TY - GEN
T1 - Combining Different Data Sources for IIoT-Based Process Monitoring
AU - Gomes, Rodrigo
AU - Amaral, Vasco
AU - Brito e Abreu, Fernando
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023/5/19
Y1 - 2023/5/19
N2 - Motivation—Industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency, as well as other economic benefits. IIoT provides more automation by using cloud computing to refine and optimize process controls. Problem—Detection and classification of events inside industrial settings for process monitoring often rely on input channels of various types (e.g. energy consumption, occupation data or noise) that are typically imprecise. However, the proper identification of events is fundamental for automatic monitoring processes in the industrial setting, allowing simulation and forecast for decision support. Methods—We have built a framework where process events are being collected in a classic cars restoration shop to detect the usage of equipment such as paint booths, sanders and polishers, using energy monitoring, temperature, humidity and vibration IoT sensors connected to a Wifi network. For that purpose, BLE beacons are used to locate cars being repaired within the shop floor plan. The InfluxDB is used for monitoring sensor data, and a server is used to perform operations on it, as well as run machine learning algorithms. Results—By combining location data and equipment being used, we are able to infer, using ML algorithms, some steps of the restoration process each classic car is going through. This detection contributes to the ability of car owners to remotely follow the restore process, thus reducing the carbon footprint and making the whole process more transparent.
AB - Motivation—Industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency, as well as other economic benefits. IIoT provides more automation by using cloud computing to refine and optimize process controls. Problem—Detection and classification of events inside industrial settings for process monitoring often rely on input channels of various types (e.g. energy consumption, occupation data or noise) that are typically imprecise. However, the proper identification of events is fundamental for automatic monitoring processes in the industrial setting, allowing simulation and forecast for decision support. Methods—We have built a framework where process events are being collected in a classic cars restoration shop to detect the usage of equipment such as paint booths, sanders and polishers, using energy monitoring, temperature, humidity and vibration IoT sensors connected to a Wifi network. For that purpose, BLE beacons are used to locate cars being repaired within the shop floor plan. The InfluxDB is used for monitoring sensor data, and a server is used to perform operations on it, as well as run machine learning algorithms. Results—By combining location data and equipment being used, we are able to infer, using ML algorithms, some steps of the restoration process each classic car is going through. This detection contributes to the ability of car owners to remotely follow the restore process, thus reducing the carbon footprint and making the whole process more transparent.
KW - Charter of Turin
KW - Classic cars restoration
KW - IIoT
KW - Indoor location
KW - Intrusive load monitoring
KW - IoT sensors
KW - Machine learning
KW - Process activity recognition
UR - http://www.scopus.com/inward/record.url?scp=85161099341&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-9331-2_10
DO - 10.1007/978-981-19-9331-2_10
M3 - Conference contribution
AN - SCOPUS:85161099341
SN - 978-981-19-9330-5
T3 - Lecture Notes in Networks and Systems
SP - 111
EP - 121
BT - Proceedings of International Conference on Information Technology and Applications
A2 - Anwar, Sajid
A2 - Ullah, Abrar
A2 - Rocha, Álvaro
A2 - Sousa, Maria José
PB - Springer
CY - Singapore
T2 - 16th International Conference on Information Technology and Applications, ICITA 2022
Y2 - 20 October 2022 through 22 October 2022
ER -