Multiscale Redundant Second Generation Wavelet Kernel-Driven Convolutional Neural Network for Rolling Bearing Fault Diagnosis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5847-4_19
Reference21 articles.
1. Huang, Z., Lei, Z., Huang, X., et al.: A multisource dense adaptation adversarial network for fault diagnosis of machinery. IEEE Trans. Ind. Electron. 69(6), 6298–6307 (2021)
2. Jiang, Y., Yin, S.: Recursive total principle component regression based fault detection and its application to vehicular cyber-physical systems. IEEE Trans. Ind. Inf. 14(4), 1415–1423 (2017)
3. Yuan, J., Cao, S., Ren, G., et al.: LW-Net: an interpretable network with smart lifting wavelet kernel for mechanical feature extraction and fault diagnosis. Neural Comput. Appl. 34(18), 15661–15672 (2022)
4. Yuan, J., He, Z., Zi, Y., et al.: Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis. Mech. Syst. Sig. Process. 23(5), 1490–1508 (2009)
5. Shao, H., Jiang, H., Zhang, H., et al.: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Sig. Process. 100, 743–765 (2018)
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