Mechanical challenges in advanced spacecraft development

Yu, DengYun

doi:10.1360/n972014-01382

Cited by 1 publication

(1 citation statement)

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“…It is hoped that the potential of the reusable rocket is exploited as much as possible. [27][28][29][30] As one of the preliminary studies on HCM of China's reusable rocket, this paper develops an MPID framework model. An MPID judgment model is built for the first time, and a judgment method based on Bayes' risk function integrating multi-algorithm prior information is developed to solve the judgment problem of MPID effectively and meet the needs of practical engineering.…”

Section: Initial Stagementioning

confidence: 99%

Liquid-propellant rocket engine online health condition monitoring base on multi-algorithm parallel integrated decision-making

Nie

Cheng

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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This study reports multi-algorithm parallel integrated decision-making for liquid-propellant rocket engine online health condition monitoring to improve reliability and safety, especially for next-generation reusable engines. Fusing multi-algorithm detection information to judge liquid-propellant rocket engine condition is multi-algorithm parallel integrated decision-making main task, and multi-algorithm judgment problem is its central issue; i.e. how to make a global judgment from judgment results of different fault detection methods. Considering opportune fault detection, adequate rocket engine information exploitation, and reliable condition judging, the multi-algorithm parallel integrated decision-making framework for problem definition is presented along with a multi-algorithm parallel integrated decision-making judgment model. For more reliable, efficient global judgment, a method based on the Bayes’ risk function integrating multi-algorithm prior information is adopted. The proposed approach is validated with liquid-propellant rocket engine ground testing data. The results show that the multi-algorithm parallel integrated decision-making judgment model gives very effective and reliable performance relative to the voting method, successfully solving multi-algorithm judgment problems and meeting practical engineering needs.

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Section: Initial Stagementioning

confidence: 99%