Faulty bearing diagnostic model based on multi-dimensional signal and multi-analysis domain
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00202-024-02522-5.pdf
Reference27 articles.
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2. Li B, Tang B, Deng L et al (2021) Self-attention ConvLSTM and its application in RUL prediction of rolling bearings. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2021.3086906
3. Shi H, Li Y, Bai X et al (2022) A two- stage sound-vibration signal fusion method for weak fault detection in rolling bearing systems. Mech Syst Signal Process 172:109012. https://doi.org/10.1016/J.YMSSP.2022.109012
4. Ma J, Zhan L, Li C et al (2020) An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction. Meas Sci Technol 32(2):025103. https://doi.org/10.1088/1361-6501/ABBC48
5. Chen X, Yang R, Xue Y et al (2023) Deep transfer learning for bearing fault diagnosis: a systematic review since 2016. IEEE Trans Instrum Meas 72:1–21. https://doi.org/10.1109/TIM.2023.3244237
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