Zhang Zhongliang, Zhu Xiaojun, Peng Fei, Mu Jinlei. Characteristic analysis and de-noising method of stress monitoring data of hull structures based on HHT[J]. Chinese Journal of Ship Research, 2019, 14(S1): 158-164. DOI: 10.19693/j.issn.1673-3185.01509
Citation: Zhang Zhongliang, Zhu Xiaojun, Peng Fei, Mu Jinlei. Characteristic analysis and de-noising method of stress monitoring data of hull structures based on HHT[J]. Chinese Journal of Ship Research, 2019, 14(S1): 158-164. DOI: 10.19693/j.issn.1673-3185.01509

Characteristic analysis and de-noising method of stress monitoring data of hull structures based on HHT

More Information
  • Received Date: January 07, 2019
  • Available Online: May 07, 2021
© 2019 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  •   Objectives  In order to remove the noise signal from the hull stress monitoring data and obtain effective data information to provide support for further data mining,
      Methods  a component analysis of data by using the Empirical Mode Decomposition(EMD) in Hilbert-Huang Transform(HHT) method was carried out firstly in this paper to get the Intrinsic Mode Function(IMF) and the remainder. Then the Hilbert spectrum was obtained by Hilbert transform to prove the non-stationary characteristics of the stress monitoring data. Finally, taking Signal-Noise-Ratio(SNR)and Root Mean Square Error(RMSE) as examples and combining the adaptive de-noising and wavelet threshold de-noising methods, the de-noising effect of stress monitoring data was compared and verified.
      Results  The results show that the two methods based on HHT have certain de-noising effect. Among them, the adaptive de-noising method has bigger SNR and smaller RMSE. Above all, the adaptive de-noising method has the best performance.
      Conclusions  The study proves that the adaptive de-noising method can de-noise the stress monitoring data more effectively.
  • [1]
    任慧龙, 贾连徽, 李陈峰, 等.船体结构应力监测系统的滤波器设计[J].哈尔滨工程大学学报, 2013, 34(8):945-951, 971. http://d.old.wanfangdata.com.cn/Periodical/hebgcdxxb201308001

    Ren H L, Jia L H, Li C F, et al. Filter design of ship structure stress monitoring system[J]. Journal of Harbin Engineering University, 2013, 34(8):945-951, 971(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hebgcdxxb201308001
    [2]
    李军华.基于小波变换理论的地震资料去噪方法研究及应用[J].能源技术与管理, 2017, 42(5):175-177. doi: 10.3969/j.issn.1672-9943.2017.05.066

    Li J H. Research and application of seismic data denoising method based on wavelet transform theory[J]. Energy Technology and Management, 2017, 42(5):175-177(in Chinese). doi: 10.3969/j.issn.1672-9943.2017.05.066
    [3]
    苏秀红, 李皓.基于经验模态分解和小波阈值的冲击信号去噪[J].计算机测量与控制, 2017, 25(1):204-208, 220. http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz201701057

    Su X H, Li H. Denoising of shock signal based on EMD and wavelet threshold[J]. Computer Measurement & Control, 2017, 25(1):204-208, 220(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz201701057
    [4]
    郑敏敏, 高小榕, 谢海鹤.心电信号小波去噪的改进算法研究[J].中国生物医学工程学报, 2017, 36(1):114-118. doi: 10.3969/j.issn.0258-8021.2017.01.015

    Zheng M M, Gao X R, Xie H H. Research on an improved algorithm for wavelet denoising of ECG[J]. Chinese Journal of Biomedical Engineering, 2017, 36(1):114-118(in Chinese). doi: 10.3969/j.issn.0258-8021.2017.01.015
    [5]
    赵薇, 陆余恬.基于自适应算法的去噪滤波仿真比较[J].中国传媒大学学报(自然科学版), 2013, 20(4):40-46. doi: 10.3969/j.issn.1673-4793.2013.04.006

    Zhao W, Lu Y T. Comparison of noise filtering simulations based on adaptive algorithms[J]. Journal of Communication University of China (Science and Technology), 2013, 20(4):40-46(in Chinese). doi: 10.3969/j.issn.1673-4793.2013.04.006
    [6]
    张莲, 秦华峰, 余成波.基于小波阈值去噪算法的研究[J].计算机工程与应用, 2008, 44(9):172-173, 199. doi: 10.3778/j.issn.1002-8331.2008.09.051

