Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (4): 959-969.doi: 10.13229/j.cnki.jdxbgxb20200889

Previous Articles    

Feature extraction method of pipeline signals based on VMD de-noising and dispersion entropy

Yi-na ZHOU1,2(),Hong-li DONG1,2,3,Yong ZHANG4,Jing-yi LU1,2,3()   

  1. 1.Artificial Intelligence Energy Research Institute，Northeastern Petroleum University，Daqing 163318，China
    2.Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control，Northeastern Petroleum University，Daqing 163318，China
    3.Sanya Offshore Oil & Gas Research Institute，Northeast Petroleum University，Sanya 572024，China
    4.Institute of Electronic Science，Northeastern Petroleum University，Daqing 163318，China
  • Received:2020-11-19 Online:2022-04-01 Published:2022-04-20
  • Contact: Jing-yi LU E-mail:1182090720@qq.com;ljywdm@126.com

RICH HTML

 7   

PDF (PC)

520

Abstract:

In view of the non-linear and non-stationary characteristics of pipeline acoustic signals and the difficulty of extracting the characteristics of pipeline leakage signal， a feature extraction method for acoustic signal was proposed. Firstly， the variational mode decomposition （VMD） was used to de-noise the collected acoustic signal， during which process the mode number of VMD was determined by the method of Minimum Bhattacharyya distance. Then the Wasserstein Distance （WD） between the probability density of the （Intrinsic Mode Function） IMF components was obtained by VMD and the original signal， which was evaluated to select the effective modes so as to reconstruct the selected effective modes. Finally， the dispersion entropy value of the reconstructed signal was used as the signal characteristic parameter， and the characteristic parameter was input into the extreme learning machine （ELM） to recognize the working condition. Experimental results show that the proposed method could classify and recognize pipeline signals accurately， and the total recognition rate is up to 100%.

Key words: pipeline leakage, variational mode decomposition, Wasserstein distance, dispersion entropy, feature extraction

CLC Number: 

  • TN911.72

Fig. 1

Flow chart of determining number of K-value based on Bdmin"

Fig.2

Flow chart of selecting effective mode component"

Fig.3

Flow chart of proposed method"

Fig.4

Time domain waveform and spectrum of threeworking conditions signals"

Table 1

Bdmin at different K"

KBdminKBdmin
20.006260.0064
30.009470.0062
40.012880.0000
50.009490.0000

Fig.5

Decomposition results of VMD"

Fig.6

WD of pdf between IMF and original signal"

Fig.7

Time domain waveform and spectrum diagram of de-noised leakage signal"

Fig.8

Denoised signals of pipeline under three conditions"

Fig.9

Dispersion entropy curve of denoised signal under different working conditions"

Table 2

Data set allocation"

管道工况训练集/个测试集/个类别标签
敲击80201
泄漏80202
正常80203

Fig.10

Classification results of test sets"

Table 3

Identification results of test sets"

