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

Duong, Bach Phi; Jeong, In-Kyu; Kim, Cheol Hong; Kim, Jong‐Myon

doi:10.3390/app10061933

Cited by 8 publications

(4 citation statements)

References 25 publications

(26 reference statements)

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“…The carried out measurements showed that the test object meets the requirements for pass-by noise specified in the TSI Noise (80 dB for vehicles traveling at 80 km/h). According to the TSI Noise guidelines, the obtained results for high-speed vehicles traveling at 200 km/h were normalized to the reference speed (80 km/h) by means of Equation (9). Based on the calculations performed, the tested high-speed vehicles obtained values of 76 dB for the straight section and 77 dB for the curve.…”

Section: Discussionmentioning

confidence: 99%

“…It is worth mentioning that the occurrence of noise, or acoustic emission, can also bring many benefits. Acoustic emission analysis is used in many sectors of transport and industry, among others, to detect and monitor damages, leaks, fatigue, or structural analysis of various materials (e.g., concrete, plastics, wood, ceramics) [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Identification of the Major Noise Energy Sources in Rail Vehicles Moving at a Speed of 200 km/h

Polak

Korzeb

2021

Energies

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In this work, the problematic identification of the main sources of noise occurring from the exploitation of railway vehicles moving at a speed of 200 km/h were analyzed. Within the conducted experimental research, the testing fields were appointed, measurement apparatus selected, and a methodology for conducting measurements was defined, including the assessment of noise on a curve and straight track for electric multiple units of the so-called Pendolino, an Alstom type ETR610 series ED25 train. The measurements were made using a microphone camera Bionic S-112 at a distance of 22 m from the track axis. As a result of the conducted experimental research, it was indicated that the noise resulting from vibrations arising at the wheel-rail contact (rolling noise) was the dominant source of sound.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of the Major Noise Energy Sources in Rail Vehicles Moving at a Speed of 200 km/h

Polak

Korzeb

2021

Energies

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“…According to a study by the Plastic Pipe Database Committee (PPDC) of the United States, design and installation errors account for the largest proportion of PVC pipe failure causes, followed by material defects (Figure 2), meaning that inappropriate pipe products with working pressure specifications that do not meet the requirements were selected and installed in the pipe engineering design and construction. In addition, several researchers have found that extreme water pressure caused by water hammers is one of the most common factors affecting PVC pipe bursts [10][11][12][13]. For the ultimate strength analysis, the burst pressure of a pipe is an important parameter in PVC pipeline design and safe operation, which is determined as the internal water pressure resulting in pipe rupture.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Burst Pressure of PVC Pipe Using Average Shear Stress Yield Criterion: Experimental and Numerical Studies

Yang

2021

Applied Sciences

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Polyvinyl chloride (PVC) pipes have been extensively applied in water supply network fields. Understanding the mechanical properties and burst pressure of PVC pipes is necessary because a large number of pipes rupture due to excessive internal water pressure. In this paper, a practical approach based on the average shear stress yield (ASSY) criterion was proposed to assess the PVC pipe burst pressure. In addition, the PVC uniaxial tensile tests and the pipe burst tests were carried out to determine the material characteristic parameters and burst pressure of the PVC pipe. Furthermore, a finite element analysis (FEA) of PVC burst pressure was also performed based on the tangent intersection (TI) method to validate the proposed method and experimental results. Moreover, the impact of material parameters and pipe size, such as the strain hardening exponent and standard dimension ratio (SDR) on bursting pressure, were investigated. The comparison with the proposed theoretical model and the experimental and FEA results shows that the burst pressure derived from ASSY was consistent with the experimental data, with a relative error ranging from −2.76% to 2.65%, which is more accurate compared to other yield criteria. The burst pressure obtained by the ASSY approach declined with the increase of the hardening exponent n and increased with the increase of SDR. Therefore, the burst pressure solution-based ASSY proposed in this paper is an adequately suitable and precise predictive tool for assessing the failure pressure of PVC pipes.

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“…Several kinds of techniques, including acoustic emission (AE) [4], vibration monitoring [5,6] and cavity dynamic pressure [7], have been used for the detection of valve internal leakage. Among these techniques, AE has witnessed a wide application in the detection of valve internal leakage both qualitatively and quantitatively, because it possesses the advantage of nonintrusive, high sensitivity and strong anti-interference ability [8][9][10]. As the accurate prediction of internal leakage Appl.…”

Section: Introductionmentioning

confidence: 99%

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Zhao

Zhu

et al. 2020

Applied Sciences

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Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.

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

Cited by 8 publications

References 25 publications

Identification of the Major Noise Energy Sources in Rail Vehicles Moving at a Speed of 200 km/h

Identification of the Major Noise Energy Sources in Rail Vehicles Moving at a Speed of 200 km/h

Estimation of Burst Pressure of PVC Pipe Using Average Shear Stress Yield Criterion: Experimental and Numerical Studies

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

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