Weak fault diagnosis of rolling bearing based on FRFT and DBN

References

• Abboud, D., Antoni, J., Sieg, Z. S., & Eltabach, M. (2017). Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment. Mechanical Systems and Signal Processing, 84, 200–226. doi: 10.1016/j.ymssp.2016.06.033
   Web of Science ®Google Scholar
• An, X. L., Zeng, H. T., Yang, W. W., & An, X. M. (2017). A fault diagnosis of a wind turbine rolling bearing using adaptive local iterative filtering and singular value decomposition. Transactions of the Institute of Measurement and Control, 39(11), 1643–1648. doi: 10.1177/0142331216644041
   Web of Science ®Google Scholar
• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127. doi: 10.1561/2200000006
   Google Scholar
• Cai, J. H., & Xiao, Y. L. (2019). Bearing fault diagnosis method based on the generalized S transform time-frequency spectrum de-noised by singular value decomposition. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 233(7), 2467–3477. doi: 10.1177/0954406218782285
   Web of Science ®Google Scholar
• Cheng, Y., Wang, Z. W., Chen, B. Y., Zhang, W. H., & Huang, G. H. (2019). An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis. ISA Transactions, 91, 218–234. doi: 10.1016/j.isatra.2019.01.038
   PubMed Web of Science ®Google Scholar
• Guo, B., Song, L. B., & Zhou, G. L. (2012). Application research on fractional Fourier filtering in deception jamming. Acta Electronica Sinica, 40(7), 1328–1332.
   Google Scholar
• Guo, Y. T., & Xiao, S. Y. (2017). Analysis of rolling bearing fault signal based on wavelet transform. China Measurement & Test, 43(S1), 142–147.
   Google Scholar
• Ji, H. Q., Huang, K. K., & Zhou, D. H. (2019). Incipient sensor fault isolation based on augmented Mahalanobis distance. Control Engineering Practice, 86, 144–154. doi: 10.1016/j.conengprac.2019.03.013
   Web of Science ®Google Scholar
• Khodja, M. E. A., Aimer, A. F., Boudinar, A. H., Benouzza, N., & Bendiabdellah, A. (2019). Bearing fault diagnosis of a PWM inverter fed-induction motor using an improved short time Fourier transform. Journal of Electrical Engineering & Technology, 14(3), 1201–1210. doi: 10.1007/s42835-019-00096-y
   Web of Science ®Google Scholar
• Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. doi: 10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Lei, Y. G., Qiao, Z. J., Xu, X. F., Lin, J., & Niu, S. T. (2017). An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 94, 148–164. doi: 10.1016/j.ymssp.2017.02.041
   Web of Science ®Google Scholar
• Li, H., Zhang, Q., Qin, X. R., & Sun, Y. T. (2018). Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network. Journal of Vibration and Shock, 37(19), 124–131.
   Google Scholar
• Ma, J. M., Miao, H. X., Su, X. H., Gao, C., Kang, X. J., & Tao, R. (2018). Research progress in theories and applications of the fractional Fourier transform. Opto-Electronic Engineering, 45(6), 170747.
   Google Scholar
• Ma, P., Zhang, H. L., Fan, W. H., & Wang, C. (2019). Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform. Measurement, 137, 189–202. doi: 10.1016/j.measurement.2019.01.036
   Web of Science ®Google Scholar
• Ozaktas, H. M., Kutayalper, M., & Zalevsky, Z. (1995). The fractional Fourier transform: with applications in optics and signal processing. New York, NJ: Wiley.
   Google Scholar
• Qiao, Z. J., Lei, Y. G., Lin, J., & Jia, F. (2017). An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis. Mechanical Systems and Signal Processing, 84, 731–746. doi: 10.1016/j.ymssp.2016.08.030
   Web of Science ®Google Scholar
• Tao, X. (2019). Research on fault diagnosis method of rolling bearing based on EMD. Automation & Instrumentation, 34(4), 56–59.
   Google Scholar
• Wang, F. T., Deng, G., Wang, H. T., Yu, X. G., Han, Q. K., & Li, H. K. (2019). A rolling bearing fault diagnosis method based on EMD and SSAE. Journal of Vibration Engineering, 32(2), 368–376.
   Google Scholar
• Wen, C. L., Lv, F. Y., Bao, Z. J., & Liu, M. Q. (2016). A review of data driven-based incipient fault diagnosis. Acta Automatica Sinica, 42(9), 1285–1299.
   Google Scholar
• Xu, Y. G., Tian, W. K., Zhang, K., & Ma, C. Y. (2019). Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Measurement Science and Technology, 30(3), 035001. doi: 10.1088/1361-6501/aafb44
   Web of Science ®Google Scholar
• Zhang, S., Zhao, R. Z., & Deng, L. F. (2018). Weak fault identification of rolling bearings based on VMD singular value entropy. Journal of Vibration and Shock, 37(21), 87–91 + 107.
   Google Scholar
• Zhao, X. H., Deng, B., & Tao, R. (2005). Dimensional normalization in the digital computation of the fractional Fourier transform. Transactions of Beijing Institute of Technology, 25(4), 360–364.
   Google Scholar
• Zhong, B. L., & Huang, R. (2007). Mechanical fault diagnosis. BeiJing, BJ: Mechanical fault diagnosis.
   Google Scholar
• Zhu, Y. S., Zhang, P., & Yuan, Q. Q. (2019). Key technologies and development trend of smart bearing. Journal of Vibration, Measurement & Diagnosis, 39(3), 455–462 + 665.
   Google Scholar