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

Anika Terbuch, Paul O'Leary, Peter Auer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Original language	English
Title of host publication	I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publication	Instrumentation and Measurement under Pandemic Constraints, Proceedings
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	978-1-6654-8360-5
DOIs	https://doi.org/10.1109/I2MTC48687.2022.9806663
Publication status	Published - 2022
Event	2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022 - Ottawa, Canada Duration: 16 May 2022 → 19 May 2022

Publication series

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

Conference

Conference	2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022
Country/Territory	Canada
City	Ottawa
Period	16/05/22 → 19/05/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Hybrid Learning
Outlier Detection
Time Series

Access to Document

10.1109/I2MTC48687.2022.9806663

Cite this

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",

series = "Conference Record - IEEE Instrumentation and Measurement Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers",

booktitle = "I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference",

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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, Canada, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

UR - http://www.scopus.com/inward/record.url?scp=85134430923&partnerID=8YFLogxK

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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Keywords

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

Cite this