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Pipeline feature intelligent recognition method based on correlation analysis

A technology of correlation analysis and intelligent identification, which is applied in the pipeline system, the use of sound waves/ultrasonic waves/infrasonic waves to analyze solids, gas/liquid distribution and storage, etc., can solve the problems of high personnel requirements and poor processing accuracy, and improve detection efficiency. Speed ​​up and reduce the effect of demand registration

Active Publication Date: 2015-03-11
BEIJING UNIV OF TECH +1
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Problems solved by technology

[0005] The purpose of the present invention is to solve the current situation of high requirements on personnel and poor processing accuracy in the signal analysis process collected by the ultrasonic guided wave equipment. The present invention proposes an intelligent identification method for pipeline characteristics based on correlation analysis. This method can Signals are intelligently and automatically classified, and the characteristic information of the pipeline in the signal is extracted

Method used

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  • Pipeline feature intelligent recognition method based on correlation analysis
  • Pipeline feature intelligent recognition method based on correlation analysis
  • Pipeline feature intelligent recognition method based on correlation analysis

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Embodiment Construction

[0035] Provide following test embodiment in conjunction with the content of the inventive method:

[0036] 1) Install the sensor on a long pipeline, use ultrasonic guided wave equipment to detect, and collect the original detection symmetrical signal curve ( Figure 4 Middle A curve), adjust the flange DAC curve ( Figure 4 Middle B curve), so that it passes through the maximum value of the known flange signal at a distance of -7m, such as Figure 4 , so that the position of the flange DAC curve is determined. Since the positional relationship among the flange DAC curve, the weld DAC curve and the warning DAC curve is fixed, the position of the flange DAC curve is determined, and the other two The position of the curve is determined. Curve C in the figure is the weld DAC curve, and curve D is the warning DAC curve.

[0037] 2) Use the method of calculating derivatives to calculate all the maximum points on the symmetrical signal curve, such as Figure 5 As shown, ○ in the ...

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Abstract

The invention relates to a pipeline feature intelligent recognition method based on correlation analysis, belonging to the field of non-destructive detection signal analysis. The method classifies detection signals according to amplitude, shape similarity and relative size relationship between symmetric and asymmetric signals based on the correlation analysis of ultrasonic guided wave detection signals. The corresponding relationship between the categories and the pipeline features is established through tests, thereby being capable of rapidly and accurately classifying the ultrasonic guided wave detection signals.

Description

technical field [0001] The invention relates to an intelligent identification method of pipeline characteristics based on correlation analysis, which belongs to the field of nondestructive detection signal analysis. Background technique [0002] Ultrasonic guided wave detection of defects and damages in pipelines is a new pipeline detection technology that has emerged in recent years. Compared with conventional magnetic flux leakage, eddy current, and ray methods, it has the advantages of high detection efficiency, long propagation distance, large detection range, no need to peel off the outer cladding, no need to fully excavate buried pipelines, and online detection. General pipeline detection, for elevated pipelines, clad pipelines, buried pipelines, pipelines passing through road subgrade sections and wall sections, pipelines running in water, etc. Compared with traditional ultrasonic testing, ultrasonic guided wave technology has the advantages of long detection distanc...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F17D5/06G01N29/11
Inventor 吴斌符浩王维斌佟文强郑阳赵彩萍宋国荣何存富
Owner BEIJING UNIV OF TECH
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