Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology
With the rapid development of smart sensor technology and the Internet of things, ensuring data accuracy and system reliability is paramount. As the number of sensors increases with demand for high-resolution, high-quality input to decision-making systems, models and digital twins, manual quality co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2367 |
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| author | Astrid Marie Skålvik Ranveig N. Bjørk Enoc Martínez Kjell-Eivind Frøysa Camilla Saetre |
| author_facet | Astrid Marie Skålvik Ranveig N. Bjørk Enoc Martínez Kjell-Eivind Frøysa Camilla Saetre |
| author_sort | Astrid Marie Skålvik |
| collection | DOAJ |
| description | With the rapid development of smart sensor technology and the Internet of things, ensuring data accuracy and system reliability is paramount. As the number of sensors increases with demand for high-resolution, high-quality input to decision-making systems, models and digital twins, manual quality control of sensor data is no longer an option. In this paper, we leverage insights into sensor technology, environmental dynamics and the correlation between data from different sensors for automatic diagnostics of a sensor node. We propose a method for combining results of automatic quality control of individual sensors with tests for detecting simultaneous anomalies across sensors. Building on both sensor and application knowledge, we develop a diagnostic logic that can automatically explain and diagnose instead of just labeling the individual sensor data as “good” or “bad”. This approach enables us to provide diagnostics that offer a deeper understanding of the data and their quality and of the health and reliability of the measurement system. Our algorithms are adapted for real time and in situ operation on the sensor node. We demonstrate the diagnostic power of the algorithms on high-resolution measurements of temperature and conductivity from the OBSEA observatory about 50 km south of Barcelona, Spain. |
| format | Article |
| id | doaj-art-940468d16afb40fc8676af24e7a81d93 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-940468d16afb40fc8676af24e7a81d932025-08-20T02:56:59ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212236710.3390/jmse12122367Multivariate, Automatic Diagnostics Based on Insights into Sensor TechnologyAstrid Marie Skålvik0Ranveig N. Bjørk1Enoc Martínez2Kjell-Eivind Frøysa3Camilla Saetre4Department of Physics and Technology, University of Bergen, 5020 Bergen, NorwayNORCE Norwegian Research Center, 5838 Bergen, NorwaySARTI-MAR Research Group, Electronics Department, Universitat Politècnica de Catalunya (UPC), 08800 Vilanova i la Geltrú, SpainDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5020 Bergen, NorwayDepartment of Physics and Technology, University of Bergen, 5020 Bergen, NorwayWith the rapid development of smart sensor technology and the Internet of things, ensuring data accuracy and system reliability is paramount. As the number of sensors increases with demand for high-resolution, high-quality input to decision-making systems, models and digital twins, manual quality control of sensor data is no longer an option. In this paper, we leverage insights into sensor technology, environmental dynamics and the correlation between data from different sensors for automatic diagnostics of a sensor node. We propose a method for combining results of automatic quality control of individual sensors with tests for detecting simultaneous anomalies across sensors. Building on both sensor and application knowledge, we develop a diagnostic logic that can automatically explain and diagnose instead of just labeling the individual sensor data as “good” or “bad”. This approach enables us to provide diagnostics that offer a deeper understanding of the data and their quality and of the health and reliability of the measurement system. Our algorithms are adapted for real time and in situ operation on the sensor node. We demonstrate the diagnostic power of the algorithms on high-resolution measurements of temperature and conductivity from the OBSEA observatory about 50 km south of Barcelona, Spain.https://www.mdpi.com/2077-1312/12/12/2367environmental monitoringmeasurement errorsmeasurement uncertaintyocean salinityreliability fault diagnosticsself-validating |
| spellingShingle | Astrid Marie Skålvik Ranveig N. Bjørk Enoc Martínez Kjell-Eivind Frøysa Camilla Saetre Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology Journal of Marine Science and Engineering environmental monitoring measurement errors measurement uncertainty ocean salinity reliability fault diagnostics self-validating |
| title | Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology |
| title_full | Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology |
| title_fullStr | Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology |
| title_full_unstemmed | Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology |
| title_short | Multivariate, Automatic Diagnostics Based on Insights into Sensor Technology |
| title_sort | multivariate automatic diagnostics based on insights into sensor technology |
| topic | environmental monitoring measurement errors measurement uncertainty ocean salinity reliability fault diagnostics self-validating |
| url | https://www.mdpi.com/2077-1312/12/12/2367 |
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