Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline

Acoustic emission bursts are signal waveforms that include a number of consecutive imbrication transients with variable strengths and contain crucial information on the leakage phenomenon in a pipeline system. Detection and isolation of a burst against the background signal increases the ability of a pipe’s fault diagnosis system. This paper proposes a methodology using the Enhanced Constant Fault Alarm Rate (ECFAR) to detect bursts and exploit the burst phenomenon in acoustic emission. The extracted information from the burst waveform is used to distinguish several levels of leakage in a laboratory leak-off experimental testbed. The multi-class support vector machine in the one-against-all method is established as the classifier. The results are compared with those of the wavelet threshold-based method, another algorithm utilized for impulse and burst detection, which indicates that the ECFAR method gives an ameliorative classification result with an accuracy of 93% for different levels of leakage.

[1]  Yj Song,et al.  Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks , 2018 .

[2]  Shantanu Datta,et al.  A review on different pipeline fault detection methods , 2016 .

[3]  Shimin Zhang,et al.  Natural gas pipeline valve leakage rate estimation via factor and cluster analysis of acoustic emissions , 2018, Measurement.

[4]  Jong-Myon Kim,et al.  Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines , 2018, Reliab. Eng. Syst. Saf..

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

[6]  P. He,et al.  Simulation of ultrasound pulse propagation in lossy media obeying a frequency power law. , 1998, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[7]  Adnan M. Abu-Mahfouz,et al.  Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview , 2017, IEEE Access.

[8]  Alessandro Rivola,et al.  Leak Detection in Water-Filled Small-Diameter Polyethylene Pipes by Means of Acoustic Emission Measurements , 2016 .

[9]  Zheng Liu,et al.  State of the art review of inspection technologies for condition assessment of water pipes , 2013 .

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

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

[12]  Myeongsu Kang,et al.  Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[13]  Gerardo G. Acosta,et al.  Accumulated CA–CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data , 2015, IEEE Journal of Oceanic Engineering.

[14]  Dang Lu,et al.  Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology , 2018, Sensors.

[15]  Jay N. Meegoda,et al.  Acoustic Emission Leak Detection on a Metal Pipeline Buried in Sandy Soil , 2013 .

[16]  Amir Mostafapour,et al.  A theoretical and experimental study on acoustic signals caused by leakage in buried gas-filled pipe , 2015 .

[17]  Cheng Siong Chin,et al.  Review of Current Technologies and Proposed Intelligent Methodologies for Water Distributed Network Leakage Detection , 2018, IEEE Access.

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

[19]  Jong-Myon Kim,et al.  A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System , 2019, Energies.

[20]  Jian Li,et al.  Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum , 2018, Measurement.

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

[22]  Manuel Rosa-Zurera,et al.  Radar detection with the Neyman–Pearson criterion using supervised-learning-machines trained with the cross-entropy error , 2013, EURASIP J. Adv. Signal Process..

[23]  Jong-Myon Kim,et al.  Leak localization in industrial-fluid pipelines based on acoustic emission burst monitoring , 2020 .

[24]  Bhavin Engineer,et al.  Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System , 2018, Applied Sciences.

[25]  Mudasser Iqbal,et al.  Wavelet-based Burst Event Detection and Localization in Water Distribution Systems , 2013, J. Signal Process. Syst..