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使用LSTM和非参数动态阈值检测航天器异常Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding |
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| 课程网址: | http://videolectures.net/kdd2018_hundman_detecting_spacecraft_ano... |
| 主讲教师: | Kyle Hundman |
| 开课单位: | NASA |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development. |
| 关 键 词: | 改进异常检测系统; 火星科学实验室; 假阳性缓解策略; 长短期记忆（LSTM）网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-02-09：cyh |
| 最后编审: | 2023-02-09：cyh |
| 阅读次数: | 115 |