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Autonomous Anomaly Detection and Handling of Spatiotemporal Railway Data
Kulahci, Murat
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.
ORCID iD:
0000-0003-4222-9631
Bergquist, Bjarne
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering.
ORCID iD:
0000-0003-3911-8009
Söderholm, Peter
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. The Swedish Transport Administration.
ORCID iD:
0000-0002-6479-9101
2022 (English)
In:
International Congress and Workshop on Industrial AI 2021 / [ed] Ramin Karim; Alireza Ahmadi; Iman Soleimanmeigouni; Ravdeep Kour; Raj Rao, Springer, 2022, Vol. 1, p. 65-72
Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2022. Vol. 1, p. 65-72
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
Keywords [en]
Data cleansing, Data filtering, Outlier detection, Prognostics, Time series analysis
National Category
Other Civil Engineering
Research subject
Quality technology and logistics
Identifiers
URN:
urn:nbn:se:ltu:diva-89297
DOI:
10.1007/978-3-030-93639-6_6
ISI:
000777604600006
Scopus ID:
2-s2.0-85125261833
OAI: oai:DiVA.org:ltu-89297
DiVA, id:
diva2:1638036
Conference
International Congress and Workshop on Industrial AI (IAI 2021), Luleå, Sweden, October 5-7, 2021
Note
ISBN för värdpublikation: 978-3-030-93638-9, 978-3-030-93639-6
Available from:
2022-02-15
Created:
2022-02-15
Last updated:
2022-07-05
Bibliographically approved
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Kulahci, Murat
Bergquist, Bjarne
Söderholm, Peter
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