysis of diesel engine failure data. Quality and Reliability Engineering International 5(3): 207–216
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): 1483–1510
Jiang R, Jardine AKS, (2006) Composite scale modeling in the presence of censored data. Reliability Engineering and System Safety 91(7): 756–764
Johnson JH, Hubert CJ, (1983) An overview of recent advances in quantitative ferrography as applied to diesel engines. Wear 90(2): 199–219
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): 829–835
Logan KP, (2005) Operational Experience with Intelligent Software Agents for Shipboard Diesel and Gas Turbine Engine Health Monitoring. 2005 IEEE Electric Ship Technologies Symposium: 184–194
Lu S, Lu H, Kolarik WJ, (2001) Multivariate performance reliability prediction in real-time. Reliability Engineering and System Safety 72: 39–45
Moubray J, (1997) Reliability-centred maintenance. Butterworth-Heinemann, Oxford.
Murthy DNP, Xie M, Jiang R, (2003) Weibull Models, Wiley.
Pontoppidan NH, Larsen J, (2003) Unsupervised condition change detection in large diesel engines. 2003 IEEE XI11 Workshop On Neural Networks For Signal Processing: 565–574
Priha I, (1991) FAKS—an on-line expert system based on hyperobjects. Expert Systems with Applications 3(2): 207–217
Raadnui S, Roylance BJ, (1995) Classification of wear particle shape. Lubrication Engineering 51(5): 432–437
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): 111–116
Roylance BJ, Raadnui S, (1994) Morphological attributes of wear particles — their role in identifying wear mechanisms. Wear 175(1–2): 115–121
Saranga H, (2002) Relevant condition-parameter strategy for an effective condition-based maintenance. Journal of Quality in Maintenance Engineering 8(1): 92–105
Scherer M, Arndt M, Bertrand P, Jakoby B, (2004) Fluid condition monitoring sensors for diesel engine control. Sensors, 2004. Proceedings of IEEE 1: 459–462
Sharkey AJC (2001) Condition monitoring, diesel engines, and intelligent sensor processing. Intelligent Sensor Processing, A DERA/IEE Workshop on: 1/1–1/6
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 2:1695–1698
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: 975–980
Wang HF, Wang JP, (2000) Fault diagnosis theory: method and application based on multisensor data fusion. Journal of Testing and Evaluation 28(6): 513–518
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): 995–1006
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): 753–756
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: 66–72
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag London Limited
About this chapter
Cite this chapter
Jiang, R., Yan, X. (2008). Condition Monitoring of Diesel Engines. In: Complex System Maintenance Handbook. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-011-7_22
Download citation
DOI: https://doi.org/10.1007/978-1-84800-011-7_22
Publisher Name: Springer, London
Print ISBN: 978-1-84800-010-0
Online ISBN: 978-1-84800-011-7
eBook Packages: EngineeringEngineering (R0)