基于非线性PCA的有效载荷健康参数提取

Payload health parameter extraction based on nonlinearpca

随着卫星有效载荷功能和结构日趋复杂,对设备的稳定性、可靠性提出更高要求,文章针对载荷设备健康表征参数庞大,导致健康诊断困难的问题,提出了非线性的PCA分析法。该方法首先对有效载荷设备自顶向下构建一个健康表征参数合并集,其次在传统PCA的基础上,采用非线性数据融合技术获取关键健康表征参数,并对所产生的协方差矩阵中的特征值、特征向量以及各特征值比率做了深入研究,结果显示非线性的PCA算法能带来更好的降维效果以及特征值累计占比率,不仅能保持数据之间的差异度信息,还能保存样本自身信息,有效降低了数据处理导致的信息损耗以及对星上载荷计算及存储能力的需求,说明改进方法在提取有效载荷设备健康表征参数中表现出显著优势。

As satellite payload functions and structures become increasingly complex,higher requirements are being placed on equipment stability and reliability.To address the problem of the huge health characterization parameters of the payload equipment,which leads to the difficulty of health diagnosis,a nonlinear Principal Component Analysis(PCA)method is proposed.The method firstly constructs a top-down merged set of health characterization parameters for the payload equipment.In the end,on the basis of traditional PCA,nonlinear data fusion technology is used to obtain the key health characterization parameters,and the eigenvalues,eigenvectors,and ratios of eigenvalues in the resulting covariance matrices are studied in depth,and the results show that nonlinear PCA algorithms can bring better dimensionality reduction and eigenvalue cumulative ratio,which not only can maintain data quality,but also can reduce the number of eigenvalues.The results show that the nonlinear PCA algorithm can bring better dimensionality reduction and eigenvalue ratios,not only maintain the information of the differences between the data,but also preserve the information of the samples themselves,which effectively reduces the information loss caused by the data processing as well as the demand for the on-board load computation and storage capacity,indicating that the improved method shows significant advantages in the extraction of the health characterization parameters of the payload equipment.