Application of kernel principal component analysis in autonomous fault diagnosis for spacecraft flywheel
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摘要:
针对在轨航天器执行机构故障诊断研究相对较少、姿态控制系统背景建模相对简单、算法自主性不强等问题，提出一种基于核主元分析（KPCA）的飞轮自主故障诊断方法。建立使用飞轮组的刚体航天器三轴稳定姿态控制系统；在力矩模式和转速模式下分别建立飞轮伺服系统，并给出飞轮常见故障及其模型；在上述模式下分别采集飞轮组输入输出的差值数据，进行同源扩维，通过改进特征向量归一化准则，优化了KPCA统计量法，并建立一种综合指标，通过比对该指标是否超限判断有无故障，减少对单一指标的主观侧重；在经典的贡献图法基础上进行溯源合并，计算综合贡献率，以此定位故障飞轮。仿真结果表明：所提方法能够实现航天器飞轮自主故障诊断，2种模式下，正确率较传统方法分别平均提高约40.94%、22.23%，适用于单点故障、多点故障、轻微故障等多种情况。
Abstract:Aiming at the problems of relatively few studies on actuator fault diagnosis of on orbit spacecraft, relatively simple background modeling for attitude control system and weak algorithm autonomy, an autonomous fault diagnosis method for spacecraft flywheel based on kernel principal component analysis (KPCA) is proposed. Firstly, the three-axis stable attitude control system of rigid spacecraft using flywheel group is established. Secondly, the flywheel servo system is established in torque mode and speed mode, and the common faults and models of flywheel are given. Then, in the above mode, the input and output differential data of flywheel group are collected for homologous dimension expansion. By improving the normalization criterion of eigenvector, the classical KPCA statistical method is optimized, and a comprehensive index is established. By comparing whether the index exceeds the limit to judge whether there is a fault, the subjective focus on a single index is reduced. Finally, based on the classical contribution graph method, the fault flywheels are located by tracing source and merging fault comprehensive contribution rate. Simulation results show that this method can realize autonomous fault diagnosis of spacecraft flywheel, and the accuracy of the two modes increases by an average of about 40.94% and 22.23% compared to traditional methods. It is suitable for single point fault, multi-point fault, and minor fault.
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图 1 力矩模式下时滞参数对量化指标影响示意图
Figure 1. Diagram of influence of delay parameter on quantitative index in torque mode
图 3 力矩模式下综合指标随时间的变化
Figure 3. Variation of comprehensive index with time in torque mode
图 4 力矩模式下指定时刻综合贡献率示意图
Figure 4. Diagram of comprehensive contribution rate at specified time in torque mode
图 5 转速模式下时滞参数对量化指标影响示意图
Figure 5. Diagram of influence of delay parameter on quantitative index in speed mode
图 7 转速模式下综合指标随时间的变化
Figure 7. Variation of comprehensive index with time in speed mode
图 8 转速模式下指定时刻综合贡献率示意图
Figure 8. Diagram of comprehensive contribution rate at specified time in speed mode
表 1 飞轮常见故障及模型
Table 1. Common faults and models of flywheel
序号 ew fw 故障特征 故障类型 1 0 0 转速不变或渐趋0 空转 2 0 0 转速迅速归0 卡死 3 1 $ \ne 0 $ 输入输出存在偏差 偏差故障 4 $ (0,1) $ 0 输入输出按比减小 增益故障 5 不定 不定 无固定特征 混合故障 
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表 2 飞轮基本参数
Table 2. Basic parameters of flywheel
转动惯量/
（kg·m2）电枢
电阻/$ \Omega $转矩系数/
（N·m·A−1）反电动势系数/
（V·s·rad−1）0.0077 2 0.029 0.029
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表 3 飞轮故障参数
Table 3. Fault parameters of flywheels
故障时间/s 故障类型 ew fw/（N·m） 故障部位 550~600 空转 0 0 飞轮1 450~500 偏差故障 1 −0.0003 飞轮2 350~400 增益故障 0.15 0 飞轮3 350~500 偏差故障 1 −0.0003 飞轮3
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表 4 力矩模式下时滞参数为2时不同指标性能对比
Table 4. Performance comparison of different indexes when number of delay parameter is 2 in torque mode
% 判定准则 误报率 漏报率 正确率 T2超限 1.50 11.55 91.80 SPE超限 2.60 74.00 49.80 T2、SPE同时超限 0.20 74.50 50.27 综合指标超限 4.60 11.25 90.97
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表 5 转速模式下时滞参数为2时不同指标性能对比
Table 5. Performance comparison of different indexes when number of delay parameter is 2 in speed mode
% 判定准则 误报率 漏报率 正确率 T2超限 6.40 0.10 97.80 SPE量超限 6.10 37.90 72.70 T2、SPE同时超限 0.70 37.90 74.50 综合指标超限 12.30 0.10 95.83
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