TY - GEN
T1 - Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults
AU - Fu, Yichuan
AU - Gao, Zhiwei
AU - Zhang, Yu
AU - Zhang, Aihua
AU - Yin, Xiuxia
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Data-driven fault classification for induction machines has received much attention in electric drives. In this study, a data-driven and supervised machine learning-based fault classification technique is addressed by integrating t-distributed stochastic neighbour embedding (t-SNE) and support vector machine (SVM) to evaluate the feasibility and capability of the classification performances. The algorithm proposed is applied to the three-phase induction machine control systems subjected to stator inter-turn faults, including single phase and multi-phase faults with different values of fault ratios. Finally, intensive simulations and comparison studies are presented to validate the classification method.
AB - Data-driven fault classification for induction machines has received much attention in electric drives. In this study, a data-driven and supervised machine learning-based fault classification technique is addressed by integrating t-distributed stochastic neighbour embedding (t-SNE) and support vector machine (SVM) to evaluate the feasibility and capability of the classification performances. The algorithm proposed is applied to the three-phase induction machine control systems subjected to stator inter-turn faults, including single phase and multi-phase faults with different values of fault ratios. Finally, intensive simulations and comparison studies are presented to validate the classification method.
KW - Data-driven
KW - expectation maximisation principal component analysis
KW - fault classification
KW - stator inter-turn faults
KW - support vector machine
KW - t-distributed stochastic neighbour embedding
KW - three-phase induction machines
UR - http://www.scopus.com/inward/record.url?scp=85123775610&partnerID=8YFLogxK
U2 - 10.1109/RCAE53607.2021.9638921
DO - 10.1109/RCAE53607.2021.9638921
M3 - Conference contribution
AN - SCOPUS:85123775610
T3 - 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
SP - 306
EP - 314
BT - 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
Y2 - 4 November 2021 through 6 November 2021
ER -
