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12 September 2024 Rolling bearing fault feature extraction based on variational mode decomposition and maximum correlated kurtosis deconvolution parameter optimization
Ruilin Wang, Wei Wang, Qiangqing Liu, Ting Fang, Hongkai Zhang
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Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132561Z (2024) https://doi.org/10.1117/12.3037842
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China
Abstract
Aiming at the problem that rolling bearing fault signals are easily interfered by strong background noise, we propose a method that combines the parameter-optimized Variational Mode Decomposition (VMD) and implementation of Maximum Correlated Kurtosis Deconvolution (MCKD) is used for extracting rolling bearing fault features. (MCKD) combined method for extracting features of faults in rolling bearings. Firstly, PSO is applied to optimize the parameters within VMD and select the optimal modal components; then, the parameters in MCKD are optimized using PSO and MCKD is used to strengthen the fault impact components in the optimal component signals; the envelope spectrum is eventually employed for identifying the eigenfrequencies of bearing faults. Experiments show that the proposed method can adaptively enhance the shock component in rolling bearing faults, and can effectively extract characteristics of rolling bearing faults under significant amount of background noise.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ruilin Wang, Wei Wang, Qiangqing Liu, Ting Fang, and Hongkai Zhang "Rolling bearing fault feature extraction based on variational mode decomposition and maximum correlated kurtosis deconvolution parameter optimization", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132561Z (12 September 2024); https://doi.org/10.1117/12.3037842
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KEYWORDS
Feature extraction
Particle swarm optimization
Modal decomposition
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