Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data

Anika Terbuch, Paul O'Leary, Peter Auer

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband

1 Zitat (Scopus)
OriginalspracheEnglisch
TitelI2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference
UntertitelInstrumentation and Measurement under Pandemic Constraints, Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
ISBN (elektronisch)978-1-6654-8360-5
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022 - Ottawa, Kanada
Dauer: 16 Mai 202219 Mai 2022

Publikationsreihe

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
ISSN (Print)1091-5281

Konferenz

Konferenz2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022
Land/GebietKanada
OrtOttawa
Zeitraum16/05/2219/05/22

Bibliographische Notiz

Funding Information:
ACKNOWLEDGMENT We wish to thank Vincent Winter and Alexander Zöhrer from the company Keller Grund-und Tiefbau GmbH for their continuing support in this project and for making data available. Also our gratitude goes to Stefan Herdy who contributed to the original developments in this application area. The authors gratefully acknowledge the financial support under the scope of the COMET program within the K2 Center “Integrated Computational Material, Process and Product Engineering (IC-MPPE)” (Project No 859480). This program is supported by the Austrian Federal Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and for Digital and Economic Affairs (BMDW), represented by the Austrian research funding association (FFG), and the federal states of Styria, Upper Austria and Tyrol.

Publisher Copyright:
© 2022 IEEE.

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