Gas leak locating in steel pipe using wavelet transform and cross-correlation method

Davoodi, Saman; Mostafapour, Amir

doi:10.1007/s00170-013-5367-1

Cited by 33 publications

(13 citation statements)

References 7 publications

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“…The signal passes a half-band low-pass digital filter so the output of filter equals to input convolution and response of filter impact. All frequency components which are higher than half of the biggest available frequency in signal were omitted [3]. The de-noised AE signals then reconstructed and the pure leakage signal with a low level of noises were processed.…”

Section: Resultsmentioning

confidence: 99%

“…As it mentioned, noises usually occur in details so if the number of levels is few, a majority part of noises will not be omitted. In general wavelet and resolution level should be first defined while omitting noises and then the signal will be decomposed [3]. On the other hand, choosing a proper threshold for each level of high frequency coefficient is very critical to remove noises.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…t and q are the Poisson's ratio and density of the wall material respectively. determined by trial-and-error method in our studies [3,17]. We applied Daubechies wavelet of db4.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…They assumed that the acoustic waves propagate along the pipe as a non-dispersive guided wave with a fixed speed. Davoodi et al [3] presented an algorithm based on wavelet transform, filtering and cross-correlation techniques to estimate the time delay for sensors and calculating of leak location. They found good results for leak location with a low error percent in leak locating.…”

Section: Introductionmentioning

confidence: 99%

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Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Mostafapour

Davoodi

2015

Ultrasonics

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Section: Resultsmentioning

confidence: 99%

Section: Wavelet Transformmentioning

confidence: 99%

“…t and q are the Poisson's ratio and density of the wall material respectively. determined by trial-and-error method in our studies [3,17]. We applied Daubechies wavelet of db4.…”

Section: Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Mostafapour

Davoodi

2015

Ultrasonics

Self Cite

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“…Therefore, more advanced signal filtering and decomposition techniques should be explored to improve the localization performance. Davoodi et al proposed a method based on wavelet transform, filtering and cross-correlation techniques to locate a continuous leak source in underground high-pressure gas pipes [11]. Cui et al investigated an empirical mode decomposition (EMD) and cross correlation method to improve the accuracy of localization [12].…”

Section: Introductionmentioning

confidence: 99%

Localization of a continuous CO₂leak from an isotropic flat-surface structure using acoustic emission detection and near-field beamforming techniques

Yan

Guo

Han

2016

Meas. Sci. Technol.

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(2016) Localization of a continuous CO2 leak from an isotropic flat-surface structure using acoustic emission detection and near-field beamforming techniques. Measurement Science and Technology, 27 (11) AbstractSeal capacity is of great importance for the safety operation of pressurized vessels. It is crucial to locate the leak hole timely and accurately for reasons of safety and maintenance. This paper presents the principle and application of a linear acoustic emission sensor array and a near-field beamforming technique to identify the location of a continuous CO2 leak from an isotropic flat-surface structure on a pressurized vessel in the Carbon Capture and Storage system. Acoustic signals generated by the leak hole are collected using a linear high-frequency sensor array. Time-frequency analysis and a narrow-band filtering technique are deployed to extract effective information about the leak. The impacts of various factors on the performance of the localization technique are simulated, compared and discussed, including the number of sensors, distance between the leak hole and sensor array and spacing between adjacent sensors. Experiments were carried out on a laboratory-scale test rig to assess the effectiveness and operability of the proposed method. The results obtained suggest that the proposed method is capable of providing accurate and reliable localization of a continuous CO2 leak.

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Acoustic emission source locating in two-layer plate using wavelet packet decomposition and wavelet-based optimized residual complexity

Mostafapour

Davoodi

2017

Struct Control Health Monit

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SummaryHealth monitoring based on acoustic emission principle needs precise time delay estimation in two-layered plate-type structures. In this paper, the theories of wavelet packet decomposition, wavelet-based optimized residual complexity (WORC), and frequency-varying velocities were used to acoustic emission source locating. A rectangular array of the four sensors was used to locate acoustic emission source. By wavelet packet decomposition, specific packets with frequency range of 0-250 kHz were selected for more signal processing. Then WORC of specific packets of captured signals was calculated as a similarity measure technique. The time delay was estimated when WORC function reached the minimum value.The group velocity was obtained using dispersive curves. The experiments were carried out, and the results of locating error showed the high precision of the proposed algorithm. | INTRODUCTIONPrecise and robust defect source locating is one of the most important issues in structure health monitoring. The acoustic emission (AE) technique is one of the real-time and in-service methods for this purpose. The main advantages of approaches based on AE are their simplicity and high sensitivity. Mostafapour et al. [1] used the theories of wavelet transform and cross-time frequency spectrum to locate AE source with frequency-varying wave velocity in plate-type structures. They used a rectangular array of the four sensors on the plate. Yang et al. [2] proposed an AE analysis method based on multiple signal classification method to calculate the direction of arrival of wave signal in plates. They estimated these directions using the multiple signal classification, and the time delay of the wave was gained using the continuous wavelet transform. Jumaili et al. [3] presented an improved automatic delta T mapping technique using a clustering algorithm to automatically identify and select the highly correlated events at each grid point. They used "minimum difference approach" to determine AE source location. Ding et al. [4] developed a new waveform analysis to estimate AE wave arrival times using wavelet transform. A variety of techniques were compared in the proposed method such as threshold crossing, cross correlation, and wavelet packet transform, and a method based on wavelet decomposition was recommended as the most consistent and accurate method for determining arrival time. Scholey et al. [5] published a method where a map was generated on the plate with arrival time differences for each pair of sensors. The time difference was calculated using sensor distances and wave velocity dependency on the fiber orientation. This technique is named as "best match point search method." Necessity to a large number of training points is an important

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Gas leak locating in steel pipe using wavelet transform and cross-correlation method

Cited by 33 publications

References 7 publications

Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Localization of a continuous CO₂leak from an isotropic flat-surface structure using acoustic emission detection and near-field beamforming techniques

Acoustic emission source locating in two-layer plate using wavelet packet decomposition and wavelet-based optimized residual complexity

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Gas leak locating in steel pipe using wavelet transform and cross-correlation method

Cited by 33 publications

References 7 publications

Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor

Localization of a continuous CO2leak from an isotropic flat-surface structure using acoustic emission detection and near-field beamforming techniques

Acoustic emission source locating in two-layer plate using wavelet packet decomposition and wavelet-based optimized residual complexity

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Localization of a continuous CO₂leak from an isotropic flat-surface structure using acoustic emission detection and near-field beamforming techniques