CN104034796B - Detection generating date device and method in a kind of pipe leakage - Google Patents

Abstract

In a kind of pipe leakage, detection generating date device and method, belongs to pipeline inspection technology field, and this device is arranged on in-pipeline detector, including: leakage field sensor unit, Signal-regulated kinase, A/D modular converter and CPU；Leakage field sensor unit includes that multiple leakage field sensor, the plurality of leakage field sensor are evenly arranged on in-pipeline detector along pipeline section circumferencial direction；CPU includes time-sequence control mode, defective data discrimination module, defective data characteristic extracting module and data memory module；Abnormal data in defects detection can quickly be identified and extract with data characteristics by the method for the present invention, and only feature and relevant information to abnormal data carries out record, major defect position can be analyzed in 30 minutes after internal detector detects, preferably prevent major defect to leak, cause catastrophic consequence.

Description

Detection generating date device and method in a kind of pipe leakage

Technical field

The invention belongs to pipeline inspection technology field, be specifically related to detection generating date device in a kind of pipe leakage And method.

Background technology

Pipeline transportation is a kind of particularly important means of transportation, for the pipeline that active time is longer, passes through leakage field Position shape and the degree of danger of its existing defects are analyzed being extremely necessary by the method for detection.Defect shape and danger The data characteristics differentiating Main Basis fault location detection data of degree.Therefore, the feature extraction of abnormality detection data is whole Most important link during Magnetic Flux Leakage Inspecting.

The interior detection of defect of pipeline mainly uses in-pipeline detector to carry out Magnetic Flux Leakage Inspecting, and detection data is stored in In the storage device of in-pipeline detector, after detection terminates, then carry out data process.The real-time of data process and intelligence Property is poor.Meanwhile, this kind of data storage processing mode is used will to need to have the storage device of substantial amounts of memory space.And need Bigger power consumption.Due to, oil pipeline especially pipeline under the ocean, the spacing of universal node is longer.Inspection in pipeline The electric energy surveying device is the most nervous.Therefore, such data storage processing mode is not very perfect.Currently for other The research of data storage method is the most relatively fewer.

Summary of the invention

The deficiency existed for prior art, the present invention provide in a kind of pipe leakage detection generating date device and Method.

Technical scheme:

In a kind of pipe leakage, detection generating date device, is arranged on in-pipeline detector, including: leakage field senses Device unit, Signal-regulated kinase, A/D modular converter and CPU；

Described leakage field sensor unit includes multiple leakage field sensor, is evenly arranged in pipeline along pipeline section circumferencial direction On internal detector, each leakage field sensor in described leakage field sensor unit is used for detecting pipeline magnetic flux leakage signal and exporting telecommunications Number to Signal-regulated kinase；

Described Signal-regulated kinase is for being filtered and processing and amplifying the signal of telecommunication received, and will filter and amplify The signal of telecommunication after process delivers to A/D modular converter；

Described A/D modular converter for carrying out analog digital conversion and will change to the signal of telecommunication received from Signal-regulated kinase After digital electric signal be sent to CPU；

Described CPU, including time-sequence control mode, defective data discrimination module, defective data feature extraction mould Block and data memory module；

Described time-sequence control mode is for controlling the change over order of each path of A/D modular converter；

Described defective data discrimination module is used for receiving the digital signal that A/D modular converter transmits, and to exception therein The effectiveness of data and abnormal data differentiates；Determine the sequence in the real time data received of each effective anomaly data Number；The effective anomaly data determined are sent to defective data characteristic extracting module, by sequence corresponding for effective anomaly data Number sends to data memory module；

Described defective data characteristic extracting module for extracting feature to the effective anomaly data received, and will extract The feature of effective anomaly data sends to data memory module；

Described data memory module is for storing the effective anomaly data characteristics received and sequence number thereof and export.

In in pipe leakage described in employing, detection generating date device carries out pipe leakage, detection data are located in real time The method of reason, comprises the following steps:

Step 1: obtain the interior detection history magnetic flux leakage data of real-time pipeline detection magnetic flux leakage data and normal pipeline section；

In units of duct section between two girth joints, the magnetic leakage signal of the detection normal pipeline section of multistage, and set up normal The interior detection history magnetic flux leakage data record of pipeline section；Normal pipeline section refers to the pipeline section not having defect to occur；

Step 2: according to the interior detection history magnetic flux leakage data of normal pipeline section, determine abnormal data threshold value；

From the interior detection history magnetic flux leakage data of the normal pipeline section of multistage, determine the maximum of each normal pipeline section magnetic flux leakage data respectively Value, tries to achieve the meansigma methods of multistage normal pipeline section magnetic flux leakage data maximumMeanwhile, wherein one section of normal pipeline section leakage field is tried to achieve The meansigma methods of data；Detect one section of defect pipeline section, and try to achieve the meansigma methods of this defect pipeline section magnetic flux leakage data；Ask for described defect pipe The meansigma methods of section magnetic flux leakage data and the ratio of the meansigma methods of described one section of normal pipeline section magnetic flux leakage data；That is,

$\stackrel{\‾}{X_{\max }}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{X}_{\max },\stackrel{\‾}{X}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}{X}_i,\stackrel{\‾}{X_1}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}{X}_{\mathrm{li}},C=\frac{\stackrel{\‾}{X_1}}{\stackrel{\‾}{X}}$

