Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8629
Main Authors: Mao, Meijiao, Zeng, Kaixin, Tan, Zhifei, Zeng, Zhi, Hu, Zihua, Chen, Xiaogao, Qin, Changjiang
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
arithmetic optimization algorithm
Fault diagnosis
Fourier transforms
K-singular value decomposition
Mathematical optimization
Noise control
Optimization algorithms
rolling bearing
Signal processing
Thrust bearings
variational mode decomposition
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Summary:To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23208629