CN101571233A - Pipeline feature intelligent recognition method based on correlation analysis - Google Patents
Pipeline feature intelligent recognition method based on correlation analysis Download PDFInfo
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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
The present invention relates to a kind of pipeline feature intelligent recognition method, belong to non-destructive inspection signal analysis field based on correlation analysis.
Background technique
The defective of ultrasonic guided wave detecting pipeline and damage are new pipe detection technology of rising in recent years.Compare with leakage field, eddy current, the ray method of routine have the detection efficiency height, propagation length is far away, detection range greatly, does not need to peel off surrounding layer, buried pipeline is not needed whole excavations, can carry out advantages such as online detection, except being applicable to general pipe detection, to overhead pipeline, the pipeline that clad is arranged, buried pipeline, pipeline in highway subgrade through section and through walls section, the situations such as pipeline of operation in water.Compare with traditional ultrasound examination, the supersonic guide-wave technology has detection distance (reaching 200 meters most), can carry out 100% detection to pipeline, does not need coupling and advantage fast easy to detect.
But because the data more complicated that ultrasonic guided wave detecting equipment is gathered from signal, is difficult to directly tell the feature on the pipeline.Need personnel, could discern the pipeline feature information in the signal through special training.Therefore just require a kind of method of intelligence, can be from signal, the rapid and precise characteristic information that extracts pipeline.
At present, mainly concentrate on the aspect of the denoising and the enhancing of signal for the signal processing of ultrasonic guided wave detecting pipeline.Sort research for signal type compares less.At present, delivered or disclosed achievement in research in the achievement of relevant pipeline feature identification still very rare.
Summary of the invention
The objective of the invention is in order to solve in the signal analysis process that supersonic guide-wave equipment collects, personnel are required the present situation of height, processing accuracy difference, the present invention proposes a kind of pipeline feature intelligent recognition method based on correlation analysis, this method can be carried out intelligence classification automatically to signal, extracts pipeline feature information in the signal.
This method is based on ultrasonic guided wave detection signals is carried out correlation analysis.According to the relation of the relative size between amplitude size, appearance similar and symmetry and the asymmetrical signals, testing signal is classified.And, set up the corresponding relation of classification and pipeline feature by test, can fast and accurately ultrasonic guided wave detection signals be classified.
The present invention utilizes the method for correlation analysis to pipeline feature intelligent identification, may further comprise the steps:
1) utilizes supersonic guide-wave pipe detection instrument, record two testing signal curves of detected pipeline, be symmetric signal curve and asymmetrical signals curve, and three apart from amplitude rectification curve (DAC curve), i.e. flange DAC curve, weld seam DAC curve and warning DAC curve.Utilize the pipeline feature information of a certain known location of pipeline, DAC curve adjustment that will be corresponding with the characteristic information of this position so far feature the symmetric signal peak point ± 20% scope in.Owing to the relation of the position between three DAC curves is fixed, so other two DAC curves can be automatically along with variation;
2) utilize the method for differentiate, ask for all maximum on the symmetric signal curve;
3) be separation with determined three DAC curves in the step 1), to step 2) in the maximum signal classify, by these three DAC curves, all maximum can be divided into four classes: the doubtful signal of flange, the doubtful signal of weld seam, flaw indication and noise signal (as Fig. 2), concrete sorting technique is as follows:
When maximum is arranged in less than flange DAC curve 50% to greater than the scope of flange DAC curve 50% time (Fig. 2 a-quadrant), think that the pairing signal of this maximum point is doubtful flange signal;
When maximum is arranged in less than warning DAC curve 50% (Fig. 2 C zone) to greater than the scope of warning DAC curve 50% time, think that the pairing signal of this maximum point is a flaw indication;
When maximum is between doubtful signal of flange and flaw indication (B zone among Fig. 2), think that the pairing signal of this maximum point is doubtful weld seam signal;
When maximum during, think that the pairing signal of this maximum point is a noise signal less than flaw indication.
4) the flange sample signal in doubtful flange signal and the standard sample database is carried out related calculation, calculate the correlation coefficient of each the flange sample signal in doubtful flange signal and the sample storehouse, and the correlation coefficient averaging to being tried to achieve, with this mean as similarity:
If just thinking more than or equal to 70%, similarity doubtful affirmation should classify as the flange characteristic signal by doubtful flange signal; If it negates that doubtful flange signal is referred in the flaw indication that similarity just thinks doubtful less than 70%.
Weld seam sample signal in doubtful weld seam signal and the standard sample database is carried out related calculation, calculate each the weld seam sample signal correlation coefficient in each doubtful weld seam signal and the sample storehouse, and the correlation coefficient of being tried to achieve averaged, with this mean value as similarity:
If just thinking more than or equal to 70%, similarity doubtful affirmation doubtful weld seam signal is classified as the weld seam characteristic signal; If it negates that doubtful weld seam signal is referred in the flaw indication that similarity just thinks doubtful less than 70%.
