Simulation of Gas Pipeline Leak Detection Based on Acoustic Emission and Wavelet Packet Analysis

Dong Pan1，Ning Zhou2, Xuanya Liu3 and Huijun Zhao4

1 Master; Jiangsu Key Laboratory of Oil-Gas Storage and Transportation Technology, Changzhou University, Changzhou, China; 2 Associate Professor; Jiangsu Key Laboratory of Oil-Gas Storage and Transportation Technology, Changzhou University, Changzhou, China; 3 Research Associate; Tianjin Fire Research Institute, Tianjin, China; 4 Professor; Jiangsu Key Laboratory of Oil-Gas Storage and Transportation Technology, Changzhou University, Changzhou, China; ABSTRACT In this article acoustic emission sensors were used to detect simulation of gas pipeline leak and the acoustic emission signals of a pipeline leak were obtained. Acoustic emission technology was adopted to detect pipeline leak. First wavelet packet decomposition methods were used to decompose the acoustic emission signals; then the attenuated signals were remedied effectively at different frequencies. The cross-correlation technology was used to calculate the delay-time received by the two acoustic emission sensors. This method effectively solved the difficulty that the existing acoustic emission system was difficult to detect continuous signals, such as pipeline leakage giving a larger leakage location error. The proposed method pipeline leakage location error is less than 8%. KEYWORDS

Acoustic Emission Testing; Emission Testing; Location; Wavelet Packet Analysis; Cross-correlation Analysis INTRODUCTION Accidents in a pressure pipeline, such as oil pipeline , gas pipeline, chemical pipeline etc., can be caused by the tiny fatigue cracks, but also by pipeline corrosion (decrease of the wall section) and by inner cracks. These cracks and damage limit pipeline operational parameters, lead to the pipeline leakage and blowout, and cause damage to the equipment, explosion, personal injury, etc. Nowadays the main methods which were widely used for the leak detection of pipelines, included the pressure gradient method, the negative pressure wave method, the flow balance method, the ultrasonic detection method, etc. (Furness,1987). All of these methods could not meet the requirements of the timeliness, the accuracy and the economy of the pipeline leak detection, not to mention the accurate positioning of the leak source (Ma & Yang,

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1999 and Wang & Li,2000). As a new nondestructive testing method, acoustic emission testing method had no damage to the pipeline equipment, high sensitivity, fast detection speed, low personnel requirements, etc. (Zhang & Shang,2007). Therefore, based on the acoustic emission technology and the improved decomposition technique of wavelet packet, this article put forward a monitoring localization method, which could be used to realize the positioning of the leak source more accurately, instating of establishing complex mathematical moels. ACOUSTIC EMISSION TESTING SYSTEM OF PIPELINE LEAK The acoustic emission signal detection system was mainly comprised of a digital signal processing card of acoustic emission, sensors of acoustic emission, preamplifier and filters, etc. The experimental pipeline system on which it was tested, contained simulation of a gas pipeline and simulation of an oil pipeline, and was made up of pump, valve, pipeline and air compressor, as shown in figure 1. The gas pipeline was composed of 20# ordinary carbon steel tube, with its external diameter 32mm, its wall thickness 3mm, its total length 55.36m, and its surface was covered by paint layer. There were four leak holes in the pipeline, and the hole diameter could be adjusted, with the leak flow measured by the rotor flowmeter and pipeline pressure measuring by the pressure sensors and the pressure gauges jointly. The leak signals were detected and recorded by the acoustic emission testing system.

Figure 1. Pipeline testing system

WAVELET PACKET DECOMPOSITION AND RECONSTRUCTION ALGORITHM Multi-resolution analysis could be used to do some effective time-frequency analysis, but its scale was changed by binary, such as jtj 2∝Δ , jtj −∝Δ 2 . When the scale j was small, the frequency resolution was poor, while j was big, the time resolution was poor, therefore, it was not suitable for the drastic changes of high frequency signals. Wavelet packet analysis was a more delicate analysis method on the basis of multi-resolution analysis, with the frequency band being multi-layer divided and the high frequency part being decomposed further which was not subdivided by

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multi-resolution analysis. It could be used to select the corresponding band to match the signal spectrum, so as to improve the time-frequency resolution, according to the characteristic of the analyzed signal.

( ){ } zntun ∈ was the wavelet packet about kh , set ( ) nj

nj Utg ∈ , then ( )tg n

j could be expressed as

( ) ( )ltudtg i

ln

njl

nj −=∑ 2,

In the formula, njld , was the projection coefficient of ( )tg n

j in the space.

It could be known from 1221

++ ⊕= n

jn

jnj UUU that wavelet packet decomposition

was namely to divide ( )tg nj 1+ into ( )tg n

j2 and ( )tg n

j12 + , thus obtained the algorithm

[ ]∑ +−−

+ +=k

njkkl

njkkl

njl dgdhd 12,

22,

2,1 .

∑ +−=

k

njklk

njl dad ,1

22,

∑ +−

+ =k

njklk

njl dbd ,1

212,

In the formula, ( )khak 021

= , ( )khbk 121

= , h was dual operator of h .

Set the signal was divided into j layers, the frequency domain part of the j th

layer was to divide max~0 f into max21~0 fj , maxmax 2

2~21 ff jj , ... ,

maxmax

1

~2

12 ffj

− . Such as the decomposition of the third layer, its decomposition

tree as shown in figure 2. A stands for low frequency, D stands for high frequency, and the serial number in the end stands for the layers of wavelet packet decomposition, with the relation S=AAA3+ DAA3+ ADA3+DDA3+ AAD3+ DAD3+ ADD3+ DDD3.

Figure 2. Tree structure of wavelet packet decomposition PIPELINE LEAK POSITIONING AND ANALYSIS Realization of acoustic emission signal positioning method. When wavelet pocket

S

D1A1

AA2

AAA3 DAA3

DA2

ADA3 DDA3

DD2

ADD3 DDD3

AD2

AAD3 DAD3

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analysis was used to make sure the position of acoustic emission source, first the noise signal should be eliminated to reduce the data volume and the subsequent analysis time, and then a wavelet was selected to analyze the acquired signal, as shown in figure 3. The acquired wavelet was supposed to be a very good one for frequency domain localization to achieve good points of signal frequency. The orthogonal wavelet with certain approximate symmetry (such as Symlets8 wavelet, Coiflets3 wavelet) and the symmetrical biorthogonal wavelet (such as b-spline wavelet) were chose in a general way, and Coiflets3 wavelet was chose in this paper (Hu et al., 1999 and Li et al., 2003).

Figure 3. Realization process of wavelet packet analysis positioning method

Positioning and analysis of pipeline leak source. The propagation speed of acoustic emission signal in the pipeline was 4167.7 m/s by broken lead experiment. The test results of leak in different pressure condition showed that the acoustic emission testing system was able to detect the pipeline leak phenomenon with various pipeline pressure conditions, as the acoustic emission signal of typical pipeline leak shown in figure 4.

Read the signal data files

Choose wavelet base and reconstruct layers according to the sampling frequency and

fundamental frequency

Decompose the signal by wavelet

Compensate the signal within different frequencies effectively according to signal

attenuation features in materials

Reconstruct the decomposition signal on the given layer by wavelet

Calculate the error of reconstruction signal by negative correlation analysis

Make sure the position according to the error localization method

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Figure 4. Acoustic emission signal graph of typical pipeline leak

In this paper wavelet packet analysis method was adopted to realize the pipeline leak positioning. The data file collected by the acoustic emission sensor 1 and 2 was lead into MATLAB, and the signal x1 and x2 were re-generated as shown in figure 5.

Figure 5. Original signal x1 and x2

Figure 6. Wavelet decomposition graph of signal x1

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Then coiflets3 wavelet was used to decompose the original signal x1, and its size scale choose 2, namely using db2 wavelet to do a 5-layers decomposition with x1 and x2 as shown in figure 6 and figure 7.

Figure 7. Wavelet decomposition graph of signal x2

Then the default threshold method was used to reconstruct the decomposed signal x1 and x2, and the reconstructed signal xx1 and xx2 were obtained as shown in figure 8. Cross-correlation analysis was used to do with the reconstructed signal xx1 and xx2, and the correlation coefficient graph was obtained as shown in figure 9.

Figure 8. Reconstructed signal xx1 and xx2

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Figure 9. Correlation coefficient graph of signal xx1 and xx2

Based on the linear correlation algorithm analysis method, do algorithm analysis to the correlation coefficient of signal xx1 and xx2, and get the correlation coefficient algorithm graph as shown in figure10.

Figure 10. Correlation coefficient algorithm graph of signal xx1 and xx2

It could be seen form the algorithm graph that the maximum offset of correlation coefficient corresponding the graph point was 25×10-6, and its offset time was 25×10-6s, so the leak position was 1000+(1000-4167675×25×10-6)/2=1343.7 mm, while its absolute error was 43.7 mm, and its relative error was (43.7/1300)×100%=3.4%. The wavelet processing result of acoustic emission signal was obtained in the simulation of gas pipeline leak under different pressure condition as shown in table 1.

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Table 1. Positioning Comparison Serial

number Pressure

/MPa Actual

position /mmWavelet analysis positioning /mm

Wavelet analysis relative error/mm

1 0.79 1300 1393.7 7.21% 2 0.62 1300 1383.1 6.39% 3 0.58 1300 1343.7 3.4% 4 0.53 1300 1353.3 4.1% 5 0.41 1300 1349.9 3.84% 6 0.37 1300 1368.7 5.28% 7 0.35 1300 1398.5 7.58% 8 0.32 1300 1306.2 0.5% 9 0.31 1300 1368.7 5.3% 10 0.19 1300 1387.5 6.73%

From the processing of acoustic emission signal with wavelet packet analysis, it could be seen that the signal after decomposition, noise reduction and reconstruction was more effective. It could realize the positioning of pipeline leak source by wavelet packet analysis and cross-correlation analysis. The positioning error was less than 8%, which could satisfy the needs of the project. CONCLUSION Based on matlab programming, wavelet packet analysis and cross-correlation analysis was used on the acoustic emission signal of the pipeline leak to get the following conclusions: (1) Use wavelet packet theory to do multi-resolution analysis to the sampling data which was collected by the acoustic emission sensor, then do cross-correlation analysis to the decomposed signals of each frequency band, so as to improve the noise ratio and the anti-interference ability. (2) Use the wavelet packet transform and correlation analysis combining method to confirm the mistiming of acoustic emission signal transmission to realize the accurate positioning of the leak source. ACKNOWLEDGEMENTS The authors thank the Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (2011BAK03B00) and Ministry of Public Security Fire Science and Technology Innovation Project (2009XFCX043) to support this work. REFERENCES

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