Identification Method for Cage Rubbing Faults of Flywheel Bearings Based on Characteristic Frequency Ratio and Convolutional Neural Network
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40455-9_41
Reference13 articles.
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3. Yu, G.: A concentrated time–frequency analysis tool for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 69(2), 371–381 (2019)
4. Jalayer, M., Orsenigo, C., Vercellis, C.: Fault detection and diagnosis for rotating machinery: a model based on convolutional LSTM, fast fourier and continuous wavelet transforms. Comput. Ind. 125, 103378 (2021)
5. Li, J., Liu, Y., Li, Q.: Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method. Measurement 189, 110500 (2022)
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