Automated Behavior Labeling for IIoT Data
Authors: 
Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Sagar Sen, Phu H. NguyenAuthors Info & Claims
Pages 174 - 178
Abstract
We present an automated data analysis tool for IIoT applications that discovers process behavior patterns in sensor data. It takes time-varying sensor data from reference production cycles and performs clustering on summary statistic feature vectors derived from raw sensor data over configurable window sizes. It automatically labels the raw sensor data based on distinct behavior modes represented by the clusters. The tool wraps, as a web service deployed in a Docker container, the AI model represented by clusters/behavior modes discovered in the reference sensor data. We have successfully evaluated the tool over four industrial datasets. Demo video: https://www.youtube.com/watch?v=MhSnwPDnAh0.
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Information
Published In
November 2023
299 pages
ISBN:9798400708541
DOI:10.1145/3627050
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 March 2024
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- Demonstration
- Research
- Refereed limited
Funding Sources
- HEU Programme
- the Research Council of Norway
- H2020 Research and Innovation
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IoT 2023
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Overall Acceptance Rate 28 of 84 submissions, 33%
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