基于DEMD和模糊熵的滚动轴承故障诊断方法研究

doi:10.3969/j.issn.1000-1158.2016.01.14

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Abstract A comprehensive rolling bearing fault diagnosis method combining differential-based empirical mode decomposition (DEMD) with fuzzy entropy and support vector machine(SVM) is proposed.Firstly, mechanical vibration signal is decomposed with differential-based empirical mode decomposition (DEMD) to obtain a certain number of intrinsic mode functions (IMFs) that have physical meaning. With a mutual relationship rule, the IMF components that have largest correlation coefficients with the original signal are sifted out. The fuzzy entropies of these IMFs are calculated and use as eigenvectors of fault signals, then the eigenvectors are put into SVM to identify the state of the rolling bearing. Compared with the method based on empirical mode decomposition (EMD) combined with fuzzy entropy and SVM, the experimental results show that the method of mechanical failure signals can accurately identify classification effectively.

Key words： metrology fault diagnosis rolling bearing differential-based empirical mode decomposition fuzzy entropy support vector machine

PACS:

TB936

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	Articles by authors
	MENG Zong
	JI Yan
	YAN Xiao-li

Cite this article:

MENG Zong,JI Yan,YAN Xiao-li. Rolling Bearing Fault Diagnosis Based on Differential-based Empirical Mode Decomposition and Fuzzy Entroy[J]. Acta Metrologica Sinica, 2016, 37(1): 56-61.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2016.01.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2016/V37/I1/56