The Deep Space Network is NASA's international array of antennas that support interplanetary spacecraft missions. A track is a block of multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing thousands of monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN Link Control Operators need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write Discrepancy Reports for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find and match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the track visualizer interface to assist DSN field operators and engineers.
翻译:深空间网络是美国航天局支持行星际航天器飞行任务的国际天线阵列。 轨迹是一个多维的时间序列,从开始到结束与目标航天器的DSN通信结束,包含数千个监测数据项目,在0.2-1Hz的频率下持续数小时。 监测每条轨道报告中关于具体航天器运行和DSN本身运行情况的数据。 DSN正在接收来自整个太阳系统32个航天器的信号。 DSN通常会撰写不统一报告,以便进行进一步的分析。 DSN 链接控制操作员需要同时监测多个轨道并实时识别异常现象。 DSN看到,随着任务数量的增加,需要处理的数据会随着时间的增加而增加。 在这个项目中,我们查看了过去8年的数据分析数据。 轨道上的任何异常都显示航天器、 DSNNE 设备或天气条件有问题。 DSNSN 操作员通常会撰写不统一报告,以便进一步的分析模式。 识别大型数据库的10个类似的历史轨道,以便快速查找和匹配异常现象。 DSNNND显示,这工具有三种功能:(1) 对比运行前的正常运行轨道和21次数据测试的轨道。