Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising
Restricted accessResearch articleFirst published online September, 2022
Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising
To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.
AbediMGharehchopoghFS (2020) An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems. Intelligent Data Analysis24(2): 309–338.
2.
Ayenu-PrahAAttoh-OkineN (2010) A criterion for selecting relevant intrinsic mode functions in empirical mode decomposition. Advances in Adaptive Data Analysis2(1): 1–24.
3.
ChengJSLiMJOuLH, et al. (2018) FA-PMA-VMD method and its application in gear tooth root crack fault diagnosis. Journal of Vibration and Shock2018(15): 27–32.
4.
daCruzRPdaSilvaFVFiletiAMF (2020) Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines. Clean Technologies and Environmental Policy22(3): 627–638.
5.
DiaoXJiangJShenG, et al. (2020) An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines. Mechanical Systems and Signal Processing143: 106787.1–106787.17.
6.
DragomiretskiyKZossoD (2014) Variational mode decomposition. IEEE Transactions on Signal Processing62(3): 531–544.
7.
GaiJShenJHuY, et al. (2020) An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing. Measurement162: 107901.1–107901.12.
8.
LangXLiPHuZ, et al. (2017) Leak detection and location of pipelines based on LMD and least squares twin support vector machine. IEEE Access5: 8659–8668.
9.
LiHChangJXuF, et al. (2019) Efficient lidar signal denoising algorithm using variational mode decomposition combined with a whale optimization algorithm. Remote Sensing11(2): 126.1–126.15.
10.
LiJFengC (2017) Pipeline leak location detection based on EEMD and second correlation. Nami Jishu Yu Jingmi Gongcheng/Nanotechnology and Precision Engineering15(5): 372–377.
11.
LiJChenYQianZ, et al. (2020) Research on VMD based adaptive denoising method applied to water supply pipeline leakage location. Measurement151: 107153.1–107153.13.
12.
LiXLWanPZhangHT, et al. (2018) The application research of Internet of Things to oil pipeline leak detection. In: Proceedings of the 2018 15th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), Chengdu, China, 14–16 December, pp. 211–214. New York: IEEE.
13.
LianJLiuZWangH, et al. (2018) Adaptive variational mode decomposition method for signal processing based on mode characteristic. Mechanical Systems and Signal Processing107: 53–77.
14.
LiuCLiYFangL, et al. (2017a) Leakage monitoring research and design for natural gas pipelines based on dynamic pressure wave. Journal of Process Control50: 66–76.
15.
LiuYWangJLiY, et al. (2017b) Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve. Journal of Vibroengineering19(8): 6007–6020.
16.
LuJYueJZhuL, et al. (2020) Variational mode decomposition denoising combined with improved Bhattacharyya distance. Measurement151: 107283.1–107283.11.
17.
LuoSYangWLuoY (2020) Fault diagnosis of a rolling bearing based on adaptive sparest narrow-band decomposition and refined composite multiscale dispersion entropy. Entropy22(4): 375.1–375.20.
18.
PengZBLiuYCHanXF (2012) Application research of the signal processing method based on Db5 wavelet transform in leakage location of ship pipeline. Applied Mechanics & Materials203: 103–107.
19.
QuyTBMuhammadSKimJM (2019) A reliable acoustic emission based technique for the detection of a small leak in a pipeline system. Energies12(8): 1472.1–1472.18.
20.
RostaghiMAzamiH (2016) Dispersion entropy: A measure for time-series analysis. IEEE Signal Processing Letters23(5): 610–614.
21.
SantosRBorgesGSantosA, et al. (2018) A semi-autonomous particle swarm optimizer based on gradient information and diversity control for global optimization. Applied Soft Computing69: 330–343.
22.
ShaoSPengYHeC, et al. (2020) Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Transactions97: 415–430.
23.
WahidFGhazaliRIsmailLH (2019) Improved firefly algorithm based on genetic algorithm operators for energy efficiency in smart buildings. Arabian Journal for Science and Engineering44(4): 4027–4047.
24.
WangCZhangYSongJ, et al. (2019) A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection. Systems Science & Control Engineering7(1): 75–88.
25.
WangHWuFZhangL (2021) Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis. Alexandria Engineering Journal60(5): 4689–4699.
26.
WangJZhaoLLiuT, et al. (2016) Novel negative pressure wave-based pipeline leak detection system using fiber Bragg grating-based pressure sensors. Journal of Lightwave Technology35(16): 3366–3373.
27.
WeiDJiangHShaoH, et al. (2019) An optimal variational mode decomposition for rolling bearing fault feature extraction. Measurement Science and Technology30(5): 055004.1–055004.26.
28.
WeiZLiuJLuZ (2018) Structural damage detection using improved particle swarm optimization. Inverse Problems in Science and Engineering26(6): 792–810.
29.
WuHChenYLinW, et al. (2018) Novel signal denoising approach for acoustic leak detection. Journal of Pipeline Systems Engineering and Practice9(4): 04018016.1–04018016.9.
30.
XiaoQYLiJBaiZL, et al. (2016) A small leak detection method based on VMD adaptive de-noising and ambiguity correlation classification intended for natural gas pipelines. Sensors16(12): 2116.1–2116.16.
31.
XuBZhouFLiH, et al. (2019) Early fault feature extraction of bearings based on Teager energy operator and optimal VMD. ISA Transactions86: 249–265.
32.
XueJShenB (2020) A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering8(1): 22–34.
33.
YanXJiaM (2019) Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection. Knowledge-Based Systems163: 450–471.
34.
YanXLiuYZhangW, et al. (2020) Research on a novel improved adaptive variational mode decomposition method in rotor fault diagnosis. Applied Sciences10(5): 1696.1–1696.27.
35.
ZhaoJTangJShiA, et al. (2020a) Improved density peaks clustering based on firefly algorithm. International Journal of Bio-Inspired Computation15(1): 24–42.
36.
ZhaoYMaFZhaoN, et al. (2020b) Study on acoustic detection method of gas pipeline leakage. IOP Conference Series: Earth and Environmental Science558(2): 0220321.
