基于GBDT的卫星工程参数异常检测

Anomaly Detection of Satellite Engineering Data Based on GBDT

卫星及其载荷的在轨运行异常诊断是卫星高效安全运行的重要支持,发展智能、高效的卫星异常检测方法,是卫星地面系统的研究焦点之一.在我国空间科学先导专项系列卫星任务的应用背景下,根据空间科学卫星的数据特性与异常形态,基于梯度提升决策树(gradient boosting decision tree, GBDT)原理构建卫星工程参数异常智能检测方法,利用量子科学实验卫星任务的工程数据开展应用验证与分析,与原采用的"阈值+规则表达式"异常检测方法相比,将平均准确率提升了约两个百分点,达到98%以上,可有效减少漏报和错报,并将检测速度提升了大约6倍.

On-orbit anomaly diagnosis of satellites and payloads is an essential support for the efficient and safe operations of satellites. Intelligent and efficient methods of satellite anomaly detection are one of the focuses of research in satellite ground systems. Under the background of the satellite missions of China’s Strategic Priority Program on Space Science, this study proposes an intelligent anomaly detection method of satellite engineering parameters based on the data characteristics and data anomaly forms of the space-science satellites and the gradient boosting decision tree(GBDT). The engineering data of the Quantum Science Experimental Satellite “Micius” are employed for application verification and analysis. Compared with the original “threshold + regular expression” anomaly detection method, the proposed method has an average accuracy of over 98%, with an increase of about two percentage points. False negatives and false positives can be effectively reduced, and the detection speed is increased by about six times.