Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

1 S. Zhang, S. Zhang, B. Wang, and T. G. Habetler, “Deep learning algorithms for bearing fault diagnostics—A comprehensive review,” IEEE Access, vol. 8, pp. 29857–29881, 2020, doi: 10.1109/ACCESS.2020.2972859. Search in Google Scholar

2 J. A. Reyes-Malanche, F. J. Villalobos-Pina, E. Cabal-Yepez, R. Alvarez-Salas, and C. Rodriguez-Donate, “Open-circuit fault diagnosis in power inverters through currents analysis in time domain,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021, Art no. 3517512, doi: 10.1109/TIM.2021.3082325. Search in Google Scholar

3 X. Chen, P. Qin, Y. Chen, J. Zhao, W. Li, Y. Mao, and T. Zhao, “Inter-turn short circuit fault diagnosis of PMSM,” Electronics, vol. 11, no. 10, p. 1576, 2022, https://doi.org/10.3390/electronics11101576. Search in Google Scholar

4 H. Pan, X. He, S. Tang, and F. Meng, “An improved bearing fault diagnosis method using one-dimensional CNN and LSTM,” 2018, bearing fault diagnosis; CNN; LSTM vol. 64, no. 7–8, p. 10, 2018. Search in Google Scholar

5 S. Liang, Y. Chen, H. Liang, and X. Li, “Sparse representation and SVM diagnosis method for inter-turn short-circuit fault in PMSM,” Applied Sciences, vol. 9, no. 2, p. 224, 2019, https://doi.org/10.3390/app9020224. Search in Google Scholar

6 Z. Zhao, Q. Xu, and M. Jia, “Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis,” Neural Computing and Applications, vol. 27, no. 2, pp. 375–385, 2016. Search in Google Scholar

7 L.-K. Chang, S.-H. Wang, and M.-C. Tsai, “Demagnetization fault diagnosis of a PMSM using auto-encoder and K-means clustering,” Energies, vol. 13, no. 17, doi: 10.3390/en13174467. Search in Google Scholar

8 J. Jiao, M. Zhao, J. Lin, and K. Liang, “A comprehensive review on convolutional neural network in machine fault diagnosis,” Neurocomputing, vol. 417, pp. 36–63, 2020. Search in Google Scholar

9 W. Zhang, X. Li, and Q. Ding, “Deep residual learning-based fault diagnosis method for rotating machinery,” ISA Transactions, vol. 95, pp. 295–305, 2019. Search in Google Scholar

10 R. Huang, Y. Liao, S. Zhang, and W. Li, “Deep decoupling convolutional neural network for intelligent compound fault diagnosis,” IEEE Access, vol. 7, pp. 1848–1858, 2019, doi: 10.1109/ACCESS.2018.2886343. Search in Google Scholar

11 X. Ding and Q. He, “Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 8, pp. 1926–1935, Aug. 2017, doi: 10.1109/TIM.2017.2674738. Search in Google Scholar

12 D. Verstraete, A. Ferrada, E. L. Droguett, V. Meruane, and M. Modarres, “Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings,” Shock and Vibration, vol. 2017, p. 5067651, 2017. Search in Google Scholar

13 Z. Shi, X. Yang, Y. Li, and G. Yu, “Wavelet-based synchroextracting transform: An effective TFA tool for machinery fault diagnosis,” Control Engineering Practice, vol. 114, p. 104884, 2021. Search in Google Scholar

14 C. Su, et al., “Damage assessments of composite under the environment with strong noise based on synchrosqueezing wavelet transform and stack autoencoder algorithm,” Measurement, vol. 156, p. 107587, 2020. Search in Google Scholar

15 J. Yuan, Z. Yao, Q. Zhao, Y. Xu, C. Li, and H. Jiang, “Dual-core denoised synchrosqueezing wavelet transform for gear fault detection,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021, Art no. 3521611, doi: 10.1109/TIM.2021.3094838. Search in Google Scholar

16 Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. Search in Google Scholar

17 G. W. Chang, Y.-L. Lin, Y.-J. Liu, G. H. Sun, and J. T. Yu, “A hybrid approach for time-varying harmonic and interharmonic detection using synchrosqueezing wavelet transform,” Applied Sciences, vol. 11, no. 2, p. 752, 2021. Search in Google Scholar

18 H. Wang and D.-Y. Yeung, “Towards Bayesian deep learning: A framework and some existing methods,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3395–3408, Dec. 2016, doi: 10.1109/TKDE.2016.2606428. Search in Google Scholar

19 M. Jiaocheng, S. Jinan, Z. Xin, and Z. Peng, “Bayes-DCGRU with Bayesian optimization for rolling bearing fault diagnosis,” Applied Intelligence, vol. 52, no. 10, pp. 11172–11183, 2022. Search in Google Scholar

20 W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study,” Mechanical Systems and Signal Processing, vol. 64–65, pp. 100–131, 2015. Search in Google Scholar

21 W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study,” Mechanical Systems and Signal Processing, vol. 64-65, pp. 100-131, 2015. Search in Google Scholar

22 USA. The Vibration Institute, Condition Based Maintenance Fault Database for Testing of Diagnostic and Prognostics Algorithms. [Online]. https://www.mfpt.org/fault-data-sets.. Search in Google Scholar

23 C. Lessmeier, J. K. Kimotho, D. Zimmer, and W. Sextro, “Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification,” In: Editor. Pub Place; 2016. pp. 5–8. Search in Google Scholar

24 Germany. University of Paderborn, Department of Design and Drive Technology, Condition Monitoring (CM) Experimental Bearing Dataset Based on Vibration and Motor Current Signals. [Online]. https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download Search in Google Scholar

25 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017. Search in Google Scholar