方 法准确率/%
训练准确率测试准确率
VMD?DE?ELM100100
DE?ELM92.0890
NLM?DE?ELM99.1795
VMD?SSE?ELM77.576.67
VMD?IE?ELM97.0893.33
VMD?BE?ELM82.580
VMD?SE?ELM99.1798.33
VMD?ELM8575
ELM80.1773.33
1 Pan S S, Xu Z D, Li D S, et al. Research on detection and location of fluid-filled pipeline leakage based on acoustic emission technology[J]. Sensors, 2018, 18(11): No. 3628.
2 Zhu J X, Ren L, Ho S C, et al. Gas pipeline leakage detection based on PZT sensors[J]. Smart Materials and Structures, 2017, 26(2): No. 025022.
3 刘翠伟,敬华飞,方丽萍,等. 输气管道泄漏声波衰减模型的理论研究[J]. 振动与冲击, 2018, 37(20): 109-114.
Liu Cui-wei, Jing Hua-fei, Fang Li-ping, et al. A theoretical study of acoustic attenuation model of gas pipeline leakage [J]. Vibration and Shock, 2018, 37(20): 109-114.
4 曹雪伟,孙首群,宣立明,等. 小波分析在管道泄漏信号去噪中的优化研究[J]. 石油化工自动化, 2019, 55(1): 29-34.
Cao Xue-wei, Sun Shou-qun, Xuan Li-ming, et al. Optimization of wavelet analysis in denoising of pipeline leakage signals[J]. Petrochemical Automation, 2019, 55(1): 29-34.
5 Yu X C, Liang W, Zhang L B, et al. Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in lead detection for natural gas pipeline[J]. Mechanical Systems and Signal Processing, 2016, 72: 266-285.
6 赵利强,王建林,于涛. 基于改进EMD的输油管道泄漏信号特征提取方法研究[J]. 仪器仪表学报, 2013, 34(12): 2696-2702.
Zhao Li-qiang, Wang Jian-lin, Yu Tao. Study on feature extraction method of oil pipeline leakage signal based on improved EMD[J]. Chinese Journal of Scientific Instrument, 2013, 34(12): 2696-2702.
7 Liang X M, Li P, Hu Z Y, et al. Leak detection and location of pipelines based on LMD and least squares twin support vector machine[J]. IEEE Access, 2017,5: 8659-8668.
8 周颖涛,周绍骑,姚远航,等. 基于EEMD的声发射管道泄漏定位检测[J]. 四川兵工学报, 2015, 36(3): 110-113, 116.
Zhou Ying-tao, Zhou Shao-qi, Yao Yuan-hang, et al. Leakage location detection of acoustic emission pipeline based on EEMD[J]. Sichuan Journal of military Industry, 2015, 36(3): 110-113, 116.
9 周怡娜,路敬祎,董宏丽,等. 云相似度测量的变分模态分解去噪方法[J]. 吉林大学学报: 信息科学版, 2020, 38(1): 9-17.
Zhou Yi-na, Lu Jing-yi, Dong Hong-li, et al. The denoising method of variational mode decomposition based on cloud similarity measurement[J]. Journal of Jilin University (Information Science Edition), 2020, 38(1): 9-17.
10 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 Mathematical Physical and Engineering ences, 1998, 454(1971): 903-995.
11 李余兴,李亚安,陈晓,等. 基于VMD和中心频率的舰船辐射噪声特征提取方法研究[J]. 振动与冲击, 2018, 37(23): 213-218, 285.
Li Yu-xing, Li Ya-an, Chen Xiao, et al. Research on the extraction method of ship radiation noise based on VMD and central frequency[J]. Vibration and Impact, 2018, 37(23): 213-218, 285.
12 吴文轩,王志坚,张纪平,等. 基于峭度的VMD分解中K值的确定方法研究[J]. 机械传动, 2018, 42(8): 153-157.
Wu Wen-xuan, Wang Zhi-jian, Zhang Ji-ping, et al. Research on the method of determining K value in VMD decomposition based on kurtosis[J]. Mechanical Transmission, 2018, 42(8): 153-157.
13 Yang H B, Liu S L, Zhang H L. Adaptive estimation of VMD modes number based on cross correlation coefficient[J]. Journal of Vibroengineering, 2017, 19(2): 1185-1196.
14 昝涛,庞兆亮,王民,等. 基于VMD的滚动轴承早期故障诊断方法[J]. 北京工业大学学报, 2019, 45(2): 103-110.
Zan Tao, Pang Zhao-liang, Wang Min, et al. Early fault diagnosis method of rolling bearings based on VMD[J]. Journal of Beijing University of Technology, 2019, 45(2): 103-110.
15 Li Y, Li Y, Chen X,et al. Research on ship- radiated noise denoising using secondary variational mode decomposition and correlation coefficient[J]. Sensors, 2017, 18(1): No. 18010048.
