A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft

Abstract The fault signal characteristics of motor rolling bearings for more electric aircraft are easily masked by strong background noise. Directly using machine learning, deep learning, or other methods results in a lower accuracy in fault recognition. In this article, a Northern Goshawk algorith...

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Bibliographic Details
Main Authors: Dongsheng Yuan, Feng Liu, Zhonggang Yin, Yanqing Zhang, Yanping Zhang, Peien Luo
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:IET Electric Power Applications
Subjects:
AC motors
fault diagnosis
rolling bearings
Online Access:https://doi.org/10.1049/elp2.12508
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https://doi.org/10.1049/elp2.12508

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