基于不完全Cholesky分解相关熵双谱的轴承故障诊断

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Abstract Aiming at the problem that the traditional bispectrum is difficult to deal with the strong noise interference and the lower computational efficiency of correntropy, a bearing fault diagnosis method based on incomplete Cholesky decomposition correlation correntropy and bispectrum analysis is proposed. Firstly, the incomplete Cholesky decomposition algorithm and kernel function are used to calculate the lower triangular matrix of the kernel matrix. Secondly, the Gini index is exploited to select the principal components of the lower triangular matrix. Thirdly, the lower rank approximation matrix of the kernel matrix is calculated using the principal components and the correntropy of the signal is obtained. Finally, the bispectrum based on correntropy of the vibration signal is computed and the bearing fault can be recognized according to the bispectrum feature of correntropy. The incomplete Cholesky decomposition algorithm and Gini index are used to calculate the correntropy of the vibration signal, which not only compresses the amount of data, highlights the transient impact characteristics of bearing fault, effectively suppresses the impact of noise, but also improves the calculation efficiency and reduces the computer memory consumption. The results of simulation and experiment show that the strong background noise will cause the failure of traditional bispectrum fault diagnosis method, while the proposed method based on correntropy and bispectrum analysis can extract the transient impact characteristics of bearing fault in the background of strong noise interference, and accurately identify bearing fault. Its performance is better than that of the traditional bispectrum and wavelet threshold denoising based bispectrum. The proposed correntropy based bispectrum method of is an effective method for bearing fault diagnosis.

Key words： Fault diagnosis Incomplete Cholesky decomposition Gini index Correntropy Bi-spectrum Bearing

Received: 01 March 2021 Published: 15 June 2022

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	LI Hui1
	HAO Rujiang2

Cite this article:

LI Hui1,HAO Rujiang2. Bearing fault diagnosis based on incomplete Cholesky decomposition correntropy and bi-spectrum[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(11): 123-140.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I11/123

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