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Automatic identification method for supersonic phased array for detecting oil gas pipeline girth weld defect type

An ultrasonic phased array and defect type technology, which is applied in the processing of detection response signals, special data processing applications, instruments, etc., can solve problems such as slow speed, inability to accurately reflect the essential characteristics of defect signals, and mathematics not satisfying Fourier analysis.

Inactive Publication Date: 2008-02-13
TIANJIN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the feature extraction speed is slow
The ultrasonic defect signal is an unsteady time-varying signal, which does not meet the conditions of Fourier analysis in mathematics
Therefore, the above methods cannot accurately reflect the essential characteristics of defect signals

Method used

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  • Automatic identification method for supersonic phased array for detecting oil gas pipeline girth weld defect type
  • Automatic identification method for supersonic phased array for detecting oil gas pipeline girth weld defect type
  • Automatic identification method for supersonic phased array for detecting oil gas pipeline girth weld defect type

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Experimental program
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Effect test

Embodiment 2

[0115] The artificial defect signal image collected by the ultrasonic phased array is shown in Figure 15. According to step 2 in step (1), after the 3-layer decomposition of the db4 lifting wavelet is performed, the 3-layer low-frequency signal s is obtained 1 ~s 3 and 3 layers of high frequency signal d 1 ~ d 3 , as shown in Figure 16. For the low-frequency signal and the high-frequency signal, the preferred features are extracted according to the calculation method of the second small step and the third small step in step 3 in step (1): wherein, the extracted energy feature is shown in Figure 17, and the extracted fractal feature The log-log graph of the scale-free interval is shown in Figure 18. The scatter points in Figure 18 are fitted to a straight line by the least square method, and the absolute value of the slope is the fractal feature of the defect signal. The energy feature and fractal feature extracted above are used as the input of the support vector machine f...

Embodiment 3

[0117] The artificial defect signal image collected by the ultrasonic phased array is shown in Figure 20. According to step 2 in step (1), after the 3-layer decomposition of the db4 lifting wavelet is performed, the 3-layer low-frequency signal s is obtained 1 ~s 3 and 3 layers of high frequency signal d 1 ~ d 3 , as shown in Figure 21. For the low-frequency signal and the high-frequency signal, the preferred features are extracted according to the calculation method of the second small step and the third small step in step 3 in step (1): wherein, the extracted energy feature is shown in Figure 22, and the extracted fractal feature The log-log graph of the scale-free interval is shown in Figure 23. The scatter points in Figure 23 are fitted to a straight line by the least square method, and the absolute value of the slope is the fractal feature of the defect signal. The energy feature and fractal feature extracted above are used as the input of the support vector machine f...

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Abstract

The invention discloses an ultrasonic phased array inspection oil-gas pipe circular weld defect automatic recognition method, in particular to an ultrasonic phased array inspection oil-gas pipe circular weld defect automatic recognition method for a support vector machine, which belongs to a pipe circular weld defect type recognition technology. The recognition method includes two parts of the construction of a support vector machine model and the automatic recognition of unknown defect types in circular weld. The construction steps of the support vector machine model include artificial defect signals extraction, wavelet decomposition promotion, feature extraction, feature optimization and the training of the support vector machine model. The automatic recognition steps of unknown defect types in circular weld include defect signals extraction, optimized feature calculation and automatic recognition based on the support vector machine. The invention combines a lifting wavelet transform with a fractal technology and has the advantages of high recognition accuracy and fast recognition speed. The automatic recognition of the defect types can be realized.

Description

technical field [0001] The present invention relates to an automatic recognition method for defect types of oil and gas pipeline girth welds detected by ultrasonic phased array, in particular to an automatic recognition method for defect types of oil and gas pipeline girth welds detected by ultrasonic phased array using support vector machine, which belongs to pipeline girth welding Identification technology of seam defect type. Background technique [0002] Various defects may be formed in the welding process of large-diameter long-distance pipelines, which will bring safety hazards to the transportation of oil and gas resources. At present, after the pipeline welding is completed, the ultrasonic phased array flaw detector is generally used to quickly locate and quantitatively analyze the defects inside the girth weld. Chinese Invention Patent Application Nos. 03115222.8 and 01131937.2 elaborated on the system structure of phased array ultrasonic non-destructive testing an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N29/44G06F17/00
Inventor 李健詹湘琳曾周末靳世久
Owner TIANJIN UNIV
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