LAPSE:2023.11209
Published Article
LAPSE:2023.11209
Signal Processing Methods of Enhanced Magnetic Memory Testing
Xu Luo, Lihong Wang, Shufeng Cao, Qiuhan Xiao, Hongjuan Yang, Jianguo Zhao
February 27, 2023
As a particular kind of detection technology under weak magnetization, metal magnetic memory testing is very likely to be affected by external factors in the detecting process, which may lead to incorrect results. In order to minimize the negative influence of interrupting signals and improve the detection accuracy, this paper adopted the enhanced metal magnetic memory testing method to preliminarily increase the signal-to-noise ratio (SNR) of the detection signal and then compares the denoising effects of wavelet threshold denoising method, empirical mode decomposition (EMD) denoising method, EMD-wavelet threshold denoising method, ensemble EMD (EEMD), complementary EEMD (CEEMD), variational mode decomposition (VMD), local mean decomposition (LMD) and empirical wavelet transform (EWT) on the detection signal and the gradient signal respectively. The results show that the enhanced metal magnetic memory testing method can significantly increase the SNR of the obtained signal and cannot improve the SNR of a gradient signal which is generated from the obtained signal. The different denoising methods can further boost the SNR and improve the detection accuracy of the obtained signal and the gradient signal. Among the eight signal processing methods, wavelet threshold, EMD and its improved methods are more applicable in the denoising of enhanced metal magnetic memory testing signals. The Wavelet threshold denoising, EMD-wavelet threshold denoising and EEMD denoising all have good denoising effects, and the denoising results to the same signal are analogous.
Keywords
EEMD denoising, EMD-wavelet threshold denoising, enhanced metal magnetic memory testing, signal processing, wavelet threshold denoising
Suggested Citation
Luo X, Wang L, Cao S, Xiao Q, Yang H, Zhao J. Signal Processing Methods of Enhanced Magnetic Memory Testing. (2023). LAPSE:2023.11209
Author Affiliations
Luo X: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
Wang L: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China [ORCID]
Cao S: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
Xiao Q: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China [ORCID]
Yang H: School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
Zhao J: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Journal Name
Processes
Volume
11
Issue
2
First Page
302
Year
2023
Publication Date
2023-01-17
Published Version
ISSN
2227-9717
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PII: pr11020302, Publication Type: Journal Article
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LAPSE:2023.11209
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doi:10.3390/pr11020302
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Feb 27, 2023
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