Avtomatika i Telemekhanika
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor
Guidelines for authors
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Avtomat. i Telemekh.:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Avtomatika i Telemekhanika, 2021, Issue 8, Pages 3–38
DOI: https://doi.org/10.31857/S0005231021080018
(Mi at15768)
 

This article is cited in 2 scientific papers (total in 2 papers)

Surveys

Modern machine learning methods for telemetry-based spacecraft health monitoring

P. A. Mukhacheva, T. R. Sadretdinova, D. A. Pritykina, A. B. Ivanova, S. V. Solov'evb

a Skolkovo Institute of Science and Technology, Moscow, 121205 Russia
b Korolev Rocket and Space Corporation Energia, Korolev, Moscow oblast, 141070 Russia
Full-text PDF (704 kB) Citations (2)
References:
Abstract: We survey the progress in data mining methods for spacecraft health monitoring. The main emphasis is placed on the analysis of telemetry data enabling the identification of spacecraft states that are atypical during normal operation and the prediction of possible failures in the operation of the spacecraft or its components. The main stages required for the creation of general-purpose spacecraft state monitoring systems are considered; methods for detecting anomalies in telemetry data taking into account the specific features of the spacecraft are presented in detail; and publications on this topic known to the authors are analyzed. Examples of the implementation of such systems in flight control centers of various countries are given. The promising areas of development of methods for analyzing the technical state of complex systems relevant for solving problems in space technology are discussed, and the main factors that hinder the development of machine learning methods for analyzing telemetry data are noted.
Keywords: data mining, anomaly detection, flight control, technical diagnostics, telemetry data.
Presented by the member of Editorial Board: V. M. Glumov

Received: 04.12.2020
Revised: 04.01.2021
Accepted: 16.03.2021
English version:
Automation and Remote Control, 2021, Volume 82, Issue 8, Pages 1293–1320
DOI: https://doi.org/10.1134/S0005117921080014
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: P. A. Mukhachev, T. R. Sadretdinov, D. A. Pritykin, A. B. Ivanov, S. V. Solov'ev, “Modern machine learning methods for telemetry-based spacecraft health monitoring”, Avtomat. i Telemekh., 2021, no. 8, 3–38; Autom. Remote Control, 82:8 (2021), 1293–1320
Citation in format AMSBIB
\Bibitem{MukSadPri21}
\by P.~A.~Mukhachev, T.~R.~Sadretdinov, D.~A.~Pritykin, A.~B.~Ivanov, S.~V.~Solov'ev
\paper Modern machine learning methods for telemetry-based spacecraft health monitoring
\jour Avtomat. i Telemekh.
\yr 2021
\issue 8
\pages 3--38
\mathnet{http://mi.mathnet.ru/at15768}
\crossref{https://doi.org/10.31857/S0005231021080018}
\elib{https://elibrary.ru/item.asp?id=47103902}
\transl
\jour Autom. Remote Control
\yr 2021
\vol 82
\issue 8
\pages 1293--1320
\crossref{https://doi.org/10.1134/S0005117921080014}
\isi{https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=000700549500001}
\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85115219434}
Linking options:
  • https://www.mathnet.ru/eng/at15768
  • https://www.mathnet.ru/eng/at/y2021/i8/p3
  • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Avtomatika i Telemekhanika
    Statistics & downloads:
    Abstract page:193
    Full-text PDF :9
    References:31
    First page:28
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024