Machine Learning-Based Online Multi-Fault Diagnosis for IMs Using Optimization Techniques With Stator Electrical and Vibration Data

Induction motors (IMs) have been commonly applied to industrial fields since the past decades; thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault diagnosis system for IMs based on the Random Forest (RF) and eXtreme Gradient Boosti...

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Veröffentlicht in:	IEEE transactions on energy conversion Jg. 39; H. 4; S. 2412 - 2424
Hauptverfasser:	Hsu, Shih-Hsien, Lee, Chien-Hsing, Wu, Wen-Fang, Jiang, Joe-Air
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Coils (windings) Data models Downtime Extreme gradient boosting algorithm Fault detection Fault diagnosis Feature extraction Induction motors Machine learning motor failure diagnosis Motors Optimization Optimization techniques Radio frequency random forest algorithm Rotors Stators Vibrations
ISSN:	0885-8969, 1558-0059
Online-Zugang:	Volltext
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