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A Data-Driven Approach based on Tensor Completion for Replacing "Physical Sensors" with "Virtual Sensors"
Halmstad University, School of Information Technology.ORCID iD: 0000-0001-8413-963x
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-2590-6661
2021 (English)In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), IEEE conference proceedings, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Sensors are being used in many industrial applications for equipment health monitoring and anomaly detection. However, sometimes operation and maintenance of these sensors are costly. Thus companies are interested in reducing the number of required sensors as much as possible. The straightforward solution is to check the prediction power of sensors and eliminate those sensors with limited prediction capabilities. However, this is not an optimal solution because if we discard the identified sensors. As a result, their historical data also will not be utilized anymore. However, typically such historical data can help improve the remaining sensors' signal power, and abolishing them does not seem the right solution. Therefore, we propose the first data-driven approach based on tensor completion for re-utilizing data of removed sensors and the remaining sensors to create virtual sensors. We applied the proposed method on vibration sensors of high-speed separators, operating with five sensors. The producer company was interested in reducing the sensors to two. But with the aid of tensor completion-based virtual sensors, we show that we can safely keep only one sensor and use four virtual sensors that give almost equal detection power when we keep only two physical sensors.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2021.
Keywords [en]
tensor completion, virtual sensors, missing value estimation, post-correction
National Category
Signal Processing Information Systems Control Engineering
Identifiers
URN: urn:nbn:se:hh:diva-46197DOI: 10.1109/DSAA53316.2021.9564118ISI: 000783799800009Scopus ID: 2-s2.0-85126125202ISBN: 978-1-6654-2099-0 (electronic)ISBN: 978-1-6654-2100-3 (print)OAI: oai:DiVA.org:hh-46197DiVA, id: diva2:1627150
Conference
2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021
Projects
Project #280033, KK foundation, Strategic recruitment, Assistant Professor in information technology with focus on data mining.Available from: 2022-01-12 Created: 2022-01-12 Last updated: 2023-10-05Bibliographically approved

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Fanaee Tork, HadiRahat, Mahmoud

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf