双小波非凸稀疏正则化去噪算法研究

doi:10.3969/j.issn.1000-1158.2021.01.14

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Abstract The aircraft engine ECT oil monitoring data is affected by various noises during the acquisition and transmission process, which affects the extraction of effective features of the data. A double wavelet denoising (DWAD) algorithm is proposed for this problem. In the two wavelet domains, the data is decomposed, filtered by the threshold function, and the difference of the wavelet coefficient distribution is used to force the denoising signals in the two wavelet domains to be the same. The inverse tangent function is used as a penalty function to obtain a more sparse coefficient expression. The experimental results show that compared with the traditional wavelet denoising method, the average signal-to-noise ratio of continuous signal and step signal is improved by 2.3dB and 4.2dB, the denoising effect is optimized.

Key words： metrology oil monitoring ECT data double wavelet denoising arctangent function

Received: 08 April 2019 Published: 19 January 2021

PACS:

TB973

Fund:The National Natural Science Foundation of China

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	MA Min
	WANG Tao

Cite this article:

MA Min,WANG Tao. Research on Double Wavelet Nonconvex Sparse Regularization Denoising Algorithm[J]. Acta Metrologica Sinica, 2021, 42(1): 85-90.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.01.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I1/85

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