Fault diagnosis of rolling bearings based on mixed domain

DAI Haomin, XU Aiqiang, LI Wenfeng, SUN Weichao

Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (19) : 57-61.

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PDF(1614 KB)
Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (19) : 57-61.

Fault diagnosis of rolling bearings based on mixed domain

  • DAI Haomin, XU Aiqiang, LI Wenfeng, SUN Weichao
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Abstract

In order to improve the accuracy of rolling bearings fault diagnosis by making full use of effective features from time domain,frequency domain and time-frequency domain,a mixed domain feature built approach is proposed,which generate time domain and frequency domain features using the original signal, and extract permutation entropy of intrinsic mode function and singular values of the Hilbert spectrum as the time-frequency domain feature sets by empirical mode decomposition, making mixed domain feature set more fully and accurately reflect the bearing running than the single domain features. For the problem of mixed domain feature sets which have the shortcomings of too high dimension and serious redundancy, a feature selection method based on weighted minimal redundancy maximal relevance is proposed, which can select seven major feature vectors based on the classification accuracy of support vector machine. It can be shown from experiment results that: the classification accuracy of mixed domain feature selection can reach to 98% based on weighted minimal redundancy maximal relevance, and effectively identify the bearing fault information.

Key words

mixed domain / empirical mode decomposition / singular values of Hilbert spectrum / permutation entropy / weighted minimal redundancy maximal relevance

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DAI Haomin, XU Aiqiang, LI Wenfeng, SUN Weichao. Fault diagnosis of rolling bearings based on mixed domain[J]. Journal of Vibration and Shock, 2015, 34(19): 57-61

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