Enhanced fault feature extraction and bearing fault diagnosis using shearlet transform and deep learning
Author:
Funder
Madhya Pradesh Council of Science &Technology (MPCOST), Bhopal, India
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03545-y.pdf
Reference23 articles.
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2. Rai, A., Upadhyay, S.H.: A review on signal processing techniques utilized in fault diagnosis of rolling element bearings. Tribol. Int. 96, 289–306 (2016). https://doi.org/10.1016/j.triboint.2015.12.037
3. Zhu, D., Yin, B., Teng, C.: An improved spectral amplitude modulation method for rolling element bearing fault diagnosis. J Braz. Soc. Mech. Sci. Eng. 45, 257 (2023)
4. Li, H., Liu, T., Wu, X., Li, S.: Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology. Measurement 185, 109986 (2021). https://doi.org/10.1016/j.measurement.2021.109986
5. Zair, M., Rahmoune, C., Benazzouz, D.: Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 233(9), 3317–3328 (2019). https://doi.org/10.1177/0954406218805510
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