| 一种基于相关概率模型的卫星异常检测方法 |
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| 作者姓名： | 孙宇豪 李国通 张鸽 |
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| 作者单位： | 1. 中国科学院微小卫星创新研究院, 上海;2. 中国科学院大学, 北京 100049;3. 上海科技大学, 上海 201210 |
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| 基金项目： | 上海市科学技术委员会科研计划项目（17DZ1100700）资助 |
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| 摘 要： | 卫星在轨运行期间，遥测数据是反映卫星健康状态的重要依据，在卫星故障早期检测到遥测数据的潜在异常对卫星的安全维护具有重大意义。工程上采用的阈值法无法有效检测到门限内的故障征兆，而且目前这一领域的理论研究无法有效地挖掘多维遥测序列的潜在相关性。针对这一问题，采用一种融合主成分分析的相关概率模型的检测方法，以某型号卫星实际在轨遥测数据为对象，深入分析故障案例。通过仿真验证该方法能够在故障早期检测出异常，并对实验结果进行对比和分析。而且，这种方法可以快速地帮助运管人员对早期故障做出诊断，以便地面及时处理，避免发生更大的事故。
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| 关 键 词： | 异常检测 故障征兆 相关概率模型 PCA检测 |
| 收稿时间： | 2019-10-18 |
| 修稿时间： | 2020-01-21 |
An anomaly detection method for satellite based on correlation probability model |
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| Authors: | SUN Yuhao LI Guotong ZHANG Ge |
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| Institution: | 1. Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. ShanghaiTech University, Shanghai 201210, China |
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| Abstract: | During the orbital operation of the satellite, the telemetry data is an important basis for reflecting the health status of satellites. The detection of potential anomalies in telemetry data is of great significance for the maintenance of satellites. Threshold method used in engineering can not effectively detect failure symptoms within the threshold, and the current theoretical research in this field can not effectively tap the potential correlation of multidimensional telemetry sequences. Therefore, this paper, taking the actual on-orbit telemetry data of a satellite as the object, adopts a detection method of correlation probability model that incorporates principal component analysis (PCA), and analyzes the failure case deeply. It verifies that this method can detect satellite's early failure and the results are compared and analyzed. This method can also quickly diagnose the failure so that the ground can handle it in time to avoid further accidents. |
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| Keywords: | anomaly detection failure symptoms correlation probability model PCA detection |
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