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

Erfu Yang, Hongjun Xiang, Dongbing Gu, Zhenpeng Zhang

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)255-261
Number of pages7
JournalInternational Journal of Automation and Computing
Volume4
Issue number3
DOIs
Publication statusPublished - Jul 2007

Fingerprint

Rockets
Fault Diagnosis
Inverse problems
Propulsion
Failure analysis
Comparative Study
Inverse Problem
Genetic algorithms
Genetic Algorithm
Liquid
Liquids
Diagnostics
Space Missions
Numerical Optimization
Control Parameter
Health
Safety
Numerical Experiment
Gas
Acoustic waves

Keywords

  • comparative study
  • fault diagnosis
  • genetic algorithm
  • inverse problem
  • liquid rocket propulsion systems

Cite this

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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.",
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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; Xiang, Hongjun; Gu, Dongbing; Zhang, Zhenpeng.

In: International Journal of Automation and Computing, Vol. 4, No. 3, 07.2007, p. 255-261.

Research output: Contribution to journalArticle

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