An Explainable Machine Learning Approach for Anomaly Detection in Satellite Telemetry Data-Reference-Cited by-同舟云学术

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An Explainable Machine Learning Approach for Anomaly Detection in Satellite Telemetry Data

Published:2024-03-02 Issue: Volume:06 Page:1-14
ISSN:
Container-title:2024 IEEE Aerospace Conference
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Author:

Kricheff Seth¹,Maxwell Emily²,Plaks Connor³,Simon Michelle⁴

Affiliation:

1. Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,US

2. Rose-Hulman Institute of Technology,Department of Electrical and Computer Engineering,Terre Haute,Indiana,US

3. School of Aeronautics and Astronautics Purdue University,West Lafayette,Indiana,US

4. Air Force Research Laboratory Kirtland AFB

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10520981/10520936/10521300.pdf?arnumber=10521300

Reference39 articles.

1. Telemetry-mining: A Machine Learning Approach to Anomaly Detection and Fault Diagnosis for Space Systems

2. Fault detection by mining association rules from housekeeping data;Yairi,2001

3. Deep clustering-based anomaly detection and health monitoring for satellite telemetry;Obied,2023

4. Anomaly Detection on Shuttle data using Unsupervised Learning Techniques

5. Combining OC-SVMs With LSTM for Detecting Anomalies in Telemetry Data With Irregular Intervals

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