References
- Azami, H., Rostaghi, M., Abásolo, D., & Escudero, J. (2017). Refined composite multiscale dispersion entropy and its application to biomedical signals. IEEE Transactions on Biomedical Engineering, 64(12), 2872–2879. https://doi.org/https://doi.org/10.1109/TBME.2017.2679136
- Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. https://doi.org/https://doi.org/10.1109/TSP.2013.2288675
- Gao, H., Ma, L., Dong, H., Lu, J., & Li, G. (2020). An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image. Systems Science & Control Engineering, 8(1), 297–307. https://doi.org/https://doi.org/10.1080/21642583.2020.1756523
- Gu, R., Chen, J., Hong, R., Wang, H., & Wu, W. (2020). Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and teager energy operator. Measurement, 149(2), 106941. https://doi.org/https://doi.org/10.1016/j.measurement.2019.106941
- Huang, H. B., Huang, X. R., Yang, M. L., Lim, T. C., & Ding, W. P. (2018). Identification of vehicle interior noise sources based on wavelet transform and partial coherence analysis. Mechanical Systems and Signal Processing, 109, 247–267. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.02.045
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. 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. https://doi.org/https://doi.org/10.1098/rspa.1998.0193
- Lahmiri, S., & Boukadoum, M. (2015). A weighted bio-signal denoising approach using empirical mode decomposition. Biomedical Engineering Letters, 5(2), 131–139. https://doi.org/https://doi.org/10.1007/s13534-015-0182-2
- Li, H., Liu, T., Wu, X., & Chen, Q. (2019). Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy. Mechanical Systems and Signal Processing, 118, 477–502. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.08.056
- Li, H., Liu, T., Wu, X., & Chen, Q. (2020). An optimized VMD method and its applications in bearing fault diagnosis. Measurement, 166(4), 108185. https://doi.org/https://doi.org/10.1016/j.measurement.2020.108185
- Li, J., Zheng, Q., Qian, Z., & Yang, X. (2019). A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Safety and Environmental Protection, 123, 309–316. https://doi.org/https://doi.org/10.1016/j.psep.2019.01.010
- Liang, W., Zhang, L., Xu, Q., & Yan, C. (2013). Gas pipeline leakage detection based on acoustic technology. Engineering Failure Analysis, 31, 1–7. https://doi.org/https://doi.org/10.1016/j.engfailanal.2012.10.020
- Liu, C., Li, Y., Fang, L., & Xu, M. (2017). Experimental study on a de-noising system for gas and oil pipelines based on an acoustic leak detection and location method. International Journal of Pressure Vessels and Piping, 151, 20–34. https://doi.org/https://doi.org/10.1016/j.ijpvp.2017.02.001
- Liu, C., Li, Y., Yan, Y., Fu, J., & Zhang, Y. (2015). A new leak location method based on leakage acoustic waves for oil and gas pipelines. Journal of Loss Prevention in the Process Industries, 35, 236–246. https://doi.org/https://doi.org/10.1016/j.jlp.2015.05.006
- Lu, J., Yue, J., Zhu, L., & Li, G. (2020). Variational mode decomposition denoising combined with improved Bhattacharyya distance. Measurement, 151(1), 107283. https://doi.org/https://doi.org/10.1016/j.measurement.2019.107283
- Muggleton, J. M., Hunt, R., Rustighi, E., Lees, G., & Pearce, A. (2020). Gas pipeline leak noise measurements using optical fibre distributed acoustic sensing. Journal of Natural Gas Science and Engineering, 78(1), 103293. https://doi.org/https://doi.org/10.1016/j.jngse.2020.103293
- Murvay, P. S., & Silea, I. (2012). A survey on gas leak detection and localization techniques. Journal of Loss Prevention in the Process Industries, 25(6), 966–973. https://doi.org/https://doi.org/10.1016/j.jlp.2012.05.010
- Rostaghi, M. (2016). Dispersion entropy: A measure for time-series analysis. IEEE Signal Processing Letters, 23(5), 610–614. https://doi.org/https://doi.org/10.1109/LSP.2016.2542881
- Tang, G. J., & Wang, X. L. (2016). An incipient fault diagnosis method for rolling bearing based on improved variational mode decomposition and singular value difference spectrum. Journal of Vibration, Measurement and Diagnosis, 36(4), 700–707. https://doi.org/https://doi.org/10.16450/j.cnki.issn.1004-6801.2016.04.014
- Wang, S. Q., Lin, Y. Y., Meng, Y. D., & Gao, Z. Q. (2012). Model order determination based on singular value decomposition. Zhendong yu Chongji (Journal of Vibration and Shock), 31(15), 87–91.
- Xiao, R., Hu, Q., & Li, J. (2019). Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine. Measurement, 146, 479–489. https://doi.org/https://doi.org/10.1016/j.Measurement.2019.06.050
- Xin, L., Liu, Z., Dou, J., Yang, Z., Zhang, X., & Liu, Z. (2020). A robust white-light interference signal leakage sampling correction method based on wavelet transform. Optics and Lasers in Engineering, 133(1), 106156. https://doi.org/https://doi.org/10.1016/j.optlaseng.2020.106156
- Yang, K., Wang, G., Dong, Y., Zhang, Q., & Sang, L. (2019). Early chatter identification based on an optimized variational mode decomposition. Mechanical Systems and Signal Processing, 115, 238–254. https://doi.org/https://doi.org/10.1016/j.ymssp.2018.05.052
- Yi, C., Lv, Y., & Dang, Z. (2016). A fault diagnosis scheme for rolling bearing based on particle swarm optimization in variational mode decomposition. Shock and Vibration, 2016. Article ID 9372691. https://doi.org/https://doi.org/10.1155/2016/9372691
- Zhang, Y., Chen, S., Li, J., & Jin, S. (2014). Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter. Measurement, 49, 382–389. https://doi.org/https://doi.org/10.1016/j.measurement.2013.12.009
- Zhang, X., Miao, Q., Zhang, H., & Wang, L. (2018). A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mechanical Systems and Signal Processing, 108, 58–72. https://doi.org/https://doi.org/10.1016/j.ymssp.2017.11.029
- Zhang, X., Peng, J., Xu, M., Yang, W., Zhang, Z., Guo, H., & Feng, Y. (2017). Denoise diffusion-weighted images using higher-order singular value decomposition. Neuroimage, 156, 128–145. https://doi.org/https://doi.org/10.1016/j.neuroimage.2017.04.017
- Zhao, M., & Jia, X. (2017). A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery. Mechanical Systems and Signal Processing, 94, 129–147. https://doi.org/https://doi.org/10.1016/j.ymssp.2017.02.036
- Zhou, Y., Zhang, Y., Yang, D., Lu, J., Dong, H., & Li, G. (2020). Pipeline signal feature extraction with improved VMD and multi-feature fusion. Systems Science & Control Engineering, 8(1), 318–327. https://doi.org/https://doi.org/10.1080/21642583.2020.1765218
- Zhou, Y., & Zhu, Z. (2019). A hybrid method for noise suppression using variational mode decomposition and singular spectrum analysis. Journal of Applied Geophysics, 161, 105–115. https://doi.org/https://doi.org/10.1016/j.jappgeo.2018.10.025