Industrial IoT devices and cyber-physical production systems: review and use case
Type
bookPart
Publisher
Identifier
RÚBIO, E.M. ; DIONÍSIO, R.P. ; TORRES, P.M.B. (2019) - Industrial IoT devices and cyber-physical production systems: review and use case. In: MACHADO, J. ; SOARES, F. ; VEIGA, G. (eds) - Innovation, Engineering and Entrepreneurship. HELIX 2018. Lecture Notes in Electrical Engineering. Cham: Springer. ISBN 978-3-319-91334-6. Vol. 505, p. 292-298
978-3-319-91334-6
0.1007/978-3-319-91334-6_40
Title
Industrial IoT devices and cyber-physical production systems: review and use case
Subject
Cyber-physical systems
Industrial IoT
Smart factories
Machine learning
Predictive maintenance
Industrial IoT
Smart factories
Machine learning
Predictive maintenance
Date
2018-06-11T16:17:32Z
2020-12-31T01:30:21Z
2019
2020-12-31T01:30:21Z
2019
Description
“This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-91334-6_40"
The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, that runs in the smart devices to detect anomalies. The results corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults.
info:eu-repo/semantics/acceptedVersion
The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, that runs in the smart devices to detect anomalies. The results corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults.
info:eu-repo/semantics/acceptedVersion
Access restrictions
openAccess
Language
eng
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