Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory
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DOI: 10.1007/s10845-015-1153-2
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Cited by:
- Xuejun Zhao & Yong Qin & Changbo He & Limin Jia, 2022. "Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 185-201, January.
- Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
- Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
- Xiaoyin Nie & Gang Xie, 2021. "A novel normalized recurrent neural network for fault diagnosis with noisy labels," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1271-1288, June.
- Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin, 2021. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing," Energies, MDPI, vol. 14(4), pages 1-24, February.
- Xiao Yang & Fengrong Bi & Yabing Jing & Xin Li & Guichang Zhang, 2022. "A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory," Energies, MDPI, vol. 15(9), pages 1-20, May.
- Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
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Keywords
Wavelet packet transform (WPT); Sparse representation; Dictionary learning; Sparse wavelet reconstruction residual (SWRR); Machinery fault diagnosis;All these keywords.
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