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

Takehisa Yairi, Yoshinobu Kawahara, Ryohei Fujimaki, Yuichi Sato, Kazuo Machida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

49 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - SMC-IT 2006
Subtitle of host publication2nd IEEE International Conference on Space Mission Challenges for Information Technology
Pages466-473
Number of pages8
DOIs
Publication statusPublished - Dec 1 2006
Externally publishedYes
EventSMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology - Pasadena, CA, United States
Duration: Jul 17 2006Jul 20 2006

Publication series

NameProceedings - SMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology
Volume2006

Conference

ConferenceSMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology
CountryUnited States
CityPasadena, CA
Period7/17/067/20/06

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Aerospace Engineering

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