OVMD-MPE群稀疏全变分去噪算法研究

doi:10.3969/j.issn.1000-1158.2022.01.08

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Abstract Bearing vibration data is susceptible to noise interference during the acquisition process and cannot effectively highlight weak local fault pulses, thereby affecting the efficiency of bearing fault diagnosis. To solve this problem, an ovmd-mpe group sparse total variational denoising algorithm (OVMD-MPE-GSTVD) is proposed. Firstly, variational model decomposition is used to decompose the signal, and then the optimal parameters of variational model decomposition are obtained by grasshopper optimization algorithm. Then, calculate the empirical model decomposition of each modal component to separate the noise dominant component and the useful component. Finally, the dominant component of the noise is filtered by group sparse total variational denoising algorithm, and the filtered component and useful component are combined to reconstruct the denoising signal. The experimental results show that compared with the traditional denoising method, the average signal-to-noise ratio of the simulated reconstructed signal is improved by about 3.3dB, the bearing data fault accuracy is increased to 98.9%.

Key words： metrology fault diagnosis rolling bearings variational modal decomposition multi-scale permutation entropy group sparse total variational denoising

Received: 15 April 2020 Published: 06 January 2022

PACS:	TB936
	TB973

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	Articles by authors
	CHEN Wei-xing
	SUN Xi-xi

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CHEN Wei-xing,SUN Xi-xi. Reasearch on OVMD-MPE Group Sparsity Total Variational Denoising Algorithm[J]. Acta Metrologica Sinica, 2022, 43(1): 48-56.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.01.08 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I1/48

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