Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network

Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network

Abstract Due to the uncertainty existing in the actual industrial environment, the rolling bearing compound fault features present coupling and complexity, which brings challenges to the compound fault feature extraction. To address this problem, this paper proposes a rolling bearing compound fault...

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Bibliographic Details
Main Authors: Runfang Hao, Yunpeng Bai, Kun Yang, Yongqiang Cheng, Shengjun Chang
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Compound fault
Adaptive multi-objective maximum correlated kurtosis deconvolution (AMOMCKD)
Convolutional neural network (CNN)
Non-dominated sorting genetic algorithm (NAGA-II)
Online Access:https://doi.org/10.1038/s41598-025-96106-3
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https://doi.org/10.1038/s41598-025-96106-3

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