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Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy
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DOI：10.1016/j.measurement.2021.109445.png)
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摘要
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The vibration signals obtained from the bearings are associated with nonlinear and nonstationary characteristics, which hinder the fault identification of the bearing. A scheme of fault identification is proposed to identify the faults in bearing in this paper. The vibration signal first pre-processed by wavelet packet transform, followed by Hilbert-Huang transform, decomposes the reconstructed signal into the IMFs. The L-moment criterion is used to select a prominent IMF. The prominent IMF is further utilized to extract the features from the processed signal. The dataset prepared from features trains the SVM model. A novel optimization technique viz 'Diversity driven multi-parent evolutionary algorithm with adaptive wavelet mutation' is proposed to optimize the SVM parameters to increase its efficiency. The efficacy of the proposed algorithm is tested against benchmark functions; obtained results proved its effectiveness. The overall accuracy of built SVM is found to be 100% with suitable regularization and kernel parameter values.
Keyword:
L-moment
Wavelet mutation
SVM
Hilbert-Huang transform
Wavelet packet transform
Evolutionary algorithm
期刊
IF:
5.6
论文数:
2.1W
被引数:
5.4W
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