TY - GEN
T1 - Automated verification of measurement precision for internet-of-things equipment
AU - Brynildsen, Mikkel Haggren
AU - Brandt, Maja Miličić
AU - Klüwer, Johan Wilhelm
AU - Woods, Caitlin
AU - Hodkiewicz, Melinda R.
N1 - Publisher Copyright:
© 2023 Copyright for this paper by its authors.
PY - 2023
Y1 - 2023
N2 - Embedded sensors and processors built into assets enable condition monitoring data to be collected and used for asset health management and process optimization. Managing data storage, transmission, and security at all levels of the technology stack is crucial for original equipment manufacturers (OEM) and their customers. The federated nature of the product value chain with software and hardware components coming from external suppliers presents challenges when managing this data. The OEM must have assurance processes to confirm that software upgrades provided by their suppliers do not adversely affect the outputs delivered to customers. The output of sensor measurements have a precision determined by the storage space of data registers in a controller’s electronics. One technical challenge for OEMs is tracking changes and settings in software code that affect measurement precision when these settings are embedded in hardware components from external suppliers. For this we use a semantic asset model based on the Industrial Data Ontology (IDO). Entities that impact the reported precision are captured in the model.
AB - Embedded sensors and processors built into assets enable condition monitoring data to be collected and used for asset health management and process optimization. Managing data storage, transmission, and security at all levels of the technology stack is crucial for original equipment manufacturers (OEM) and their customers. The federated nature of the product value chain with software and hardware components coming from external suppliers presents challenges when managing this data. The OEM must have assurance processes to confirm that software upgrades provided by their suppliers do not adversely affect the outputs delivered to customers. The output of sensor measurements have a precision determined by the storage space of data registers in a controller’s electronics. One technical challenge for OEMs is tracking changes and settings in software code that affect measurement precision when these settings are embedded in hardware components from external suppliers. For this we use a semantic asset model based on the Industrial Data Ontology (IDO). Entities that impact the reported precision are captured in the model.
KW - IoT
KW - metadata
KW - ontology
KW - predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85184381123&partnerID=8YFLogxK
UR - https://dblp.org/db/conf/semweb/iswc2023p.html#BrynildsenBKWH23
M3 - Conference paper
VL - 3632
T3 - CEUR Workshop Proceedings
BT - Proceedings of the International Semantic Web Conference (ISWC)
PB - Central Europe Workshop Proceedings (CEUR-WS)
T2 - 22nd International Semantic Web Conference on Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2023
Y2 - 6 November 2023 through 10 November 2023
ER -