With these flaw indications that obtain and before the flaw indication that obtains of classification be classified as similarly, like this, signal just can be divided three classes maximum: flange signal, weld seam signal and flaw indication;
5) because the elbow signal is made up of two weld seam signals not far from one another, therefore, the weld seam signal that obtains in the step 4) includes the elbow signal.Just can determine the doubtful signal of weld seam by the distance of judging continuous two weld seam signals.In general, the welding seam distance of elbow signal is no more than 2 meters.Therefore, if two weld seam signals are apart less than two meters, just can be judged to be doubtful elbow signal, all elbow sample signals in doubtful elbow signal and the sample storehouse are carried out related calculation, calculate average correlation coefficient then, 70% being threshold value, when average correlation coefficient classifies as the elbow signal more than or equal to 70% the time, when average correlation coefficient less than 70% the time, classify as the weld seam signal.
6) through above 1)～step 5) just can be divided into all maximum flange, weld seam, elbow and four types of defective.Realized classification to testing signal.
The present invention utilizes correlation analysis that pipeline feature is classified, and its principle is as follows:
Along the hyperacoustic energy of pipe transmmision because self consumption, scattering and reflection, along with the increase of propagation length is the exponentially decay.Therefore, the DAC curve of utilization index decay can provide the reference of signal amplitude on the different distance for the pipe ultrasonic testing signal.And the amplitude of the unlike signal on same distance also is different.The signal maximum of flange, the signal of weld seam approximately are 25% of flange signals, and noise signal is generally all less than 15% of flange signal.Therefore, the DAC curve also divides three: flange DAC curve, weld seam DAC curve and warning DAC curve.If with flange DAC curve is standard, weld seam DAC curve is exactly 25% of a flange DAC curve, and warning DAC curve is exactly 15% of a flange DAC curve.The relative position of these three curves is fixed, and changes a certain curve, and all the other two can change thereupon.When running into pipeline feature, the pulse signal of guided wave detector emission can reflect, with part energy reflected back sensor place.This part energy is received by sensor, will form one " ripple bag " in received signal, corresponds to a peak value on the testing signal curve.Therefore, the characteristic signal of pipeline all is distributed in the maximum place of testing signal curve.Different pipeline features, its profile is also inequality.And correlation coefficient just in time is exactly the appearance similar degree that characterizes two curves.The curve that profile is similar more, correlation coefficient are more near 1.Therefore just can utilize and ask for the correlation coefficient that is classified characteristic signal and sample characteristics signal be used as the classifying foundation of judgement.
Compare with existing manual classification's method, the present invention has following having a few:
1) realized the fully-automatic intelligent of testing signal is classified.Making becomes possibility to a large amount of testing signal analyses;
2) realized the analysis of computer, accelerated the speed of analysis detecting data greatly testing signal;
3) demand that has reduced the check and analysis personnel is registered, and has improved detection efficiency.
Description of drawings
Fig. 1 testing signal intelligent method for classifying algorithm block diagram
The distribution map of Fig. 2 DAC curve and all kinds of doubtful signals
Fig. 3 detects schematic representation
Fig. 4 initial data shows
Fig. 5 maximum shows
The doubtful characteristic point of Fig. 6 shows
Fig. 7 final result shows
Embodiment
Content in conjunction with the inventive method provides following experimental example:
1) sensor is installed on the long pipeline, utilize the supersonic guide-wave Equipment Inspection, collect original detection symmetric signal curve (A curve among Fig. 4), regulate flange DAC curve (B curve among Fig. 4), make it to pass the maximum value of distance at the known flange signal at-7m place, as Fig. 4, so just, determined the position of flange DAC curve, because the position relation between flange DAC curve, weld seam DAC curve and the warning DAC curve is changeless, so determined the position of flange DAC curve, the position of other two curves has just been determined.Curve C is a weld seam DAC curve among the figure, and curve D is warning DAC curve.
2) utilize the method for differentiating, calculate all maximum points on the symmetric signal curve, as shown in Figure 5, the maximum that zero expression calculates among the figure.
3) utilize the DAC curve, maximum is categorized into doubtful flange signal, doubtful weld seam signal and three kinds of situations of flaw indication, as Fig. 6." " represents doubtful flange (A and B among Fig. 6), always have two places all in distance near-the 7m." ◇ " represents doubtful weld seam (C, D, E, F, G among Fig. 6), always has 5 places, in distance be respectively-7m ,-3m ,+3m ,+10m ,+the 16m place." △ " represents defective (comprising noise).
4) calculation procedure 3) described in each doubtful flange signal and the correlation coefficient of master sample flange signal, and all correlation coefficients of being tried to achieve are averaged, determine in accordance with the following methods according to correlation coefficient mean value whether doubtful flange signal is real flange signal, is specially again:
The average correlation coefficient of doubtful flange signal A is 91.8%, greater than 70%.Therefore, doubtful flange signal A is judged as the flange signal.
The average correlation coefficient of doubtful flange signal B is 38.2%, less than 70%.Therefore, doubtful flange B is judged as flaw indication.
