基于VMD的隧道空洞小波包能量熵提取方法研究

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (22) : 199-208.

论文

基于VMD的隧道空洞小波包能量熵提取方法研究

鞠景会1，赵维刚*2，田秀淑3，范博阳1，石壮1，郑伟康1

作者信息 +

Wavelet packet decomposition energy entropy extraction method for the tunnel void desease based on VMD

JU Jinghui1,ZHAO Weigang*2,TIAN Xiushu3,FAN Boyang1,SHI Zhuang1,ZHENG Weikang1

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文章历史 +

摘要

空洞病害作为铁路隧道主要病害之一，影响铁路的安全运行，实现空洞病害的精准化智能化检测一直是铁路维护部门的难点之一。针对该问题本文利用Comsol软件建立了声-固耦合有限元模型，分析了不同工况下的声学信号特征，提出了基于粒子群优化-变分模态分解-小波包分解(particle swarm optimization-variational mode decomposition-wavelet packet decomposition,PSO-VMD-WPD)的多算法融合的空洞声学能量熵的提取方法。首先利用粒子群（PSO）优化的变分模态分解算法（VMD）对空洞的声学信号进行处理，提取子信号中相关系数较大的信号进行重组，从而抑制原信号中相关性比较的低的模态。然后利用小波包算法（WPD）对重组后的信号进行分解，选择空洞的信号影响较大的频率段，计算小波包的分解层数，提取子信号的能量熵。结果表明：(1)当声波信号中6250Hz-9375Hz频段的能量熵小于0.264时，混凝土结构中出现空洞病害。(2)利用VMD算法处理混凝土空洞的声波信号时，选择与原信号相关系数大于0.3的子信号进行重构后，信号中6250Hz-9375Hz频段的能量熵明显降低。

Abstract

As one of the main diseases of railway tunnel, void disease affects the safe operation of railway. The accurate detection of void disease is always one of the difficulties in railway maintenance department. To solve this problem, this paper establishes the acoustic-solid coupling finite element model by using Comsol software, analyzes the acoustic signal characteristics under different working conditions and puts forward the extraction method of acoustic energy entropy based on multi-algorithm fusion of particle swarm optimization-variational mode decomposition-wavelet packet decomposition (PSO-VMD-WPD). Firstly, the variational mode decomposition (VMD) algorithm optimized by particle swarm optimization (PSO) is used to process the void acoustic signal, then the signals with large correlation coefficients are extracted and reassembled, so as to suppress the low correlation modes in the original signal. The wavelet packet algorithm (WPD) is used to decompose the reconstructed signal, select the frequency segment where the empty signal has a greater influence, calculate the decomposition layer number of WPD and extract the energy entropy of the sub-signal. The results show that (1) When the energy entropy of 6250Hz-9375Hz frequency band in the acoustic signal is less than 0.264, void disease appears in the concrete structure. (2) When the VMD algorithm is used to process the acoustic signal of the concrete void, the energy entropy of the 6250Hz-9375Hz band in the signal is obviously reduced after the sub-signal with a correlation coefficient greater than 0.3 is selected for reconstruction.

导出引用

鞠景会1, 赵维刚2, 田秀淑3, 范博阳1, 石壮1, 郑伟康1 . 基于VMD的隧道空洞小波包能量熵提取方法研究[J]. 振动与冲击, 2024, 43(22): 199-208

JU Jinghui1, ZHAO Weigang2, TIAN Xiushu3, FAN Boyang1, SHI Zhuang1, ZHENG Weikang1. Wavelet packet decomposition energy entropy extraction method for the tunnel void desease based on VMD[J]. Journal of Vibration and Shock, 2024, 43(22): 199-208

参考文献

[1] 巩江峰,王伟,黎旭,等. 截至2022年底中国铁路隧道情况统计及2022年新开通项目重点隧道概况[J]. 隧道建设(中英文), 2023, 43(4): 721.
GONG Jiangfeng, WANG Wei, LI Xu, et al. Statistics of railway tunnels in China by the end of 2022 and overview of key tunnels of projects newly put into operation in 2022[J]. Tunnel Construction, 2023, 43(4): 721.
[2] 郭海鹏,王永红,安业成，等. 铁路隧道衬砌空洞成因分析及整治[J]. 隧道建设(中英文). 2020, 40(S1): 444-453.
GUO Haipeng, WANG Yonghong, AN Yecheng, et al. Cause analysis and renovation of railway tunnel lining cavity[J]. Tunnel Construction.2020, 40(S1): 444-453.
[3] 尹涛,王瑞燕,高鹏,等.基于探地雷达技术的隧道二衬缺陷探测研究[J].地下空间与工程学报, 2018, 14(S1): 220-226.
YIN Tao, WANG Rui-yan, GAO Peng, et al. Research on defect detection of tunnel secondary lining based on ground penetrating radar technology[J]. Journal of Underground Space and Engineering, 2018, 14(S1): 220-226.
[4] 吴刚,李再帏,朱文发,等.基于空耦超声导波的无砟轨道 CA 砂浆脱空检测方法［J］.铁道科学与工程学报，2019，16（6）：1375−1383.
WU Gang，LI Zaiwei，ZHU Wenfa，et al. Ballastless track CA mortar void detection method based on air-coupled ultrasonic guided wave[J]. Journal of Railway Science and Engineering，2019,16(6):1375−1383.
[5] 马晔.混凝土结构缺陷的红外热成像检测识别技术[J]. 公路交通科技, 2017, 34(12): 59-65.
MA Ye. Infrared thermal imaging detection and identification technology for concrete structural defects[J]. Highway traffic technology, 2017, 34(12): 59-65.
[6] S. Zhao, P. Shangguan, I.L. Al-Qadi, Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data, NDT & E International. 2015, 73: 1-7.
[7] 朱自强,喻波,密士文,等.超声波在混凝土中的衰减特征[J]. 中南大学学报(自然科学版), 2014, 11: 3900-3907.
ZHU Ziqiang, YU Bo, MI Shiwen, et al.Ultrasonic attenuation characteristics of ultrasonic in concrete[J].Journal of Central South University (Science and Technology), 2014, 11: 3900-3907.
[8] 谢春霞.红外热成像技术在水泥混凝土无损检测中的新发展[J].公路,2008(11): 161-164.
XIE Chunxia.New development of infrared thermal imaging technology in nondestructive testing of cement concrete[J]. Highway, 2008(11): 161-164.
[9] 高磊,刘振奎,魏晓悦,等.铁路隧道二次衬砌敲击检查声音特征分析及智能识别[J].铁道科学与工程学报.2022, 19(07): 1997-2004.
GAO Lei, IU Zhenkui, EI Xiaoyue, et al. Feature analysis and intelligent recognition of percussion inspection sound of secondary lining in railroad tunnel[J]. Journal of Railway Science and Engineering. 2022, 19(07): 1997-2004.
[10] 窦顺,贺磊,郑静,等.模态分析在隧道二衬脱空声振检测中的应用[J].铁道工程学报.2018, 35(03): 52-56.
DOU Shun，HE Lei，ZHENG Jing，et al. Application of modal analysis in acoustic-ibration detection of tunnel secondary liner void[J]. Journal of Rilway Way Engineering Society. 2018, 35(03): 52-56.
[11] 赵维刚,鞠景会,田秀淑,等.基于声频阈值估计的混凝土空洞快速识别方法研究[J].振动与冲击.2023，42(24): 136-142.
ZHAO Weigang，JU Jinghui，TIAN Xiushu, et al. A fast recognition method of concrete void based on acoustic frequency threshold estimation [J]. Journal of Vibration and Shock. 2023,42(24): 136-142.
[12] 余腾,胡伍生,吴杰,等. 基于小波阈值去噪与EMD分解方法提取润扬大桥振动信息[J]. 振动与冲击. 2019, 38(12): 264-270.
YU Teng, HU Wusheng, WU Jie, et al. Extraction of Runyang bridge vibration information based on a fusion method of wavelet threshold denoising and EMD decomposition[J]. Journal of Vibration and Shock. 2019, 38(12): 264-270.
[13] 邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击. 2012, 31(08): 96-101.
SHAO Renping，CAO Jingming，LI Yonglong. Gear fault pattern identification and diagnosis using Time-Frequency Analysis and wavelet threshold de-noising based on EMD[J]. Journal of Vibration and Shock. 2012, 31(08): 96-101.
[14] 李大柱,牛江,梁树林,等.基于多尺度时频图与卷积神经网络的车轮故障智能诊断[J].铁道科学与工程学报. 2023, 20(03): 1032-1043.
LI Dazhu, NIU Jiang, LIANG Shuling, et al. Intelligent wheel fault diagnosis based on multi-scale time-frequency map and convolutional neural network[J]. Journal of Railway Science and Engineering. 2023, 20(03): 1032-1043.
[15] WU Edmondqi, WANG Jin, PENG Xianyong, et al. Fault diagnosis of rotating machinery using gaussian process and EEMD‐treelet[J]. International Journal of Adaptive Control and Signal Processing. 2019, 33(1): 52-73.
[16] LEI Yaguo, HE Zhengjia, ZI Yanyang. EEMD method and WNN for fault diagnosis of locomotive roller bearings[J]. Expert Systems with Applications. 2011, 38(6): 7334-7341.
[17] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing. 2014, 62(3): 531-544.
[18] 尹佳璠,陈小奇,李世林,等.优化VMD与NLM结合的信号去噪[J].计算机工程与设计.2021, 42(04): 1135-1142.
YIN Jiafan, CHEN Xiaoqi, LI Shilin, et al. Signal denoising based on optimized VMD and NLM[J]. Computer Engineering and Design. 2021, 42(04): 1135-1142.
[19] 周小龙,徐鑫莉,王尧,等.基于变分模态分解和最大重叠离散小波包变换的齿轮信号去噪方法[J].振动与冲击. 2021, 40(12): 265-274.
ZHOU Xiaolong，XU Xinli，WANG Yao, et al. A gear signal de-noising method based on variational mode decomposition and maximal overlap discrete wavelet packet transform[J]. Journal of Vibration and Shock. 2021, 40(12): 265-274.
[20] 戴前伟,丁浩,张华,等.基于变分模态分解和奇异谱分析的GPR信号去噪[J].吉林大学学报(地球科学版).2022, 52(03): 701-712.
DAI Qianwei, DING Hao, ZHANG Hua, et al. Noise reduction method of GPR signal based on VMD-SSA[J]. Journal of Jilin University(Earth Science Edition ),2022, 52(03): 701-712.
[21] JIN Zhenzhen, HE Deqiang, LAO Zhenpeng, et al. Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM[J]. Nonlinear Dynamics. 2023, 111(6): 5287-5306.
[22] ZHAO Na, SU Yingxin, WANG Shijuan, et al. Chatter detection in variable cutting depth side milling using VMD and vibration characteristics analysis[J]. Electronics. 2022, 11(22): 3779.
[23] 曹志远.板壳振动理论[M].北京:中国铁道出版社,1983.
CAO Zhiyuan. Vibration theory of plates and shells[M]. Bei Jing: China Railway Publishing House,1983.
[24] 杜圆,李海超,庞福振,等.任意边界条件下矩形板薄板自由振动特性分析[J].振动与冲击.2019, 38(19):70-76.
DU Yuan, LI Haichao, Pang Fuzhen, Free vibration characteristics of rectangular thin plates under arbitrary boundary conditions [J]. Journal of Vibration and Shock. 2019. 38 (19): 70-76.
[25] LEISSA AW. The free vibration of rectangular plates[J]. Journal of Sound and Vibration.1973,31: 257–293.
[26] 赵盛杰.基于冲击回波的混凝土桥面板分层缺陷检测方法研究[D]. 大连理工大学, 2021.
ZHAO Shengjie. Research on Delamination Detection in Concrete Bridge Decks using Impact-Echo Method [D]. Dalian University of Technology, 2021.
[27] SEONGHOON Kee, NENAD Gucunski. Interpretation of Flexural Vibration Modes from Impact-Echo Testing[J]. Journal of Infrastructure Systems.2016:1-10.
[28] 杨健,孙涛,陈小龙,等.基于VMD-EWT-IASSP-EBILSTM的短期电力负荷预测[J]. 科学技术与工程. 2023, 23(27): 11646-11654.
YANG Jian, SUN Tao, CHEN Xiaolong, et al. Short-term power load forecasting based on VMD-EWT-IASSP-EBILSTM[J]. Science Technology and Engineering. 2023, 23(27): 11646-11654.
[29] 曾宪伟,赵卫明,盛菊琴.小波包分解树结点与信号子空间频带的对应关系及其应用[J]. 地震学报.2008(01): 90-96.
ZENG Xianwei, ZHAO Weiming, SHENG Juqin. Corresponding relationships between nodes of decomposition tree of wavelet packet and frequency bands of signal subspace[J]. Acta Seismologica Sinica. 2008(01): 90-96.
[30] TAEKEUN Oh, JOHN S. Popovics, SUYUN Ham, et al. Improved Interpretation of Vibration Responses from Concrete Delamination Defects Using Air-Coupled Impact Resonance Tests[J]. Journal of engineering mechanics，2013,139(3): 315-324.