148
Views
0
CrossRef citations to date
0
Altmetric
Mechanical engineering

Denoise for propeller acoustic signals based on the improved wavelet thresholding algorithm of CEEMDAN

ORCID Icon, , &
Article: 2327570 | Received 11 Dec 2023, Accepted 01 Mar 2024, Published online: 01 Apr 2024

References

  • Alkareem Alyasseri, Z. A., Khader, A. T., Al-Betar, M. A., & Abualigah, L. M. (2017 ECG signal denoising using β-hill climbing algorithm and wavelet transform [Paper presentation]. 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan, pp. 96–101. https://doi.org/10.1109/ICITECH.2017.8079971
  • Bowen, W. E. I., & Zimeng, Z. (2019). Flow-induced vibration response analysis of high arch dam discharge structure based on improved wavelet threshold-EMD algorithm[J]. Hydro-Science and Engineering, (4), 83–91. https://doi.org/10.16198/j.cnki.1009-640x.2019.04.012
  • DONG, Xin, LI, Guolong, HE, Kun, JIA, Yachao, XU, Kai, & LI, Biao (2020). Spectral Graph Wavelet Threshold Denoising and Its Application to Vibration Signal Analysis for Hob Spindle[J]. Journal of Mechanical Engineering, 56(11), 96–107. https://doi.org/10.3901/JME.2020.11.096
  • Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627. https://doi.org/10.1109/18.382009
  • Erzheng, F., Zhihao, H., & Chenyang, G. (2020). Current status and challenges of surface and underwater target recognition technology. National Defense Science, Technology and Industry, 7, 66–68.
  • Fei, W., Chenhao, M., & Kun, C. (2022). Research on wavelet noise of vibration signal based on improved threshold. Journal of Hefei University of Technology (Natural Science Edition), 45(7), 873–877. + 900.
  • Feng, C., & Xinmin, C. (2005). Signal denoising technology based on wavelet transform. Modern Electronic Technology, 28(3), 11–13.
  • Hongqiang, Y., Xiaocheng, C. A. I., & Tong, T. C. (2023). Transponder signal demodulation method based on wavelet denoising and chaos theory. Dynamic Technology and Application, 42(02), 21–25. https://doi.org/10.20033/j.1003-7241.(2023)02-0021-05
  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971): 903–995.
  • Jian, Q., Taiyong, F., Zhiguo, Q., & Yinan, Z. (2023). The bearing vibration signal noise reduction algorithm based on optimized wavelet threshold. Modern Defense Technology, 51(2), 141–147.
  • Khaldi, K., Turki-Hadj Alouane, M., & Boudraa, A.-O. (2008). A new EMD denoising approach dedicated to voiced speech signals [Paper presentation]. 2008 2nd International Conference on Signals, Circuits and Systems, Nabeul, Tunisia. pp. 1–5. https://doi.org/10.1109/ICSCS.2008.4746883
  • Lei, Y., Liu, Z., Ouazri, J., & Lin, J. (2017). A fault diagnosis method of rolling element bearings based on CEEMDAN. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231(10):1804–1815. https://doi.org/10.1177/0954406215624126
  • Li, Y., Li, Y., Chen, X., Yu, J., Yang, H., & Wang, L. (2018). A new underwater acoustic signal denoising technique based on CEEMDAN, mutual information, permutation entropy, and wavelet threshold denoising. Entropy, 20(8), 563. https://doi.org/10.3390/e20080563
  • Ngoyi, M. (2023). Application of the EEMD and CEEMDAN algorithm for non-linear signal processing. International Journal of Engineering and Applied Physics, 3(2), 780–789.
  • Parmar, J. M., & Patil, S. A. (2013 Performance evaluation and comparison of modified denoising method and the local adaptive wavelet image denoising method [Paper presentation]. 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), Vallabh Vidyanagar, India, pp.101–105. https://doi.org/10.1109/ISSP.2013.6526883
  • Pauline, S. H., Narayanamoorthi, R., & Dhanalakshmi, S. (2022). A Low-complexity underwater acoustic signal denoising technique based on multi-stage adaptive filter configuration. OCEANS 2022-Chennai. IEEE, 1–4.
  • Tingpeng, D., & Guicang, Z. (2023). Research application of the new wavelet threshold function in image denoising. Modern Electronic Technology, 46(09), 55–60. https://doi.org/10.16652/j.issn.1004-373x.2023.09.011
  • Xu, Y., Luo, M., Li, T., & Song, G. (2017). ECG signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold. Sensors, 17(12), 2754. https://doi.org/10.3390/s17122754
  • Zhang, S., Liu, H., Hu, M., Jiang, A., Zhang, L., Xu, F., & Hao, G. (2020). An adaptive CEEMDAN thresholding denoising method optimized by nonlocal means algorithm. IEEE Transactions on Instrumentation and Measurement, 69(9), 6891–6903. https://doi.org/10.1109/TIM.2020.2978570
  • Zhou, K., Zhu, Z., Wang, B. et al. (2024). Research on propeller cavitation wake characteristics based on multivariate statistical modeling method. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 238(1), 102–113. https://doi.org/10.1177/14750902231164789
  • Zhu, Z. (2014). Characteristic correlation between propellers cavitating wake and cavitation noise. Applied Acoustics, 81, 31–39. https://doi.org/10.1016/j.apacoust.2014.02.004
  • Zhu, Z. F., Zhou, F., & Li, D. (2017). Numerical prediction of tip vortex cavitation for marine propellers in non-uniform wake. Chinese Journal of Mechanical Engineering, 30(4), 804–818. https://doi.org/10.1007/s10033-017-0145-x