Autonomous Fuzzy Comprehensive Evaluation Method for Small Satellite Health State
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摘要: 当前卫星地面测试系统实时属性突出，但由于对数据挖掘分析不足，难以实现卫星系统级的健康诊断，需人工完成综合评估，存在效率低、通用性差等问题。该文提出一种多层次异构卫星系统综合评估方法，依据数据中缓变量、急变量和关键变量的特性，分别实现基于高斯分布模型、长短期记忆模型（LSTM）和统计模型的单项评估生成单因素模糊向量，采用离差最大化方法实现层次分析法和熵权法的主客观权值向量组合，基于模糊综合评判的方法对卫星状态进行全面综合评价，实现评估流程的自动化和智能化。在小卫星半实物仿真平台进行系统验证，结果表明该评估方法能有效地对卫星系统的健康状态进行全面评估。Abstract: The current satellite ground test system has outstanding real-time attributes, but due to insufficient data mining and analysis, it is difficult to achieve satellite system-level health diagnosis. Comprehensive evaluation needs to be completed manually, and there are problems such as low efficiency and poor versatility. A comprehensive evaluation method for multi-level heterogeneous satellite systems is proposed in this paper. According to the characteristics of the slow, urgent, and key variables in the data, the single-item evaluation generation sheet based on the Gaussian distribution model, the Long Short-Term Memory model (LSTM) and the statistical model is realized respectively. The maximum deviation method is used to realize the combination of subjective and objective weight vector of the analytic hierarchy process and entropy weight method, and comprehensively evaluate the satellite state based on the fuzzy comprehensive evaluation method, and the automation and intelligence of the evaluation process are realized. The system verification is carried out on the small satellite semi-physical simulation platform, and the results show that the evaluation method can effectively evaluate the health status of the satellite system.
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表 2 “能源速变分系统”正互反矩阵
${{\boldsymbol{A}}_2}$ 28 V +12 V +5 V –12 V 28 V 1 3 2 3 +12 V 1/3 1 1/2 1 +5 V 1/2 2 1 2 –12 V 1/3 1 1/2 1 
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表 4 “能源分系统”下的各权值
AHP EWM 组合权重 能源速变分系统 0.6667 0.5227 0.5964 能源缓变分系统 0.3333 0.4723 0.4036 28V电源电压 0.4554 0.6788 0.5671 +12V电源电压 0.1409 0.1150 0.1280 +5V电源电压 0.2628 0.0911 0.1770 –12V电源电压 0.1409 0.1150 0.1280 蓄电池组当前电量 0.5000 0.3879 0.4440 蓄电池组充电电量 0.2500 0.2823 0.2661 蓄电池组放电电量 0.2500 0.3298 0.2899
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表 5 对象集的健康等级模糊向量
良好 正常 一般 恶化 病态 能源系统 0 0.5563 0.0643 0.2097 0.1698 能源速变 0.2560 0 0.1077 0.3516 0.2847 能源缓变 1 0 0 0 0
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