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

Autor:	X. F. Tang
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	metallurgical machinery transmission diagnosis of faults intrinsic mode functions convolutional neural networks Mining engineering. Metallurgy TN1-997
Zdroj:	Metalurgija, Vol 63, Iss 3-4, Pp 350-352 (2024)
Druh dokumentu:	article
ISSN:	0543-5846 1334-2576 42640458
Popis:	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 transmission components faults. Our method achieves an impressive accuracy of 98,97 %. This robust technology significantly enhances the detection and diagnosis of transmission faults in metallurgical plant, providing an efficient solution for intelligent manufacturing applications.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/2cb55a0ea42640458318dd7a799d28b7 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF