Study on Rolling Bearing Wear Experiment with Multi-source Information Monitoring
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69483-7_23
Reference12 articles.
1. Li, S., Xin, Y., Li, X., et al.: A review on the signal processing methods of rotating machinery fault diagnosis. In: 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1559−1565. IEEE, Chongqing, China (2019).
2. Choudhary, A., Goyal, D., Shimi, S., et al.: Condition monitoring and fault diagnosis of induction motors: a review. Arch. Comput. Methods in Eng. 26(4), 1221–1238 (2019)
3. Tao, X., Ren, C., Wu, Y., et al.: Bearings fault detection using wavelet transform and generalized Gaussian density modeling. Measurement 155, 107557 (2020)
4. Zhou, J., Xiao, J., Xiao, H., et al.: Multifault diagnosis for rolling element bearings based on intrinsic mode permutation entropy and ensemble optimal extreme learning machine. Adv. Mech. Eng. 6, 1–10 (2014)
5. CWRU Bearing Dataset. https://engineering.case.edu/bearingdatacenter/download-data-file. Accessed 16 Apr 2024
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