Predictive unsupervised organisation in marine engine fault detection

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 249-256

  Morgan, Ian ^a   Liu, Honghai ^a   Turnbull, George ^a   Brown, David ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; DIESEL ENGINES; ELECTRIC FAULT CURRENTS; ENGINE CYLINDERS; ENGINES; FAULT DETECTION; NEURAL NETWORKS; SOCIETIES AND INSTITUTIONS; TOPOLOGY;

DIAGNOSIS SYSTEMS; DO-MAINS; FAULT DIAGNOSIS; FAULT DIAGNOSIS TECHNIQUES; K MEANS; K-MEANS ALGORITHMS; LOCATION OF FAULTS; MARINE DIESEL ENGINES; MARINE INDUSTRIES; NEURAL GAS ALGORITHMS; NUMBER OF CLUSTERS; REPRESENTATIVE SAMPLES; SELF-ORGANISING MAPS;

MARINE ENGINES;

EID: 56349102693     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633798     Document Type: Conference Paper

References (33)

1. A. Tsang, "Condition based maintenance: tools and decision making," Journal of Quality in Maintenance Engineering, vol. 1, pp. 3-17, 1995.
2. P. Tse, "Maintenance practices in hong kong and the use of the intelligent scheduler," Journal of Quality in Maintenance Engineering. vol. 8, pp. 369-380, 2002.
3. J. Dragsted and O. Bergeson, "Influence of low cylinder consumption on operating cost for 2-stroke engines," International council on combustion engines, CIMAC Congress, Kyoto, vol. 9, 2004.
4. K. Yu, "An intelligent fault diagnostic tool for marine diesel engine system by neural network with fuzzy modification," Marine Research Journal, vol. 10, pp. 91-102, 2001.
5. D. Edwards, G. Holt, and F. Harris, "Predictive maintenance techniques and their relevance to construction plant," Journal of Quality in Maintenance, vol. 4, pp. 25-37, 1998.
6. C. Tan, P. Irving, and D. Mba, "A comparitive experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears," Mechanical Systems and Signal Processing, vol. 21, pp. 208-233, 2007.
7. R. Douglas, J. Steel, and R. Reuben, "A study of the tribological behaviour of piston ring/cylinder liner interaction in diesel engines using acoustic emission," Tribology International, vol. 39, pp. 1634-1642, 2006.
8. H. Zhang, Z. Li, and Z. Chen, "Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines," Tribology International, vol. 36, pp. 753-756, 2003.
9. Y. Liu, Z. Liu, Y. Xie, and Z. Yao, "Research on an on-line wear condition monitoring system for marine diesel engine," Tribology International, vol. 33, pp. 829-835, 2000.
10. B. Tormos, V. Macian, P. Olmeda, and L. Montoro, "Wear rate determination for IC engine condition monitoring; results obtained in an urban transport fleet," SAE International, 2004.
11. V. Macian, B. Tormos, P. Olmeda, and L. Montoro, "Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis," Tribology International, vol. 36, pp. 771-776, 2003.
12. V. Macian, B. Tormos, P. Olmeda, L. Montoro, and E. Anubla, "Results and benefits of an oil analysis programme for railway locomotive diesel engines," Insight, vol. 45, pp. 1-5, 2003.
13. M. Camero, "Selection of diagnostic techniques and instrumentation in a predictive maintenance program. a case study," Decision Support Systems, vol. 38, pp. 539-555, 2005.
14. V. Macian, B. Tormos, A. Sala, and J. Ramirez, "Fuzzy logic-based expert system for diesel engine oil analysis diagnosis," Insight, vol. 8, pp. 1-8, 2006.
15. L. Pevzner, G. Rozenburg, and V. Spirova, "Diagnostic interpretation of results from analyses of used motor oils," Chemistry and Technology of Fuels and Oils, vol. 30, pp. 235-239, 1994.
16. K. Qi, S. Zhang, and X. Zhang, "Element correlation analysis and its application on condition monitoring and fault diagnosis of diesel engines," CIMAC Congress, Hamburg, pp. 1165-1171, 2001.
17. M. Rychetsky, S. Ortmann, and M. Glesner, "Support vector approaches for engine knock detection," Neural Networks, vol. 2, pp. 969-974, 1999.
18. V. Palade, R. Patton, F. Uppal, J. Quevedo, and S. Daley, "Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods," Proceedings of the 15th IFAC World Congress, pp. 2477-2482, 2002.
19. R. Antonic, Z. Vukic, and O. Kuljaca, "Marine diesel engine process modelling and control using advanced technologies," Proceedings of the IEEE International Symposium on Industrial Electronics, pp. 229-234, 2005.
20. R. Antonic, Z. Vukic, and L. Kuljaca, "Fuzzy modelling and control of marine diesel engine process," 11th Mediterranean Conference on Control and Automation, Greece, 2003.
21. Z. Jiang, H. Fu, and L. Li, "Support vector machine for mechanical faults classification," Journal of Zhejiang University Science, vol. 5, pp. 433-439, 2005.
22. Z. Hu, Y. Cai, Y. Li, and X. Xu, "Data fusion for fault diagnosis using multi-class support vector machines," Journal of Zhejiang University Science, vol. 6A, pp. 1030-1039, 2005.
23. E. Laukonen, K. Passino, V. Krishnaswami, G. Luh, and G. Rizzoni, "Fault detection and isolation for an experimental combustion engine via fuzzy identification," IEEE Transactions on Control Systems Technology, vol. 3, pp. 347-354, 1995.
24. J. Lehtonen and H. Koivo, "Fault diagnosis of induction motors with dynamical neural networks," IEEE International Conference on Systems Management and Cybernetics, vol. 3, pp. 2979-2984, 2005.
25. H. Gelgele and K. Wang, "An expert system for engine fault diagnosis: development and application," Journal of Intelligent Manufacturing, vol. 9, pp. 539-545, 1998.
26. D. Preston, "Aircraft fuel control diagnostic fault detection through expert systems," IEEE International Symposium on Industrial Electronics, pp. 644-645, 1993.
27. S. Russell, and P. Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall, 1995.
28. E. Keogh, S. Lonardi, and B. Chiu, "Finding surprising patterns in a time series database in linear time and space," Proceedings of ACM Knowledge Discovery and Data Mining, pp. 550-556, 2002.
29. T. Kohonen, "The self-organizing map," Proceedings of the IEEE, vol. 78, pp. 1464-1480, 1990.
30. -, Self-Organizing Maps, 3rd ed. Springer, 2001.
31. F. Murtagh and M. Hemandez-Pajares, "The kohonen self-organizing map method: an assessment," Journal of Classification, vol. 12, pp. 165-190, 1995.
32. B. Fritzke, "A growing neural gas network learns topologies," Advances in Neural Information Processing Systems, vol. 7, 1995.
33. J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, "Som toolbox for matlab 5," 2000.