# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 1 Introduction  
Municipal piping systems are widely used to supply gas or water for daily activities. Such systems have become nationally important assets in large-scale industry for conveying different types of fluids--such as oil, petroleum, and chemicals--over a long distance. Even though the pipeline system is designed and installed to industrial standards, the fault could happen due to long-lasting use, corrosion, or error in material, which generate the impending leakage [1, 2]. A pipeline leak not only causes economic loss, but also poses safety and environmental risks. Since leaks are inevitable, monitoring the condition of the pipelines and life-cycle maintenance have obtained considerable attention over the past years. Several methodologies have been proposed for leak detection in pipeline systems, including fiber optic, electromagnetic, visual inspection, and acoustic emission [3, 4, 5]. Recently, acoustic emission (AE) has been demonstrated to be effective for leakage detection due to advances in data acquisition and processing speeds required to handle the vast amount of signals gathered [6, 7]. Furthermore, AE sensors provide enough sensitivity to instantly identify any abnormal state in pipelines. AE-based methods have been employed in numerous studies to detect leaks by modeling them as classification problems [8, 9, 10, 11]. These data-driven approaches are reasonable because the AE signal from leakage is non-stationary and becomes difficult to find in an explicit model for leak detection. However, when the leak occurs at different positions in a pipeline, it would return dissimilar AE sensor signals due to the wave dispersion principle [12]. In addition, the wave attenuation depends on the frequency [13], and the AE signals will be distorted by their own propagation as well as the influence of ambientnoise. To address this problem, de-noising and decomposition techniques were applied to search the most intrinsic components related to leakage syndromes for individual sensors, because AE signal is not complete after it is analyzed in the time-frequency domain compared with the original signal recorded from sensors. Thus, the analyzed signals differ from each other in the case of the leak's position changing. Consequently, classifiers that mine knowledge from separate signal channels would result in misclassification. As leakage occurs, the leaking fluids have a turbulent flow; due to impact force around the leak point, this stress induces a detectable acoustic wave inside a duct that propagates along its length [14]. When a solid wall of the pipe is stimulated by an impact, it generates a dynamic vibration, which is represented by corresponding movement equations. The exhaustion of fluid generates an AE signal that causes radial vibration. The AE sensors attached on the outside of the pipe can detect an internal pipeline signal. Some research has been performed to analyze the AE characteristics of the source signal, propagation of the wave along the pipeline, and the relationships of the AE signal with various parameters, such as flow rate, propagating distance, material, and operating conditions [15; 16]. The knowledge obtained via pipe wall stimulation by AE springing from leakage is a skeleton key for fault detection and locating the leak position. However, signals from the leak are non-stationary and are unavoidably contaminated with non-leak sounds. The leak signal that emerges from an abrupt change in flow through a perforation conventionally includes transient bursts. Bursts usually come from abnormal points--such as bending segments, cracks, or leaks--or collisions inside the pipeline. The arrival time and the amplitude of bursts vary with their strength and the location of their source. Therefore, such bursts can provide information about the leak [17]. During detection of a burst event, it is also important to calculate the time difference in arrival between the sensors, which helps to localize the leak position.