Fault diagnosis of rolling bearings based on sfla optimized variational mode extraction

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (10) : 132-139.

ZHANG Huaibin1，CHEN Zhigang1,2，YANG Yuanpeng1，WANG Yanxue1

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Abstract

In order to solve problems that the selection of approximate center frequency and penalty factor in variational mode extraction (VME) depends too much on experts’ experience, a rolling bearing fault diagnosis method based on shuffled frog leaping algorithm (SFLA) and VME was proposed. Firstly, in order to solve problems of incomplete information when using a single index as an objective function to extract features, a new parameter optimization index-KIC was established with combining information entropy(IE), envelope spectral kurtosis and correlation coefficient. Then, the minimum value of KIC was used as the objective function of SFLA to adaptively select the approximate center frequency and penalty factor expected modes of VME. Finally, expected modes were analyzed with envelope demodulation for fault diagnosis. The analysis results of simulation signals and the related data sets of the bearing test-bed show that the proposed SFLA-VME method can accurately extract the desired mode and diagnosis of the bearing fault.

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rolling bearing / variational mode extraction / shuffled frog leaping algorithm / envelope spectral kurtosis / information entropy

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ZHANG Huaibin1，CHEN Zhigang1,2，YANG Yuanpeng1，WANG Yanxue1. Fault diagnosis of rolling bearings based on sfla optimized variational mode extraction[J]. Journal of Vibration and Shock, 2024, 43(10): 132-139

References

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