Fault Diagnosis of Rolling Element Bearings Based on a Second Order Cyclic Autocorrelation and a Deep Auto-encoder
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26193-0_45
Reference13 articles.
1. Shi, H.T., Shang, Y.J.: Research on early fault diagnosis method of rolling bearing based on second-order cyclic autocorrelation and DCAE combined with transfer learning. IEEE Trans. Instrum. Meas. 71, 1–18 (2021)
2. Yu, K., Lin, T.R., Tan, J.: A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster-Shafer theory. Struct. Health Monit. 19(1), 240–261 (2019)
3. Zhu, Z.K., Feng, Z.H., Kong, F.R.: Cyclostationarity analysis for gearbox condition monitoring: Approaches and effectiveness. Mech. Syst. Signal Process. 19(3), 467–482 (2005)
4. Antoniadis, I., Glossiotis, G.: Cyclostationary analysis of rolling-element bearing vibration signals. J. Sound Vib. 248(5), 829–845 (2001)
5. Li, L., Qu, L.: Cyclic statistics in rolling bearing diagnosis. J. Sound Vib. 267(2), 253–265 (2003)
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