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

Yang, Erfu and Xiang, Hongjun and Gu, Dongbing and Zhang, Zhenpeng (2007) 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, 4 (3). pp. 255-261. ISSN 1476-8186

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Abstract

Fault diagnosis of liquid rocket propulsion systems (LRPSs) is a very important issue in space launch activities particularly when manned space missions are accompanied, since the safety and reliability can be significantly enhanced by exploiting an efficient fault diagnosis system. Currently, inverse problem-based diagnosis has attracted a great deal of research attention in fault diagnosis domain. This methodology provides a new strategy to model-based fault diagnosis for monitoring the health of propulsion systems. To solve the inverse problems arising from the fault diagnosis of LRPSs, GAs have been adopted in recent years as the first and effective choice of available numerical optimization tools. However, the GA has many control parameters to be chosen in advance and there still lack sound theoretical tools to analyze the effects of these parameters on diagnostic performance analytically. In this paper a comparative study of the influence of GA parameters on diagnostic results is conducted by performing a series of numerical experiments. The objective of this study is to investigate the contribution of individual algorithm parameter to final diagnostic result and provide reasonable estimates for choosing GA parameters in the inverse problem-based fault diagnosis of LRPSs. Some constructive remarks are made in conclusion and will be helpful for the implementation of GA to the fault diagnosis practice of LRPSs in the future.

Item type:Article
ID code:53015
Keywords:comparative study, fault diagnosis, genetic algorithm, inverse problem, liquid rocket propulsion systems, Electronic computers. Computer science, Engineering design, Control and Systems Engineering, Computer Science Applications, Applied Mathematics, Modelling and Simulation
Subjects:Science > Mathematics > Electronic computers. Computer science
Technology > Engineering (General). Civil engineering (General) > Engineering design
Department:Faculty of Engineering > Design, Manufacture and Engineering Management
Depositing user: Pure Administrator
Date deposited:14 May 2015 09:20
Last modified:21 Sep 2017 12:27
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URI:https://strathprints.strath.ac.uk/id/eprint/53015
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