De-noising algorithm of vibration signals based on quantum Gaussian mixture model

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (11) : 235-241.

YANG Wangcan1, ZHANG Peilin2, CHEN Yanlong3, WU Dinghai2, LI Haiping4

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Abstract

Vibration signals of machinery equipment are often disturbed by background noise to cause machinery equipment’s fault features being not obvious. Here, a de-noising algorithm of vibration signals based on quantum Gaussian mixture model was proposed. Firstly, the dual-tree complex wavelet packet transform was performed on vibration signals, and Gaussian mixture model was established for dual-tree complex wavelet packet coefficients. According to Bayesian maximum posteriori estimation criterion, shrinkage function of dual-tree complex wavelet packet coefficients was acquired. Then the spatial correlation between dual-tree complex wavelet packet coefficients’ father generation and child one was used to combine the quantum superposition state theory, and calculate appearing probabilities of noise signal and useful one’ wavelet coefficients, respectively. Lastly, the shrinkage function of dual-tree complex wavelet packet coefficients was adjusted with probability parameters achieved with the quantum superposition state theory to make wavelet packet coefficients shrink adaptively and nonlinearly, and the local adaptability of Gaussian mixture model was improved to realize machinery vibration signals’ de-noising processing. The test results of simulated signals and measured planetary gearbox vibration signals indicated that this proposed method can be used to effectively get rid of noise in vibration signals and highlight fault state features of machinery equipment.

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denoising process / gaussian mixture model / quantum theory / vibration signal / dual-tree complex wavelet packet transform

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YANG Wangcan1, ZHANG Peilin2, CHEN Yanlong3, WU Dinghai2, LI Haiping4. De-noising algorithm of vibration signals based on quantum Gaussian mixture model[J]. Journal of Vibration and Shock, 2019, 38(11): 235-241

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