Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction
Author:
Funder
National Natural Science Foundation of China
Outstanding Youth Fund of Universities in Anhui Province of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11071-024-10106-y.pdf
Reference36 articles.
1. Abudurexiti, Y., Han, G., Liu, L., et al.: Graph-guided higher-order attention network for industrial rotating machinery intelligent fault diagnosis. IEEE Trans. Ind. Inform. 20(2), 1113–1123 (2023)
2. Guo, J., He, Q., Zhen, D., et al.: Intelligent fault detection for rotating machinery using cyclic morphological modulation spectrum and hierarchical Teager permutation entropy. IEEE Trans. Industr. Inf. 19(4), 6196–6207 (2022)
3. Chang, S., Wang, L., Shi, M., et al.: Extended attention signal transformer with adaptive class imbalance loss for long-tailed intelligent fault diagnosis of rotating machinery. Adv. Eng. Inform. 60, 102436 (2024)
4. Zhang, C., Wang, R., Yu, L., et al.: Order domain beamforming for the acoustic localization of rotating machinery under variable speed working conditions. Appl. Acoust. 205, 109290 (2023)
5. He, X., Zhou, W., Luo, Z., et al.: Data privacy protection health status assessment for rotating machinery with dual-task feature fusion framework. Neurocomputing 582, 127464 (2024)
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