16 周怡娜,路敬祎,张勇,等. VMD-SG-WT去噪法及其在混沌去噪中的应用[J]. 东北石油大学学报, 2020, 44(4): 113-120, 132.
Zhou Yi-na, Lu Jing-yi, Zhang Yong, et al. VMD-SG-WT denoising method and its application in chaotic denoising[J]. Journal of Northeast Petroleum University, 2020, 44(4): 113-120, 132.
17 Zhang X, Miao Q, Zhang H, et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 108: 58-72.
18 周洋,向阳,黄陈哲,等. 基于VMD⁃SVD能量标准谱⁃Teager能量算子的轴承故障诊断方法[J]. 噪声与振动控制, 2019, 39(4): 212-217, 253.
Zhou Yang, Xiang Yang, Huang Chen-zhe, et al. The fault diagnosis method for bearing Based on VMD-SVD energy standard spectrum-Teager energy operator[J]. Noise and Vibration Control, 2019, 39(4): 212-217, 253.
19 任学平,李攀,王朝阁,等. 基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击, 2018, 37(15): 6-13.
Ren Xue-ping, Li Pan, Wang Chao-ge, et al. Early fault diagnosis of rolling bearing based on improved VMD and envelope derivative energy operator[J]. Vibration and Shock, 2018, 37(15): 6-13.
20 王秀芳,檀丽丽,姜春雷,等. 基于互信息的VMD算法在管道泄漏检测中的应用[J]. 压力容器, 2017, 34(8): 75-80.
Wang Xiu-fang, Tan Li-li, Jiang Chun-lei, et al. The application of VMD algorithm based on mutual information in pipeline leakage detection[J]. Pressure vessel, 2017, 34(8): 75-80.
21 Chang L, Cao H, Shen C. Dual-Mass MEMS Gyroscope parallel denoising and temperature compensation processing based on WLMP and CS-SVR[J]. Micromachines, 2020, 11(6): 586.
22 李帅永,夏传强,程振华,等. 基于VMD和互谱分析的供水管道泄漏定位方法[J]. 仪器仪表学报, 2019, 40(7): 195-205.
Li Shui-yong, Xia Chuan-qiang, Cheng Zhen-hua, et al. Water supply pipeline leakage location method Based on VMD and mutual spectrum analysis[J]. Journal of Instrumentation, 2019, 40(7): 195-205.
23 Ma W P, Yin S X, Jiang C L, et al. Variational mode decomposition denoising combined with the Hausdorff distance[J]. Review of Entific Instruments, 2017, 88(3): No. 035109.
24 Yang J, Wang X, Feng Z, et al. Research on pattern recognition method of blockage signal in pipeline based on LMD information entropy and ELM[J]. Mathematical Problems in Engineering Theory Methods & Applications, 2017(3): 1-16.
25 王秀芳,王昕. EEMD近似熵和支持向量机的管道泄漏特征向量提取研究[J]. 化工自动化及仪表, 2015, 42(11): 1226-1229.
Wang Xiu-fang, Wang Xin. Study on EEMD approximate entropy and SVM pipeline leak feature extraction[J]. Control and Instruments in Chemical Industry, 2015, 42(11): 1226-1229.
26 冯雪松,文玉梅,甄锦鹏,等. 管道泄漏声振动信号的特征分析[J]. 声学技术, 2015, 34(5): 413-418.
Feng Xue-song, Wen Yu-mei, Zhen Jin-peng, et al. Characteristic analysis of acoustic vibration signals of pipeline leakage[J]. Acoustic Technology, 2015, 34(5): 413-418.
27 刘炜,刘宏昭. 应用模糊熵和相似度的管道泄漏检测与定位[J]. 机械科学与技术, 2016, 35(3): 437-443.
Liu Wei, Liu Hong-zhao. Application of fuzzy entropy and similarity in pipeline leakage detection and location [J]. Mechanical Science and Technology, 2016, 35(3): 437-443.
28 袁培龙,周绍骑,周颖涛,等. 基于内禀模态奇异值熵的声发射管道泄漏诊断[J]. 重庆理工大学学报, 2016, 30(11): 60-64.
Yuan Pei-long, Zhou Shao-qi, Zhou Ying-tao, et al. Diagnosis of acoustic emission pipeline leakage based on intrinsic mode singular value entropy[J]. Journal of Chongqing University of Technology, 2016, 30(11): 60-64.
29 孙洁娣,肖启阳,温江涛,等. 基于LMD包络谱熵及SVM的天然气管道微小泄漏孔径识别[J]. 机械工程学报, 2014, 50(20): 18-25.
Sun Jie-di, Xiao Qi-yang, Wen Jiang-tao, et al. Small leakage aperture identification of natural gas pipeline based on LMD envelope spectrum entropy and SVM[J]. Journal of Mechanical Engineering, 2014, 50(20): 18-25.
30 Rostaghi M, Azami H. Dispersion entropy: a measure for time-series analysis[J]. IEEE Signal Processing Letters, 2016: 23(5): 610-614.