    Zhang L, Qin H F, Yu C B. Research of denoising method based on wavelet threshold[J]. Computer Engineering and Applications, 2008, 44(9):172-173, 199(in Chinese). doi: 10.3778/j.issn.1002-8331.2008.09.051
    [7]
    Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995. doi: 10.1098/rspa.1998.0193
    [8]
    陈喆, 王荣, 周文颖, 等.非平稳信号度量方法综述[J].数据采集与处理, 2017, 32(4):667-683. http://d.old.wanfangdata.com.cn/Periodical/sjcjycl201704003

    Chen Z, Wang R, Zhou W Y, et al. Review on measurement parametrics and methods for nonstationary signal[J]. Journal of Data Acquisition & Processing, 2017, 32(4):667-683(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/sjcjycl201704003
    [9]
    吕震宇, 基于ELMD和KNN分类器的船体砰击载荷检测算法研究[D].哈尔滨: 哈尔滨工程大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10217-1017240776.htm

    Lu Z Y. Ship slamming loads detection algorithm based on ELMD and KNN classifier[D]. Harbin: Harbin Engineering University, 2016(in Chinese). http://cdmd.cnki.com.cn/Article/CDMD-10217-1017240776.htm
    [10]
    王莹.基于成分分解的自适应滤波降噪方法研究[D].哈尔滨: 哈尔滨工业大学, 2017. http://cdmd.cnki.com.cn/Article/CDMD-10213-1017863878.htm

    Wang Y. Research on adaptive filter denoising method based on component decomposition[D]. Harbin: Harbin Institute of Technology, 2017(in Chinese). http://cdmd.cnki.com.cn/Article/CDMD-10213-1017863878.htm
    [11]
    陈卫萍, 潘紫微.基于经验模态分解的小波阈值滤波去噪[J].安徽工业大学学报(自然科学版), 2010, 27(4):397-400. doi: 10.3969/j.issn.1671-7872.2010.04.015

    Chen W P, Pan Z W. Denoising of wavelet threshold filtering based on empirical mode decomposition[J]. Journal of Anhui University of Technology (Natural Science), 2010, 27(4):397-400(in Chinese). doi: 10.3969/j.issn.1671-7872.2010.04.015
    [12]
    姜泉璐, 汪立新, 吕永佳, 等.基于LMS自适应滤波器的设计[J].电子设计工程, 2011, 19(14):67-69. doi: 10.3969/j.issn.1674-6236.2011.14.027

    Jiang Q L, Wang L X, Lu Y J, et al. Design of adaptive filter based on LMS[J]. Electronic Design Engineering, 2011, 19(14):67-69(in Chinese). doi: 10.3969/j.issn.1674-6236.2011.14.027
    [13]
    江虹, 苏阳.一种改进的小波阈值函数去噪方法[J].激光与红外, 2016, 46(1):119-122. doi: 10.3969/j.issn.1001-5078.2016.01.023

    Jiang H, Su Y. Denoising method based on improved wavelet threshold function[J]. Laser & Infrared, 2016, 46(1):119-122(in Chinese). doi: 10.3969/j.issn.1001-5078.2016.01.023
    [14]
    邵鸿翔, 高宏峰.改进小波阈值去噪方法处理FBG传感信号[J].激光与红外, 2014, 44(1):73-76. doi: 10.3969/j.issn.1001-5078.2014.01.016

    Shao H X, Gao H F. Processing of FBG sensor signal with improved wavelet threshold de-noising method[J]. Laser & Infrared, 2014, 44(1):73-76(in Chinese). doi: 10.3969/j.issn.1001-5078.2014.01.016
    [15]
    沈再阳. MATLAB信号处理[M].北京:清华大学出版社, 2017:344.

    Shen Z Y. Signal processing based on MATLAB[M]. Beijing:Tsinghua University Press, 2017:344(in Chinese).

Catalog

    Article views (383) PDF downloads (55) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return