WPT-Base Selection for Bearing Fault Feature Extraction: A Node-Specific Approach Study
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47637-2_14
Reference11 articles.
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2. Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015)
3. Chen, L., Xu, G., Tao, T., Wu, Q.: Deep Residual network for identifying bearing fault location and fault severity concurrently. IEEE Access 8, 168026–168035 (2020)
4. Skora, M., Ewert, P., Kowalski, C.T.: Selected rolling bearing fault diagnostic methods in wheel embedded permanent magnet brushless direct current motors. Energies 12(21), 4212 (2019)
5. Maliuk, A.S., Prosvirin, A.E., Ahmad, Z., Kim, C.H., Kim, J.M.: Novel bearing fault diagnosis using Gaussian mixture model-based fault band selection. Sensors 21(19), 6579 (2021)
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