Diesel engine fault diagnosis by means of ANNs: A comparison of two methods

Proceedings of the Institute of Marine Engineering, Science and Technology Part B: Journal of Marine Design and Operations

Volumn , Issue 6 B, 2004, Pages 29-37

  Mesbahi, Ehsan ^a   Arriagada, Jaime ^b   Assadi, Mohsen ^b   Ghorban, Hassan ^c  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b LUND UNIVERSITY   (Sweden)

^c IRISL Training Institute   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

GAS TURBINES; GRAPHICAL USER INTERFACES; NEURAL NETWORKS; PARAMETER ESTIMATION; PERSONNEL TRAINING;

DATA SETS; DIESEL ENGINE FAULT DIAGNOSIS; FAULT PATTERN METHODS;

DIESEL ENGINES;

EID: 10844243101     PISSN: 14761556     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (16)

1. Woodward JB and Latorre RG. Modelling of Diesel Engine Transient behaviour in Marine Propulsion Analysis, SNAME transVol. 92, 33-49, 1984.
2. Mesbahi E. 2000. Modelling, simulation, fault diagnosis and control of Diesel engines, PhD thesis, University of Newcastle upon Tyne.
3. Mesbahi E and Roskilly AP. A Model Reference Neural Adaptive Controller (MRNAC) for Time-variant Systems with Uncertainties, IASTED International Conference on Control and Applications, Honolulu, Hawaii, USA, 1998.
4. Narendra K and Parthasarathy K. Identification and Control of Dynamic Systems Using Neural Networks, IEEE Trans, on Neural Networks, vol 1, No 1, March 1990.
5. Mesbahi E and Atlar M. Applications of Artificial Neural networks in marine design and modelling, Proc of AIOMA'98 conference, Technical University of Hamburg-Harburg (TUHH), 34-46. Germany, 1998.
6. Isermann R. Process fault detection based on modelling and estimation methods- A survey, Automatica, vol 20, 387-404. 1984
7. Al-Najjar B. 1997. Condition-Based Maintenance: Selection and Improvement of a Cost-effective Vibration-Based Policy for Rolling Element Bearings. Doctoral Thesis, Department of Industrial Engineering, Lund University.
8. Handschin E. Real-Time Data Processing Using State Estimation in Electric Power Systems. In Real-Time Control of Electric Power Systems - Proc of the Symposium on Real-Time Control of Electric Power Systems. Edited by Handschin, Elsevier, 1972.
9. Russell S and Norvig P. Artificial Intelligence - A Modern Approach. Prentice-Hall, Inc. 1995.
10. Massie D. Neural Network Fundamentals for Scientists and Engineers. Proc of ECOS'01, Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems, 1st International Conference on Applied Thermodynamics, Istanbul, Turkey, vol 1, pp 123-128, 2001.
11. Bishop CM. Neural Networks for Pattern Recognition. Oxford University Press Inc., Oxford, UK, 1995.
12. Haykin S. Neural Networks - A Comprehensive Foundation. 2nd ed Prentice Hall, Inc, New Jersey, USA, 1999.
13. Grimmelius HT, Meiler PP, Maas HLMM, Bonnier B, Grevink JS, and van Kuilenburg RF. Three State-of-the-Art Methods for Condition Monitoring. IEEE Trans on Industrial Electronics 46(2):407-416, 1999.
14. rd CIMAC Congress, Hamburg, Germany, 2001.
15. Cybenko G. Approximation by Superpositions of a Sigmoidal Function. Mathematics of Control, Signals, and Systems, vol. 2, pp 303-314, 1989.
16. Learning Internal Representations by Error Propagation, Parallel Distributed Processing. Explorations in the Microstructure of Cognition, vol 1: Foundations, pp 318-362, 1986.