TY - GEN
T1 - Application of Different Machine Learning Strategies for Current- And Vibration-based Motor Bearing Fault Detection in Induction Motors
AU - Rações, Hugo D. L.
AU - Ferreira, Fernando J. T. E.
AU - Pires, João M.
AU - Damásio, Carlos V.
N1 - info:eu-repo/grantAgreement/FCT/5876/147279/PT#
Sem PDF conforme despacho.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper, the application of different machine learning strategies for current- and vibration-based detection of bearing faults in squirrel-cage induction motors is studied. This study compares several feature extraction strategies such as a statistical and spectral analysis of vibration, a statistical analysis of the Hilbert's Transform envelope of vibration, an analysis of the currents deviation to a perfect sinusoid and a statistical and spectral analysis of the Park's Vector Modulus, with its performances being evaluated with the Support Vector Machine, Artificial Neural Network, Random Forests and Extreme Gradient Boosting algorithms. A comparison of results obtained using sampling frequencies of 0.8 kHz, 1 kHz, 2 kHz, 5 kHz and 10 kHz and analysis periods between 20 ms and 100 ms is made and promising models are achieved even with the lowest sampling frequencies.
AB - In this paper, the application of different machine learning strategies for current- and vibration-based detection of bearing faults in squirrel-cage induction motors is studied. This study compares several feature extraction strategies such as a statistical and spectral analysis of vibration, a statistical analysis of the Hilbert's Transform envelope of vibration, an analysis of the currents deviation to a perfect sinusoid and a statistical and spectral analysis of the Park's Vector Modulus, with its performances being evaluated with the Support Vector Machine, Artificial Neural Network, Random Forests and Extreme Gradient Boosting algorithms. A comparison of results obtained using sampling frequencies of 0.8 kHz, 1 kHz, 2 kHz, 5 kHz and 10 kHz and analysis periods between 20 ms and 100 ms is made and promising models are achieved even with the lowest sampling frequencies.
KW - bearing fault detection
KW - condition monitoring
KW - current analysis
KW - induction motor
KW - machine learning
KW - predictive maintenance
KW - vibration analysis
UR - http://www.scopus.com/inward/record.url?scp=85084005928&partnerID=8YFLogxK
U2 - 10.1109/IECON.2019.8927129
DO - 10.1109/IECON.2019.8927129
M3 - Conference contribution
AN - SCOPUS:85084005928
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 68
EP - 73
BT - Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Y2 - 14 October 2019 through 17 October 2019
ER -