Satellite anomaly is a process of evolution. Detecting this evolution and the underlying feature changes is critical to satellite health prediction, fault early warning, and response. Analyzing the correlation between telemetry parameters is more convincing than detecting single-point anomalies. In this article, principal component analysis method was adopted to downscale the multivariate probability model, T 2 {T}^{2} statistic was checked to determine the data anomaly, without the trouble of threshold setting. After an anomaly was detected, time-domain visualization and dimension reduction methods were introduced to visualize the satellite anomaly evolution, where the dimensions of telemetry or features were reduced and presented in two- or three-dimensional coordinates. Engineering practice shows that this method facilitates the early detection of satellite anomalies, and helps ground operators to respond in the early stages of an anomaly.

中文翻译：

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基于核方法的航天器特征提取故障检测与主成分分析研究

卫星异常是一个演化过程。检测这种演变和潜在特征变化对于卫星健康预测、故障预警和响应至关重要。分析遥测参数之间的相关性比检测单点异常更有说服力。本文采用主成分分析方法对多元概率模型进行降尺度， 吨 2 {T}^{2} 通过统计数据判断数据异常，免去阈值设置的麻烦。在检测到异常后，引入时域可视化和降维方法来可视化卫星异常演变，其中遥测或特征的维度被缩减并呈现在二维或三维坐标中。工程实践表明，该方法有助于早期发现卫星异常，并帮助地面操作人员在异常的早期阶段做出响应。