31 Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
32 Kailath T. The divergence and bhattacharyya distance measures in signal selection[J]. IEEE Transactions on Communication Technology, 1967, 15(1): 52-60.
33 Lipman Y, Puente J, Daubechies I. Conformal wasserstein distance: II. computational aspects and extensions[J]. Mathematics of Computation, 2011, 82(281): 331-381.
34 Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1-3): 489-501.
35 梁洪卫. 油气管道泄漏声波信号检测与识别技术研究[D]. 大庆:东北石油大学电气信息工程学院,2019.
Liang Hong-wei. A study on detection and identification technology of oil and gas pipeline leakage acoustic signal [D]. Daqing: School of Electrical and Information Engineering, Northeast Petroleum University, 2019.
[1] Jin-hua WANG,Jia-wei HU,Jie CAO,Tao HUANG. Multi⁃fault diagnosis of rolling bearing based on adaptive variational modal decomposition and integrated extreme learning machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 318-328.
[2] Guo-fa LI,Yan-bo WANG,Jia-long HE,Ji-li WANG. Research progress and development trend of health assessment of electromechanical equipment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 267-279.
[3] Xiao⁃lei CHEN,Yong⁃feng SUN,Ce LI,Dong⁃mei LIN. Stable anti⁃noise fault diagnosis of rolling bearing based on CNN⁃BiLSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 296-309.
[4] Hong-kui XU,Tong-tong JIANG,Xin LI,Bin-xiang JIANG,Yong-lei WANG. LTE uplink interference analysis combined with denoising autoencoder and extreme learning machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 195-203.
[5] Gui-xia LIU,Zhi-yao PEI,Jia-zhi SONG. Prediction of protein-ATP binding site based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 187-194.
[6] Yuan-hong LIU,Pan-pan GUO,Yan-sheng ZHANG,Xin LI. Feature extraction of sparse graph preserving projection based on Riemannian manifold [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2268-2279.
[7] Hui ZHONG,Heng KANG,Ying-da LYU,Zhen-jian LI,Hong LI,Ruo-chuan OUYANG. Image manipulation localization algorithm based on channel attention convolutional neural networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1838-1844.
[8] Xiao-long ZHU,Zhong XIE. Automatic construction of knowledge graph based on massive text data [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1358-1363.
[9] Tao XU,Ke MA,Cai-hua LIU. Multi object pedestrian tracking based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 27-38.
[10] Yi-bin LI,Jia-min GUO,Qin ZHANG. Methods and technologies of human gait recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 1-18.
[11] GENG Qing-tian, YU Fan-hua, WANG Yu-ting, GAO Qi-kun. New algorithm for vehicle type detection based on feature fusion [J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.
[12] DONG Qiang, LIU Jing-hong, ZHOU Qian-fei. Improved SURF algorithm used in image mosaic [J]. 吉林大学学报(工学版), 2017, 47(5): 1644-1652.
[13] YIN Ming, ZHAN Yin-wei, PEI Hai-long. Co-sparse analysis operator learning for image fusion [J]. 吉林大学学报(工学版), 2016, 46(6): 2052-2058.
[14] XIAO Zhong-jie. Recognition of digital image based on wavelet space feature spectrum entropy [J]. 吉林大学学报(工学版), 2015, 45(6): 1994-1998.
[15] LIU Hong,SUN Shuang-zi,WANG Qing-yuan,LI Yan-zhong. PSO based feature extraction method for analog circuit fault information [J]. 吉林大学学报(工学版), 2015, 45(2): 675-680.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd