多源声发射信号混合重叠组稀疏分类研究

doi:10.3969/j.issn.1000-1158.2024.01.10

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Abstract Acoustic emission detection of high-speed train body cracks involving multiple sources, overlapping wave modes, and noise interference, a mixed overlapping group sparse (MOGS) classification method with intrinsic mode function (IMF) is proposed for the identification of acoustic emission sources. MOGS is a structured sparse model that involve inter-and intra-group sparsity while allowing feature overlap between classes. A new noise pre-decomposition matrix is designed to reduce the computational complexity of IMFs. The IMF that include eigenfrequencies was selected as the sample to improve the difference between classes. MOGS dictionary was trained by the K-SVD with hierarchical group sparse lasso penalty function, and a separable block coordinates with approximate smoothing process method was proposed to solve MOGS lasso penalty function. Experiments show that the classification accuracy of this method is higher than 80%, the identification rate and waveform reconstruction effect are better than other algorithms.

Key words： acoustic metrology;acoustic emission;group sparse representation-based classification;mixed overlapping group sparse multi-source signal recognition

Received: 20 September 2022 Published: 22 January 2024

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TB95

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	Articles by authors
	DENG Tao
	LIU Zhechao
	WANG Huazhang
	HE Lei

Cite this article:

DENG Tao,LIU Zhechao,WANG Huazhang, et al. Research on Mixed Overlapping Group Sparse for Multi-source Acoustic Emission Signal[J]. Acta Metrologica Sinica, 2024, 45(1): 64-72.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.01.10 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I1/64

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