Cost-efficient real-time condition monitoring and fault diagnostics system for BLDC motor using IoT and Machine learning

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2022
Major/Subject
Mcode
Degree programme
Language
en
Pages
4
Series
Diagnostika 2022 - 2022 International Conference on Diagnostics in Electrical Engineering, Proceedings
Abstract
A cost-efficient condition monitoring and fault diagnostic system are presented in this paper using the Internet of Things and machine learning. Most condition monitoring systems nowadays are either costly or used to monitor current values without emphasizing the analysis part. On the other hand, predictive maintenance of different electrical machines, including BLDC motors, is becoming the need of the hour. It reduces the cost needed for maintenance and can also be used to evade more significant faults in the machine. The data is transmitted in real-time using a data acquisition system onto the cloud, which is further processed to determine if there is a chance of any fault occurring in the motor. A short comparison of the results of different machine learning algorithms is also discussed related to predictive maintenance.
Description
Funding Information: This research leading to these results has received funding from the PSG453, Digital twin for propulsion drive of autonomous electric vehicles” and ETAG21001, “Industrial internet methods for electrical energy conversion systems monitoring and diagnostics”. Publisher Copyright: © 2022 IEEE.
Keywords
condition monitoring, fault diagnostic, Internet of Things, IoT
Other note
Citation
Raja , H A , Raval , H , Vaimann , T , Kallaste , A , Rassolkin , A & Belahcen , A 2022 , Cost-efficient real-time condition monitoring and fault diagnostics system for BLDC motor using IoT and Machine learning . in P Trnka (ed.) , Diagnostika 2022 - 2022 International Conference on Diagnostics in Electrical Engineering, Proceedings . Diagnostika , IEEE , International Conference on Diagnostics in Electrical Engineering , Pilsen , Czech Republic , 06/09/2022 . https://doi.org/10.1109/Diagnostika55131.2022.9905102