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

Hadi Ashraf Raja, Hardik Raval, Toomas Vaimann, Ants Kallaste, Anton Rassolkin, Anouar Belahcen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
5 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoDiagnostika 2022 - 2022 International Conference on Diagnostics in Electrical Engineering, Proceedings
ToimittajatPavel Trnka
KustantajaIEEE
Sivumäärä4
ISBN (elektroninen)9781665480826
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Diagnostics in Electrical Engineering - Pilsen, Tshekki
Kesto: 6 syysk. 20228 syysk. 2022

Julkaisusarja

NimiDiagnostika 2022 - 2022 International Conference on Diagnostics in Electrical Engineering, Proceedings

Conference

ConferenceInternational Conference on Diagnostics in Electrical Engineering
LyhennettäDiagnostika
Maa/AlueTshekki
KaupunkiPilsen
Ajanjakso06/09/202208/09/2022

Sormenjälki

Sukella tutkimusaiheisiin 'Cost-efficient real-time condition monitoring and fault diagnostics system for BLDC motor using IoT and Machine learning'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä