Evaluation of Acoustic Emission Monitoring of Buried Pipelines

Stulen, F.B.; Kiefner, J.F.

doi:10.1109/ultsym.1982.197964

Cited by 4 publications

(14 citation statements)

References 1 publication

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“…For instance, with ψ0(t) = sin(t) as mother wavelet, at frequency band [1,2] and scale at 1, the child function is: φ0(t) = 2 sin(2t) − sin(t). If the scale parameter is a, the frequency band becomes 1 a , 2 a , and then the general child wavelets will be:…”

Section: Basic Methodology Of Waveletmentioning

confidence: 99%

“…The algorithm of both wavelet and STFT is implemented in Matlab 7.1 platform, as a four-step program, where PDR, LSR and SSR refer to the ratios in Table 1: (1). Extract only the signal part, locating at 0.75s to 0.80s.…”

Section: Methodsmentioning

confidence: 99%

“…Stulen and Muravev showed in their research [1] that observed values for attenuation (in dB/ft) in several gases and water are 4.9 × 10 −11 f 2 and 7.8 × 10 −14 f 2 , respectively, where f is frequency. Therefore, AE signals propagating along steels attenuate very quickly because the frequency is high.…”

Section: Introductionmentioning

confidence: 98%

“…In steel pipelines, when a present defect extends, tension energy is released to produce acoustic signals [1]. Although some of the events occur before monitoring, or are too small to be detected during inspection, various events occur due to temperature, pressure, physical defect development and environmental conditions so that they become detectable.…”

Section: Introductionmentioning

confidence: 99%

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Wavelets application on acoustic emission signal detection in pipeline

Liao

Zhao

et al. 2008

2008 Canadian Conference on Electrical and Computer Engineering

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As a popular non-destructive test, acoustic emission (AE) has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time-frequency distribution. However, classical signal processing methods only give global information on time or frequency domain, while local information are lost. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve a limited precision. Wavelet Transform (WT) is a time-scale-frequency technique with adaptable precision, which does better features extraction and details detection. This paper is an application of wavelet transform in acoustic emission signal detection where strong noise exists. Developing for industrial applications, the techniques presented are both accurate and computationally implemental for embedded systems. In addition, STFT is compared with wavelets to show the advantages of wavelet transforms in this particular application field.

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Section: Basic Methodology Of Waveletmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelets application on acoustic emission signal detection in pipeline

Liao

Zhao

et al. 2008

2008 Canadian Conference on Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…They have been used for health monitoring and detecting of cracks in large pressure vessels, for control of water cooling pipeline systems in nuclear power plants, monitoring corrosion of reinforced concrete, or as failure control of important structural points [64][65][66][67][68][69][70]. Acoustic emission based methods also found their way to biological research with a drought stress experiment on leaves of common European trees [71].…”

Section: Acoustic Emission Introductionmentioning

confidence: 99%

On the Importance of Combined Scratch/Acoustic Emission Test Evaluation: SiC and SiCN Thin Films Case Study

et al. 2018

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Abstract:The scratch test, as probably the most widespread technique for assessment of the adhesive/cohesive properties of a film-substrate system, fully depends on reliable evaluation based on assessment of critical loads for systems' failures. Traditionally used evaluation methods (depth change record and visual observation) may sometimes give misleading conclusions about the failure dynamics, especially in the case of opaque films. Therefore, there is a need for another independent evaluation technique with the potential to complete the existing approaches. The nondestructive method of acoustic emission, which detects the elastic waves emitted during film cracking and delamination, can be regarded as a convenient candidate for such a role even at nano/micro scale. The strength of the combination of microscopic observation of the residual groove and depth change record with the acoustic emission detection system proved to be a robust and reliable approach in analyzing adhesion/cohesion properties of thin films. The dynamics of the gradual damage taking place during the nano/micro scratch test revealed by the combined approach is presented for SiC and SiCN thin films. Comparison of critical load values clearly reflects the higher ability of the AE approach in detecting the initial material failure compared to the visual observation.

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Prediction of PCCP Failure Based on Hydrophne Detecting

Zhang

2013

International Journal on Smart Sensing and Intelligent Systems

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Prestressed Concrete Cylinder Pipe (PCCP) is a widely used water pipe all over the world.A major cause of PCCP failure is the internal wire break, which will emit acoustic signal. In this paper, a hydrophone-based PCCP real-time monitoring and failure-prediction system was proposed. By applying wavelet energy normalization analysis to signal feature extraction and Support VectorMachine (SVM) to signal recognition, a high prediction accuracy of 98.33% was achieved. The result showed that the hydrophone-based PCCP failure prediction system is much more effective and economic in real application compared with electromagnetic method and acoustic fiber optical.

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Evaluation of Acoustic Emission Monitoring of Buried Pipelines

Cited by 4 publications

References 1 publication

Wavelets application on acoustic emission signal detection in pipeline

Wavelets application on acoustic emission signal detection in pipeline

On the Importance of Combined Scratch/Acoustic Emission Test Evaluation: SiC and SiCN Thin Films Case Study

Prediction of PCCP Failure Based on Hydrophne Detecting

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