A model-based health indicator for leak detection in gas pipeline systems

Abstract Leakage in gas pipelines is becoming a significant issue and has attracted much attention in recent years. This paper is concerned with the development of a robust health indicator for identifying the leakage in gas pipeline systems. A spectral exponent indicator is proposed based on a theoretical leak noise spectrum model. Measurements of the leak acoustic signals are also presented from a pipe rig with air under pressure. Then, a feature selection technique is employed to select properly desired features. Three data-driven approaches, artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF) are trained with the most discriminative features. The proposed methodology showed to achieve 99.4%, 99.6% and 99.6% accuracies for ANN, SVM and RF respectively. Furthermore, the proposed indicator showed to be robust under different conditions illustrating its ability for applications in the field.

[1]  Sez Atamturktur,et al.  Sustainability Analysis of a Leakage-Monitoring Technique for Water Pipeline Networks , 2020 .

[2]  Sriram Narasimhan,et al.  Long-Term Monitoring for Leaks in Water Distribution Networks Using Association Rules Mining , 2020, IEEE Transactions on Industrial Informatics.

[3]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[4]  Sez Atamturktur,et al.  Novel vibration-based technique for detecting water pipeline leakage , 2017 .

[5]  Roya A. Cody,et al.  Detecting Leaks in Water Distribution Pipes Using a Deep Autoencoder and Hydroacoustic Spectrograms , 2020, J. Comput. Civ. Eng..

[6]  Fei Wang,et al.  Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM , 2014 .

[7]  Sez Atamturktur,et al.  Experimental evaluation of a vibration-based leak detection technique for water pipelines , 2018 .

[8]  Ian F. C. Smith,et al.  Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks , 2013, Adv. Eng. Informatics.

[9]  Shuaiyong Li,et al.  Leak location in gas pipelines using cross-time–frequency spectrum of leakage-induced acoustic vibrations , 2014 .

[10]  M. Troncossi,et al.  Vibroacoustic Measurements for Detecting Water Leaks in Buried Small-Diameter Plastic Pipes , 2017 .

[11]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[12]  Hongfang Lu,et al.  Leakage detection techniques for oil and gas pipelines: State-of-the-art , 2020 .

[13]  Alessandro Rivola,et al.  Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements , 2015 .

[14]  Yehia A. Khulief,et al.  Acoustic Detection of Leaks in Water Pipelines Using Measurements inside Pipe , 2012 .

[15]  Wei Liang,et al.  Acoustic detection technology for gas pipeline leakage. , 2013 .

[16]  Pierre Beauseroy,et al.  Reliable leak detection in a heat exchanger of a sodium-cooled fast reactor , 2020 .

[17]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[18]  Paulo Seleghim,et al.  Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids , 2010 .

[19]  Xiaobo Qiu,et al.  Pipeline Leak Detection by Using Time-Domain Statistical Features , 2017, IEEE Sensors Journal.

[20]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

[21]  Jing Wen,et al.  Adaptive noise cancellation based on EMD in water-supply pipeline leak detection , 2016 .

[22]  Suzhen Li,et al.  Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition , 2018 .

[23]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[24]  E. Rustighi,et al.  Estimating the spectrum of leak noise in buried plastic water distribution pipes using acoustic or vibration measurements remote from the leak , 2021 .

[25]  Cuiwei Liu,et al.  Sound–turbulence interaction model for low mach number flows and its application in natural gas pipeline leak location , 2020 .

[26]  Jong-Myon Kim,et al.  Leak detection in a gas pipeline using spectral portrait of acoustic emission signals , 2020 .

[27]  Michael J. Brennan,et al.  A model of the correlation function of leak noise in buried plastic pipes , 2004 .

[28]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[29]  Jong-Myon Kim,et al.  Discriminative feature analysis based on the crossing level for leakage classification in water pipelines. , 2019, The Journal of the Acoustical Society of America.

[30]  Jinhui Zhao,et al.  A novel hybrid technique for leak detection and location in straight pipelines , 2015 .

[31]  Qiyang Xiao,et al.  Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis , 2016 .

[32]  Phillip Joseph,et al.  The leak noise spectrum in gas pipeline systems: Theoretical and experimental investigation , 2020 .

[33]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[34]  Jie Li,et al.  Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine , 2019, Measurement.

[35]  Li Yuxing,et al.  Experimental study on leak detection and location for gas pipeline based on acoustic method , 2012 .

[36]  Wei Liang,et al.  Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline , 2016 .

[37]  Gangbing Song,et al.  Experimental study of pipeline leak detection based on hoop strain measurement , 2015 .

[38]  Yu Chen,et al.  Novel Signal Denoising Approach for Acoustic Leak Detection , 2018 .

[39]  Hazem Nounou,et al.  Chronic leak detection for single and multiphase flow: A critical review on onshore and offshore subsea and arctic conditions , 2020 .

[40]  Lin Zhao,et al.  Novel Negative Pressure Wave-Based Pipeline Leak Detection System Using Fiber Bragg Grating-Based Pressure Sensors , 2017, Journal of Lightwave Technology.

[41]  Ioan Silea,et al.  A survey on gas leak detection and localization techniques , 2012 .

[42]  Minghai Xu,et al.  An integrated detection and location model for leakages in liquid pipelines , 2019, Journal of Petroleum Science and Engineering.

[43]  Anthony W. Papageorgiou,et al.  In-pipe fibre optic pressure sensor array for hydraulic transient measurement with application to leak detection , 2018, Measurement.

[44]  Eslam Mohammed Abdelkader,et al.  An accelerometer-based leak detection system , 2018 .

[45]  Quan Pan,et al.  Two denoising methods by wavelet transform , 1999, IEEE Trans. Signal Process..

[46]  Phillip Joseph,et al.  On the Acoustic Filtering of the Pipe and Sensor in a Buried Plastic Water Pipe and its Effect on Leak Detection: An Experimental Investigation , 2014, Sensors.

[47]  Janet F.C. Sham,et al.  Perturbation mapping of water leak in buried water pipes via laboratory validation experiments with high-frequency ground penetrating radar (GPR) , 2016 .

[48]  Lei Ni,et al.  An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines , 2020 .

[49]  Hong-Nan Li,et al.  Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine , 2018, Structural Control and Health Monitoring.

[50]  Roya A. Cody,et al.  Linear Prediction for Leak Detection in Water Distribution Networks , 2020 .

[51]  Jiheon Kang,et al.  Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems , 2018, IEEE Transactions on Industrial Electronics.

[52]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .