Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults

Yichuan Fu; Zhiwei Gao; Yu Zhang; Aihua Zhang; Xiuxia Yin

doi:10.1109/RCAE53607.2021.9638921

Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults

Yichuan Fu, Zhiwei Gao, Yu Zhang, Aihua Zhang, Xiuxia Yin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
Publisher	IEEE
Pages	306-314
Number of pages	9
ISBN (Electronic)	9781665427302
DOIs	https://doi.org/10.1109/RCAE53607.2021.9638921
Publication status	Published - 2021
Event	4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 - Wuhan, China Duration: 4 Nov 2021 → 6 Nov 2021

Publication series

Name	2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021

Conference

Conference	4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
Country/Territory	China
City	Wuhan
Period	4/11/21 → 6/11/21

Keywords

Data-driven
expectation maximisation principal component analysis
fault classification
stator inter-turn faults
support vector machine
t-distributed stochastic neighbour embedding
three-phase induction machines

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/RCAE53607.2021.9638921

Cite this

Fu, Y., Gao, Z., Zhang, Y., Zhang, A., & Yin, X. (2021). Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults. In 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 (pp. 306-314). (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021). IEEE. https://doi.org/10.1109/RCAE53607.2021.9638921

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title = "Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults",

abstract = "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. ",

keywords = "Data-driven, expectation maximisation principal component analysis, fault classification, stator inter-turn faults, support vector machine, t-distributed stochastic neighbour embedding, three-phase induction machines",

author = "Yichuan Fu and Zhiwei Gao and Yu Zhang and Aihua Zhang and Xiuxia Yin",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 ; Conference date: 04-11-2021 Through 06-11-2021",

year = "2021",

doi = "10.1109/RCAE53607.2021.9638921",

language = "English",

series = "2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021",

publisher = "IEEE",

pages = "306--314",

booktitle = "2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021",

address = "United States",

}

Fu, Y, Gao, Z, Zhang, Y, Zhang, A & Yin, X 2021, Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults. in 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021, IEEE, pp. 306-314, 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021, Wuhan, China, 4/11/21. https://doi.org/10.1109/RCAE53607.2021.9638921

Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults. / Fu, Yichuan; Gao, Zhiwei; Zhang, Yu et al.
2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. IEEE, 2021. p. 306-314 (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85123775610

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 - IEEE

T2 - 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021

Y2 - 4 November 2021 through 6 November 2021

ER -

Fu Y, Gao Z, Zhang Y, Zhang A, Yin X. Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults. In 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. IEEE. 2021. p. 306-314. (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021). doi: 10.1109/RCAE53607.2021.9638921

Data-Driven Fault Classification for Three-Phase Induction Machines under Stator Inter-Turn Faults

Abstract

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Keywords

UN SDGs

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