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Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems

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5 Author(s)
Yairi, T. ; Res. Center for Adv. Sci. & Technol., Tokyo Univ. ; Kawahara, Y. ; Fujimaki, R. ; Sato, Y.
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For any space mission, safety and reliability are the most important issues. To tackle this problem, we have studied anomaly detection and fault diagnosis methods for spacecraft systems based on machine learning (ML) and data mining (DM) technology. In these methods, the knowledge or model which is necessary for monitoring a spacecraft system is (semi-)automatically acquired from the spacecraft telemetry data. In this paper, we first overview the anomaly detection/diagnosis problem in the spacecraft systems and conventional techniques such as limit-check, expert systems and model-based diagnosis. Then we explain the concept of ML/DM-based approach to this problem, and introduce several anomaly detection/diagnosis methods which have been developed by us

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Space Mission Challenges for Information Technology, 2006. SMC-IT 2006. Second IEEE International Conference on

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