TY - GEN
T1 - A Low-Cost Remote Asset Monitoring Solution Through Energy Consumption
AU - Obidike, Obiefuna
AU - Alexoulis, Aris
AU - Ateeq, Muhammad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This study introduces a cost-effective methodology for remote asset monitoring by utilising energy usage as a key indicator. The study highlights the integration of several technologies such as the Industrial Internet of Things (IIoT), Big Data, Cloud computing, Artificial Intelligence (AI), and Cyber-physical systems in the context of Industry 4.0 and Smart Manufacturing. This study undertakes a comprehensive examination of literature to elucidate the fundamental ideas underlying Smart Manufacturing, the use of sensor-driven insights, and the importance of predictive maintenance. The methodology section presents a comprehensive framework that incorporates emonBase, Node-RED, Siemens Insights Hub, and Mendix. The verification findings demonstrate a notable average error rate of 2.68%, hence confirming the system's high accuracy level. The success of the project highlights its capacity for efficient and economical remote asset monitoring. In summary, this paper highlights the achievements of the project and presents opportunities for predictive analysis and operational enhancement, therefore providing a significant contribution to the field of Smart Manufacturing and remote asset management.
AB - This study introduces a cost-effective methodology for remote asset monitoring by utilising energy usage as a key indicator. The study highlights the integration of several technologies such as the Industrial Internet of Things (IIoT), Big Data, Cloud computing, Artificial Intelligence (AI), and Cyber-physical systems in the context of Industry 4.0 and Smart Manufacturing. This study undertakes a comprehensive examination of literature to elucidate the fundamental ideas underlying Smart Manufacturing, the use of sensor-driven insights, and the importance of predictive maintenance. The methodology section presents a comprehensive framework that incorporates emonBase, Node-RED, Siemens Insights Hub, and Mendix. The verification findings demonstrate a notable average error rate of 2.68%, hence confirming the system's high accuracy level. The success of the project highlights its capacity for efficient and economical remote asset monitoring. In summary, this paper highlights the achievements of the project and presents opportunities for predictive analysis and operational enhancement, therefore providing a significant contribution to the field of Smart Manufacturing and remote asset management.
KW - Analysis
KW - IIoT
KW - Industry 4.0
KW - Remote Asset Monitoring
KW - Smart Energy monitoring
KW - Smart Manufacturing
KW - Smart Systems
UR - http://www.scopus.com/inward/record.url?scp=85189345544&partnerID=8YFLogxK
U2 - 10.1109/DeSE60595.2023.10468767
DO - 10.1109/DeSE60595.2023.10468767
M3 - Conference Proceeding
AN - SCOPUS:85189345544
T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE
SP - 270
EP - 275
BT - DeSE 2023 - Proceedings
A2 - Obe, Dhiya Al-Jumeily
A2 - Assi, Sulaf
A2 - Jayabalan, Manoj
A2 - Hind, Jade
A2 - Hussain, Abir
A2 - Tawfik, Hissam
A2 - Rowe, Neil
A2 - Mustafina, Jamila
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Developments in eSystems Engineering, DeSE 2023
Y2 - 18 December 2023 through 20 December 2023
ER -
