Integrating empirical mode decomposition and convolutional neural network for efficient fault diagnosis in metallurgical machinery

Integrating empirical mode decomposition and convolutional neural network for efficient fault diagnosis in metallurgical machinery

The paper introduces an innovative framework for rotating machinery fault recognition by combining Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). This novel approach integrates feature extraction and selection, utilizing deep learning for precise classification of transmi...

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Bibliographic Details
Main Author:	X. F. Tang
Format:	Article
Language:	English
Published:	Croatian Metallurgical Society 2024-01-01
Series:	Metalurgija
Subjects:	metallurgical machinery transmission, diagnosis of faults intrinsic mode functions convolutional neural networks
Online Access:	https://hrcak.srce.hr/file/456135

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