Zhao, K, Xiao, J, Li, C, Xu, Z and Yue, M (2023) Fault diagnosis of rolling bearing using CNN and PCA fractal based feature extraction. Measurement: Journal of the International Measurement Confederation, 223. ISSN 0263-2241

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Abstract

A novel adaptive decomposition algorithm based on CEEMDAN and fractal dimension is proposed in this study to overcome limitations like redundancy and mode confusion in traditional EMD-based algorithms. An intelligent fault diagnosis model is developed using CNN and the proposed CEEMDAN to enhance rolling bearing state recognition. Sub-signals generated by CEEMDAN are selected and reconstructed using PCA and fractal dimension. In feature extraction and pattern recognition, the proposed Improve Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), coupled with CNN, extracts advanced features from the reconstructed signal for intelligent diagnosis. The methodology is validated through empirical experiments involving rolling bearings, where its superiority and reliability are compared with approaches based on CNN. The accuracy of this method reaches 99.79%

Item Type:	Article
Uncontrolled Keywords:	0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0913 Mechanical Engineering; Electrical & Electronic Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier BV
SWORD Depositor:	A Symplectic
Date Deposited:	19 Aug 2024 08:39
Last Modified:	30 Oct 2024 00:50
DOI or ID number:	10.1016/j.measurement.2023.113754
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23972

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