Spacecraft health state evaluation method

Abstract

The invention provides a spacecraft health state evaluation method. The method is used for solving the technical problem that an existing spacecraft health state evaluation method is high in health state evaluation subjectivity to different levels of objects and poor in project realization adaptation. The method comprises the steps of firstly determining an index system of certain evaluation, and distinguishing conventional indexes and core indexes; then, determining a weight coefficient of the conventional indexes; adopting spacecraft measured data and historical information as evaluation data; building a layering evaluation model; combining scores and weights of the indexes according to the layering evaluation model, and finally obtaining a top layer health state index score through layer-by-layer calculation from the bottom layer. According to the spacecraft health state evaluation method, the subjectivity of the spacecraft health state evaluation method is improved, and the method easy to implement is provided for spacecraft in-orbit health state evaluation.

Description

technical field [0001] The invention belongs to the field of aerospace measurement and control, and in particular relates to an evaluation method for the health state of a spacecraft. Background technique [0002] Health status assessment is one of the key technologies to ensure the safe and stable work of spacecraft. A complete health status assessment mainly includes status monitoring, status determination, status grading, and status prediction. It is necessary to determine the real-time working status of the spacecraft by studying the data information of the spacecraft. The health status of the spacecraft is also closely related to the internal structural characteristics of the spacecraft system. It is necessary to stratify the system according to its structure and comprehensively evaluate the health status of each level. [0003] The spacecraft is divided into multiple levels according to the structure, such as the lowest functional components, subsystems that can work...

AI Technical Summary

Problems solved by technology

[0004] At present, the commonly used health assessment methods mainly include AHP, Bayesian, neural network and other methods. For the practical application of spacecraft health assessment, these methods have their own shortcomings. The selection and quantification of AHP parameters and weights It is determined that it is too much affected by subjective factors. The Bayesian method needs to know the inherent relationship of the spacecraft. The relationship between the subdivision system and the entire star evaluation model is intricate, with a large amount of calculation and poor applicability. The evaluation method based on neural networks requires Based on a large amount of training data and continuous feedback and iterative updates, it is possible to obtain relatively accurate results. The convergence speed is slow and cannot meet the real-time evaluation requirements.

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