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Improved variational mode decomposition method with adaptive parameter selection

Published:2024-06-27 author:LI Zhiqiang, LI Dewen, ZUO Hongfu, et al. Browse: 37 Check PDF documents

Improved variational mode decomposition method with

adaptive parameter selection

LI Zhiqiang1, LI Dewen1, ZUO Hongfu2, CAI Jing2, ZHANG Ying1

(1.College of Automotive and Transportation Engineering, Nanjing Forestry University, Nanjing 210037, China;

2.College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract: In response to the difficulty in determining the number of modes and penalty parameters in traditional variational mode decomposition (VMD) methods, an improved variational mode decomposition method with adaptive parameter selection was proposed. Firstly, the impact and periodicity characteristics of faults were comprehensively considered, and a weighted spectral peak ratio (WSPR) index was constructed based on the Gini index and spectral peak ratio index. Secondly, in order to obtain the optimal combination of mode number and penalty parameter, the African vulture optimization algorithm (AVOA) was used to iteratively optimize the mode number and penalty parameter of the variational mode decomposition method, which overcame the drawbacks of subjective parameter selection. The constructed weighted spectral peak to peak ratio index could not only serve as the objective function for optimizing the parameters of the African vulture optimization algorithm, but also adaptively select intrinsic modal functions after signal decomposition using variational modal methods. Finally, the selected optimal intrinsic modal functions were subjected to envelope demodulation analysis to extract early fault characteristics of rolling bearing faults. The method was validated using simulated signals, single-fault rolling bearing test signals, and composite-fault rolling bearing test signals. The experimental results show that the method can accurately extract the fault frequency (100 Hz) of the simulated signal, the fault frequency (236.4 Hz) of the single fault signal, and the fault frequency (inner ring fault frequency 149.14 Hz, outer ring fault frequency 86.39 Hz) of the composite fault signal. In comparison with other methods and indicators, the spectral peaks of the fault characteristic frequency and its multiple frequencies in the optimal IMF envelope spectrum are more prominent and obvious, with higher accuracy and stronger robustness. The research results show that the method can effectively extract the weak features of the early fault signal of the bearing and achieve accurate identification of the fault type.

Key words: rolling bearing; early fault diagnosis; variational mode decomposition(VMD); number of modes; penalty parameters; African vulture optimization algorithm(AVOA); weighted spectrum peak ratio(WSPR) index

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