Multilevel feature fusion of multi-domain vibration signals for bearing fault diagnosis
Author:
Funder
Qingdao Postdoctoral Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02715-8.pdf
Reference25 articles.
1. Wang, Z., Zhang, Q., Xiong, J., Xiao, M., Sun, G., He, J.: Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens. J. 17(17), 5581–5588 (2017)
2. Liu, Z., Jiang, L., Wei, H., Chen, L., Li, X.: Optimal transport-based deep domain adaptation approach for fault diagnosis of rotating machine. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)
3. Cao, L., Shen, Y., Shan, T., Xia, Y., Wang, J., & Lin, Z.: Bearing fault diagnosis method based on GMM and coupled hidden Markov model. In: 2018 Prognostics and System Health Management Conference (PHM-Chongqing), pp. 932–936, IEEE (2018)
4. Zhang, X., and Kang, J.: Hidden Markov models in bearing fault diagnosis and prognosis. In: 2010 Second International Conference on Computational Intelligence and Natural Computing, IEEE (2010)
5. Wang, D., Li, Y., Jia, L., Song, Y., Liu, Y.: Novel three-stage feature fusion method of multimodal data for bearing sault diagnosis. IEEE Trans. Instrum. Meas. 70, 1–10 (2021)
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