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

Anika Terbuch; Paul O'Leary; Peter Auer

doi:10.1109/I2MTC48687.2022.9806663

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

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband

1 Zitat (Scopus)

Originalsprache	Englisch
Titel	I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference
Untertitel	Instrumentation and Measurement under Pandemic Constraints, Proceedings
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers
ISBN (elektronisch)	978-1-6654-8360-5
DOIs	https://doi.org/10.1109/I2MTC48687.2022.9806663
Publikationsstatus	Veröffentlicht - 2022
Veranstaltung	2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022 - Ottawa, Kanada Dauer: 16 Mai 2022 → 19 Mai 2022

Publikationsreihe

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

Konferenz

Konferenz	2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022
Land/Gebiet	Kanada
Ort	Ottawa
Zeitraum	16/05/22 → 19/05/22

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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10.1109/I2MTC48687.2022.9806663

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Verknüpfung zur Publikation in Scopus

Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data
Anika Terbuch (Redner)
19 Mai 2022
Aktivität: Gespräch oder Vortrag › Mitwirkung als Gastredner/-in

Dieses zitieren

Terbuch, A., O'Leary, P., & Auer, P. (2022). Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data. in I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement under Pandemic Constraints, Proceedings (Conference Record - IEEE Instrumentation and Measurement Technology Conference). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/I2MTC48687.2022.9806663

Terbuch, Anika ; O'Leary, Paul ; Auer, Peter. / Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data. I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement under Pandemic Constraints, Proceedings. Institute of Electrical and Electronics Engineers, 2022. (Conference Record - IEEE Instrumentation and Measurement Technology Conference).

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title = "Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data",

keywords = "Hybrid Learning, Outlier Detection, Time Series",

author = "Anika Terbuch and Paul O'Leary and Peter Auer",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022 ; Conference date: 16-05-2022 Through 19-05-2022",

year = "2022",

doi = "10.1109/I2MTC48687.2022.9806663",

language = "English",

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Terbuch, A , O'Leary, P & Auer, P 2022, Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data. in I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement under Pandemic Constraints, Proceedings. Conference Record - IEEE Instrumentation and Measurement Technology Conference, Institute of Electrical and Electronics Engineers, 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022, Ottawa, Kanada, 16/05/22. https://doi.org/10.1109/I2MTC48687.2022.9806663

Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data. / Terbuch, Anika ; O'Leary, Paul ; Auer, Peter.
I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement under Pandemic Constraints, Proceedings. Institute of Electrical and Electronics Engineers, 2022. (Conference Record - IEEE Instrumentation and Measurement Technology Conference).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband

TY - GEN

T1 - Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data

AU - Terbuch, Anika

AU - O'Leary, Paul

AU - Auer, Peter

PY - 2022

Y1 - 2022

KW - Hybrid Learning

KW - Outlier Detection

KW - Time Series

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U2 - 10.1109/I2MTC48687.2022.9806663

DO - 10.1109/I2MTC48687.2022.9806663

M3 - Conference contribution

AN - SCOPUS:85134430923

T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference

BT - I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference

PB - Institute of Electrical and Electronics Engineers

T2 - 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022

Y2 - 16 May 2022 through 19 May 2022

ER -

Terbuch A , O'Leary P , Auer P. Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data. in I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement under Pandemic Constraints, Proceedings. Institute of Electrical and Electronics Engineers. 2022. (Conference Record - IEEE Instrumentation and Measurement Technology Conference). Epub 2022. doi: 10.1109/I2MTC48687.2022.9806663

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

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

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