Induction Motor Failure Identification Based on Multiscale Acoustic Entropy Feature Selection and Hierarchical Adaptive Neuro-Fuzzy Inference System With Localized Recurrent Input-Reference-Cited by-同舟云学术

Induction Motor Failure Identification Based on Multiscale Acoustic Entropy Feature Selection and Hierarchical Adaptive Neuro-Fuzzy Inference System With Localized Recurrent Input

Published:2023-12-15 Issue:24 Volume:23 Page:30821-30834
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Xue Shenghao¹^ORCID,Hou Yaochun²^ORCID,Mi Junpeng¹^ORCID,Zhou Changqing²,Xiang Tian³,He Weiting⁴,Wu Dazhuan²^ORCID,Huang Wenjun¹

Affiliation:

1. College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. College of Energy Engineering, Zhejiang University, Hangzhou, China

3. Intelligent Robot Research Center, Zhejiang Laboratory, Hangzhou, China

4. Zhejiang Supcon Technology Company Ltd., Hangzhou, China

Funder

Key Research and Development Program of Zhejiang Province

Key Technology and Application of Wind Turbine Main Control System

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10360293/10313247.pdf?arnumber=10313247

Reference35 articles.

1. Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals

2. Bearing Fault Detection in Three-Phase Induction Motors Using Support Vector Machine and Fiber Bragg Grating

3. Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system

4. Automatic representation and detection of fault bearings in in-wheel motors under variable load conditions

5. Induction Motor Fault Classification Based on ROC Curve and t-SNE

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