Signal de-noising method for vibration signal of flood discharge structure based on combined wavelet and EMD

被引:20
作者
Zhang, Jianwei [1 ,2 ]
Jiang, Qi [2 ]
Ma, Bin [1 ]
Zhao, Yu [2 ]
Zhu, Lianghuan [2 ]
机构
[1] Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin, Peoples R China
[2] North China Univ Water Conservancy & Elect Power, Coll Water Conservancy, Zhengzhou 450011, Henan, Peoples R China
基金
中国国家自然科学基金;
关键词
Flood discharge structure; de-noising; combined wavelet threshold and EMD; low signal to noise ratio; structure vibration; IDENTIFICATION METHOD; HILBERT SPECTRUM;
D O I
10.1177/1077546315616551
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
A new de-noising method combining Wavelet threshold and empirical mode decomposition (EMD) (WTEMD for short) is proposed to improve the precision of de-noising performance for vibration signal of flood discharge structure in low signal to noise ratio (SNR). White noise is partially filtered out by decomposing the vibration signal with wavelet. Then conducting the further EMD on wavelet reconstructed signal to obtain Intrinsic Mode Function (IMF), through analyzing spectrum diagram of every IMF component, low-frequency waterflow noise and the rest of high-frequency white noise are filtered out, regarding SNR and root mean square error (RMSE) as evaluation index for noise reduction effect. The novelty of this method is that it can reduce the endpoint effect of EMD. By comparing the filtering effect of WTEMD with other methods on simulation signals, study shows that, WTEMD has a higher precision and a better de-noising effect. The dominant vibration information of dam structure is achieved by using WTEMD in Laxiwa arch dam hydro-elastic model and Three Gorges Dam, which can provide the basis for safe operation and on-line monitoring of the dam structure. This method can effectively solve the problem of dominant information extraction for large flood discharge structure.
引用
收藏
页码:2401 / 2417
页数:17
相关论文
共 50 条
[31]   Signal de-noising in magnetic resonance spectroscopy using wavelet transforms [J].
Cancino-De-Greiff, HF ;
Ramos-Garcia, R ;
Lorenzo-Ginori, JV .
CONCEPTS IN MAGNETIC RESONANCE, 2002, 14 (06) :388-401
[32]   The BOTDR System Signal De-noising Based on Labview [J].
Zhao, Lijuan ;
Li, Yongqian ;
Lv, Anqiang .
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION (ICMS2009), VOL 8, 2009, :230-234
[33]   The comparison study of wavelet and wavelet packet analysis in the de-noising of EEG test signal [J].
Li, Yifeng ;
Zhang, Lihui ;
Li, Baohui ;
Yan, Guiding ;
Geng, Xichen ;
Jin, Zhao ;
Xu, Yan ;
Wang, Haixia ;
Liu, Xiaoyan ;
Lin, Rong ;
Wei, Xiaoyang ;
Wang, Quan .
2015 7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME), 2015, :274-277
[34]   De-noising of Seismic Signal based on Gabor Transform [J].
Kumar, Roshan ;
Sumathi, P. ;
Kumar, Ashok .
2013 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2013, :1997-2001
[35]   A SVD-Based Signal De-Noising Method With Fitting Threshold for EMAT [J].
Lei, Biting ;
Yi, Pengxing ;
Xiang, Jiayun ;
Xu, Wei .
IEEE ACCESS, 2021, 9 :21123-21131
[36]   Wavelet thresholding method using higher-order statistics for seismic signal de-noising [J].
Li, Yuanyuan ;
Yang, Yushan .
PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, :4866-4870
[37]   Research On De-Noising Method Of Railway Vehicle Noise Signal [J].
Wang JinHua .
2019 11TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2019), 2019, :83-87
[38]   Study on Wavelet Energy Entropy and its Application to Bioelectrical Signal De-noising [J].
Luo Zhi-zeng ;
Zhou Wei .
2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11, 2009, :2625-2628
[39]   Comparison of De-Noising Methods of LiDAR Signal [J].
Ding H. ;
Wang Z. ;
Liu D. .
Wang, Zhenzhu (zzwang@aiofm.ac.cn); Wang, Zhenzhu (zzwang@aiofm.ac.cn), 2021, Chinese Optical Society (41)
[40]   De-noising and recognition of EOG signal based on mathematical morphology [J].
Jiang, Pan ;
Zhou, Runjing .
2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2, 2013, :351-354