A supervised sparsity-based wavelet feature for bearing fault diagnosis
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DOI: 10.1007/s10845-016-1243-9
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Cited by:
- Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
- Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
- 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.
- Dengyu Xiao & Chengjin Qin & Honggan Yu & Yixiang Huang & Chengliang Liu, 2021. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 377-391, February.
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Keywords
Wavelet packet transform (WPT); Sparse coding; Structured dictionary; Supervised sparsity-based wavelet feature (SSWF); Machinery fault diagnosis;All these keywords.
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