Zero-Shot Rolling Bearing Compound Fault Diagnosis Based on Envelope Spectrum Semantic Construction
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49413-0_31
Reference13 articles.
1. Tang, J., Wei, C., Huang, W.: Bearings intelligent fault diagnosis by 1-D adder neural networks. J. Dyn. Monit. Diagnostics 1(3), 160–168 (2022)
2. Tang G, Wang Y, Huang Y (2021) Multiple time-frequency curve classification for Tacho-less and resampling-less compound bearing fault detection under time-varying speed conditions. IEEE Sensors J. 21(4).
3. Li, Z., Jiang, Y., Hu, C., Peng, Z.: Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: a review. Measurement 90, 4–19 (2016)
4. Yuan, H., Wu, N., Chen, X.: Mechanical compound fault analysis method based on shift invariant dictionary learning and improved FastICA algorithm. Machines 9(8), 144 (2021)
5. Xie, W., Zhou, L., Liu, T.: Blind fault extraction of rolling-bearing compound fault based on improved morphological filtering and sparse component analysis. Sensors 22(18), 7093 (2022)
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