Multisensory Data-Based Fault Diagnosis of Induction Motors Using 1D and 2D Convolutional Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49421-5_92
Reference14 articles.
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3. Eren, L., Ince, T., Kiranyaz, S.: A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J. Signal Process. Syst. 91(2), 179–189 (2019). https://doi.org/10.1007/s11265-018-1378-3
4. Wang, X., Mao, D., Li, X.: Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 173, 108518 (2021). https://doi.org/10.1016/j.measurement.2020.108518
5. Zhang, J., Sun, Y., Guo, L., Gao, H., Hong, X., Song, H.: A new bearing fault diagnosis method based on modified convolutional neural networks. Chin. J. Aeronaut. 33(2), 439–447 (2020). https://doi.org/10.1016/j.cja.2019.07.011
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