Spacecraft telemetry data monitoring by dimensionality reduction techniques

Takehisa Yairi, Minoru Inui, Akihiro Yoshiki, Yoshinobu Kawahara, Noboru Takata

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

13 Citations (Scopus)

Abstract

In this paper, we consider a "data-driven" anomaly detection framework for spacecraft systems using dimensionality reduction and reconstruction techniques. This method first learns a mapping from the original data space to a low dimensional space and its reverse mapping by applying linear or non-linear dimensionality reduction algorithms to a normal training data set. After the training, it applies the learned pair of mappings to a test data set to obtain a reconstructed data set, and then evaluate the reconstruction errors. We will show the results of applying several representative linear and non-lineardimensionality reduction algorithms with this framework to the electrical power subsystem (EPS) data of actual artificial satellites.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers
PublisherSociety of Instrument and Control Engineers (SICE)
Pages1230-1234
Number of pages5
ISBN (Print)9784907764364
Publication statusPublished - 2010
Externally publishedYes

Publication series

NameProceedings of the SICE Annual Conference

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Spacecraft telemetry data monitoring by dimensionality reduction techniques'. Together they form a unique fingerprint.

Cite this