5) calculation procedure 3) described in each doubtful weld seam signal and the correlation coefficient of master sample weld seam signal, and all correlation coefficients of trying to achieve are averaged, again according to correlation coefficient mean value according to summary of the invention part steps 4) in method determine whether doubtful weld seam signal is real weld seam signal:
The average correlation coefficient of doubtful weld seam C is 25.2%, less than 70%.Therefore, doubtful weld seam C is judged as flaw indication.
The average correlation coefficient of doubtful weld seam D is 98.1%, greater than 70%.Therefore, doubtful weld seam D is judged as the weld seam signal.
The average correlation coefficient of doubtful weld seam E is 98.2%, greater than 70%.Therefore, doubtful weld seam E is judged as the weld seam signal.
The average correlation coefficient of doubtful weld seam F is 97.9%, greater than 70%.Therefore, doubtful weld seam F is judged as the weld seam signal.
The average correlation coefficient of doubtful weld seam G is 98.3%, greater than 70%.Therefore, doubtful weld seam G is judged as the weld seam signal.
6) judge the distance of adjacent weld seam,, classify the elbow signal as when distance during less than 2 meters.Among weld seam D, the E that obtains in the step 5), F, the G:
DE is two adjacent weld seams, and its distance is 7m, greater than 2m, gets rid of.
EF is two adjacent weld seams, and its distance is 7m, greater than 2m, gets rid of.
FG is two adjacent weld seams, and its distance is 7m, greater than 2m, gets rid of.
Therefore, in this example, there is not the elbow signal.
7) in figure, mark different features with legend, as Fig. 7." " expression flange signal among Fig. 7, " ◇ " expression weld seam signal, " △ " represents flaw indication.
Actual result on the contrast pipeline, in the useful signal distance, weld seam, flange signal are judged entirely accurate, and find place's defective, satisfy the detection demand.
Claims (1)
- Based on the pipeline feature intelligent recognition method of correlation analysis, it is characterized in that 1, this method may further comprise the steps:1) utilizes supersonic guide-wave pipe detection instrument, record two testing signal curves of detected pipeline, be i.e. symmetric signal curve and asymmetrical signals curve, and three apart from the amplitude rectification curve, i.e. flange DAC curve, weld seam DAC curve and warning DAC curve;Utilize the pipeline feature information of a certain known location of pipeline, DAC curve adjustment that will be corresponding with the characteristic information of this position so far feature the symmetric signal peak point ± 20% scope in; Owing to the relation of the position between three DAC curves is fixed, so other two DAC curves can be automatically along with variation;2) ask for all maximum on the symmetric signal curve;3) be separation with determined three DAC curves in the step 1), to step 2) in the maximum signal classify, by these three DAC curves, all maximum is divided into four classes: the doubtful signal of flange, the doubtful signal of weld seam, flaw indication and noise signal, concrete sorting technique is as follows:When maximum is positioned at less than flange DAC curve 50% to greater than the scope of flange DAC curve 50% time, the pairing signal of this maximum point is doubtful flange signal;When maximum is positioned at less than warning DAC curve 50% to greater than the scope of warning DAC curve 50% time, the pairing signal of this maximum point is a flaw indication;When maximum was between doubtful signal of flange and flaw indication, the pairing signal of this maximum point was doubtful weld seam signal;When maximum during less than flaw indication, the pairing signal of this maximum point is a noise signal;4) the flange sample signal in doubtful flange signal and the standard sample database is carried out related calculation, calculate the correlation coefficient of each the flange sample signal in doubtful flange signal and the sample storehouse, and the correlation coefficient of being tried to achieve averaged, with this mean value as similarity:If just thinking greater than 70%, similarity doubtful affirmation should classify as the flange characteristic signal by doubtful flange signal; If it negates that doubtful flange signal is referred in the flaw indication that similarity just thinks doubtful less than 70%;Weld seam sample signal in doubtful weld seam signal and the standard sample database is carried out related calculation, calculate each the weld seam sample signal correlation coefficient in each doubtful weld seam signal and the sample storehouse, and the correlation coefficient of being tried to achieve averaged, with this mean value as similarity:If just thinking greater than 70%, similarity doubtful affirmation doubtful weld seam signal is classified as the weld seam characteristic signal; If it negates that doubtful weld seam signal is referred in the flaw indication that similarity just thinks doubtful less than 70%.With these flaw indications that obtain and before the flaw indication that obtains of classification be classified as similarly, like this, signal just is divided three classes maximum: flange signal, weld seam signal and flaw indication;5) if two weld seam signals at a distance of less than two meters, just judge that these two weld seam signals are doubtful elbow signal, all elbow sample signals in doubtful elbow signal and the sample storehouse are carried out related calculation, calculate average correlation coefficient then, when average correlation coefficient classifies as the elbow signal more than or equal to 70% the time, when average correlation coefficient less than 70% the time, classify as the weld seam signal;6) through above 1)～step 5) just is divided into all maximum flange, weld seam, elbow and four types of defective.
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