A comparative study of genetic algorithm parameters for the inverse problem-based fault diagnosis of liquid rocket propulsion systems

International Journal of Automation and Computing

Volumn 4, Issue 3, 2007, Pages 255-261

  Yang, Erfu ^a   Xiang, Hongjun ^a   Gu, Dongbing ^b   Zhang, Zhenpeng ^a  

^a BEIHANG UNIVERSITY   (China)

^b UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Comparative study; Fault diagnosis; Genetic algorithm; Inverse problem; Liquid rocket propulsion systems

Indexed keywords

FAILURE ANALYSIS; GENETIC ALGORITHMS; INVERSE PROBLEMS; PARAMETER ESTIMATION; ROCKETS;

COMPARATIVE STUDY; LIQUID ROCKET PROPULSION SYSTEMS; OPTIMIZATION TOOLS;

SPACECRAFT PROPULSION;

EID: 34547416484     PISSN: 14768186     EISSN: 17518520     Source Type: Journal    
DOI: 10.1007/s11633-007-0255-5     Document Type: Article

References (9)

1. A. Duyar, W. Merrill. Fault Diagnosis for the Space Shuttle Main Engine. Journal of Guidance, Control, and Dynamics, vol. 15, no. 2, pp. 384-389, 1992.
2. E. Yang, Z. Zhang, G. Liu. Model and Algorithm of Inverse Problems on Fault Diagnosis for Propulsion Systems. Journal of Beijing University of Aeronautics and Astronautics, vol. 25, no. 6, pp. 684-687, 1999. (in Chinese)
3. E. Yang, H. Xiang, Z. Zhang. A Decomposition Approach to the Inverse Problem-based Fault Diagnosis of Liquid Rocket Propulsion Systems. In Proceedings of the SICE Annual Conference, Sapporo, Japan, pp. 1607-1612, 2004.
4. E. Yang, Y. Xu, Z. Zhang. Study on Modeling and Simulations of Failure Process for Liquid Propellant Rocket's Propulsion Systems. Journal of Tsinghua University, vol. 41, no. 3, pp. 104-108, 2001 (in Chinese)
5. A. Duyar, V. Eldem, W. C. Merill, T. Guo. Fault Detection and Diagnosis in Propulsion Systems: A Fault Parameter Estimation Approach. Journal of Guidance, Control, and Dynamics, vol. 17, no. 1, pp. 104-108, 1994.
6. C. L. Karr, I. Yakushin, K. Nicolosi. Solving Inverse Initial Value, Boundary-value Problems via Genetic Algorithm. Engineering Applications of Artificial Intelligence, vol. 13, no. 6, pp. 625-633, 2000.
7. Y. Yao. Population Size and Sampling Complexity in Genetic Algorithms. In Proceedings of the Bird of a Feather Workshops on Learning, Adaptation, and Approximation in Evolutionary Computation, Chicago, Illinois, USA, pp. 178-181, 2003. [Online], Available: http://citeseer.ist.psu.edu/gao03population.html.
8. Á. E. Eiben, R. Hinterding, Z. Michalewicz. Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, vol.3, no.2, pp. 124-141, 1999.
9. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs, 3rd edition, Springer-Verlag, Berlin, 1996.