时间依赖 马哈拉诺比斯距离用于多变量航天器遥测系列中的异常检测
Jingyue Pang1, Datong Liu2, Yu Peng2
1School of Artificial Intelligence, Chongqing Technology and Business University, China.
ISA transactions
|June 18, 2023
概括
这项研究引入了时间依赖Mahalanobis距离 (TDMD) 以改善航天器异常检测. TDMD有效地识别了遥测数据中的复杂异常,减少了错误报警和错过检测.
科学领域:
- 太空飞船工程 太空飞船工程
- 数据科学是数据科学.
- 时间序列分析时间序列分析.
背景情况:
- 航天器遥测数据对于监测健康和性能至关重要.
- 高维,依赖和伪周期遥测数据挑战了传统的异常检测.
- 现有的Mahalanobis距离 (MD) 方法缺乏时间演变捕获,导致检测不准确.
研究的目的:
- 开发一种先进的异常检测方法,用于多变量航天器遥测数据.
- 解决固定门MD方法在捕捉时间动态方面的局限性.
- 增强实时航天器操作中的上下文和集体异常的检测.
主要方法:
- 使用多因素预测实现时间依赖马哈拉诺比斯距离 (TDMD).
- 基于时间序列相关性的MD动态上下限的构建.
- 在线测试和验证使用模拟和真实航天器遥测数据.
主要成果:
- 在多变量远程测量系列中,TDMD有效检测上下文和集体异常.
- 与传统的固定门方法相比,拟议的方法显示了更高的准确性.
- 实验证实了TDMD在各种遥测数据集上的有效性和适用性.
结论:
- 在复杂的航天器遥测中,TDMD为异常检测提供了强大的解决方案.
- 该方法通过准确识别微妙和不断变化的异常来增强航天器健康监测.
- TDMD是确保太空任务可靠性和安全性的宝贵工具.
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