In formula: n is normal pipe hop count amount；X_maxMaximum for every section of normal pipeline section magnetic flux leakage data；For multistage just The often meansigma methods of pipeline section magnetic flux leakage data maximum；It it is one section of normal pipeline section magnetic flux leakage data meansigma methods；X_iIt it is one section of normal pipeline section Magnetic flux leakage data point；N₁It is one section of normal pipeline section magnetic flux leakage data point quantity；It is that one section of existing defects pipeline section magnetic flux leakage data is average Value；X_1iIt it is one section of existing defects pipeline section magnetic flux leakage data point；N₂It is one section of existing defects pipeline section magnetic flux leakage data point quantity；C is one section The ratio of the meansigma methods of defect pipeline section magnetic flux leakage data and the meansigma methods of one section of normal pipeline section magnetic flux leakage data；

Then abnormal data threshold value is

Step 3: according to abnormal data threshold value, isolate the abnormal data in real-time pipeline detection magnetic flux leakage data, and right Abnormal data and axial location thereof carry out record；Axially refer to pipe lengths；

The method separating described abnormal data is: regard more than the real-time pipeline detection magnetic flux leakage data of abnormal data threshold k For abnormal data；

Step 4: utilize correlation analysis, is adjacent the comparison of data, it determines abnormal data by abnormal data Effectiveness；Adjacent data refers to the sensor adjacent with sensor abnormal data being detected and detects in same axial position Data；

Use the method calculating the covariance that abnormal data is adjacent data, weigh abnormal data and be adjacent data Between dependency；Because close together between two adjacent groups sensor, so, two adjacent groups signal has certain similarity, If two groups of data have reached certain positive correlation degree, then it is assumed that data acquisition is correct, and isolated abnormal data is effective Data；Otherwise it is assumed that data acquisition is wrong, isolated abnormal data is invalid data；

Step 5: utilize cubic spline interpolation, the effective abnormal data recorded each sensor in real time respectively carries out three Secondary spline interpolation, respectively obtains the cubic spline interpolation matched curve of the effective anomaly data that each sensor records in real time, is called for short Each bar curve；

The vertical coordinate of described each bar curve is abnormal data value, abscissa be abnormal data axially detect position；

Step 6: respectively according to each bar curve, ask for the eigenvalue of the effective anomaly data that each sensor records in real time；

Step 6.1: ask for the axial eigenvalue of the effective anomaly data that each sensor records in real time；

Step 6.1.1: determine peak value and the valley of each bar curve, and calculate the peak-to-valley value of each bar curve；Peak-to-valley value refers to It it is the difference of peak value and valley；The size of peak-to-valley value is relevant with the degree of depth of defect of pipeline；That is, determine that what each sensor recorded in real time has The effect maximum of abnormal data, minima and the difference of maxima and minima；

Step 6.1.2: determine abscissa value and the abscissa value of time low valley point of the minimum valley point of each bar curve, and calculate The paddy valley of each bar curve；Paddy valley refers to the abscissa value of minimum valley point and the difference of the abscissa value of time low valley point；I.e., really The axial location of the minima of the effective anomaly data that fixed each sensor records in real time and the axial location of secondary minima, and described The difference of the axial location of the axial location of minima and time minima；The size of paddy valley is relevant with the length of defect etching；

Step 6.1.3: calculate the area of each bar curve respectively；

The computing formula of each bar area under the curve is:

$S=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\{x_i\left(t\right)-\min [x_i\left(t\right)\left]\right\}$

In formula, S is area under the curve, x_iT () is exceptional data point, min [x_i(t)] it is minimum abnormal data；

Step 6.1.4: calculate each bar curve energy；

The computing formula of each bar curve energy is:Wherein, S_eFor curve energy；

Step 6.1.5: the method utilizing wavelet transformation, asks for the flex point spacing on each bar curve；

Method: due to the required corner position taken respectively between crest and two troughs, therefore only respectively to each bar Part between minimum valley point and two troughs of secondary low valley point of curve carries out wavelet transformation, and the wavelet basis chosen is smooth function Second dervative；The zero point of the curve after continuous wavelet transform, is the flex point of virgin curve, and it is little that the present invention chooses sombrero Ripple, as mother wavelet, carries out continuous wavelet transform to abnormal data in the range of yardstick a=4 to a=32.According to concrete abnormal The transform effect of signal chooses wavelet scale, and during general yardstick a=8, effect is best, tries to achieve the position of each bar knee of curve；

Step 6.1.6: ask for peak-to-valley value and the ratio of paddy valley, area and the ratio of peak-to-valley value, the area of each bar curve Ratio with paddy valley；

Step 6.2: ask for the circumferential eigenvalue of the effective anomaly data that each sensor records in real time；Circumference refers to pipeline Cross-sectional periphery direction；

Step 6.2.1: determine circumference abnormal data threshold value；

Method flow is: the peak value of the effective anomaly data recorded with current sensor is as basic point, corresponding at this peak value Near axial location, first the peak value of the effective anomaly data that the sensor of on the left of current sensor or right side next-door neighbour records is entered Row judges, if the peak value of its effective anomaly data recorded more than or equal to current sensor, then using this peak value as peak-peak, After the same method, the peak value continuing on the effective anomaly data that follow-up adjacent sensors is recorded by the direction successively is carried out Judge, until the effective anomaly data that record less than previous sensor of the peak value of effective anomaly data that a rear sensor records Till peak value, obtain final peak-peak, and the sensor corresponding to this peak-peak and this peak-peak is carried out record； If the peak value of its effective anomaly data recorded less than current sensor, the then sensing on the right side of current sensor or left side next-door neighbour The peak value of the effective anomaly data that device records judges, if its effective anomaly data recorded more than or equal to current sensor Peak value, then using this peak value as peak-peak, after the same method, continue on the direction successively to follow-up adjacent sensors The peak value of the effective anomaly data recorded judges, until the peak value of effective anomaly data that a rear sensor records is less than front Till the peak value of the effective anomaly data that one sensor records, obtain final peak-peak, and to this peak-peak and this Big sensor corresponding to peak value carries out record；If the effective anomaly data that the adjacent sensors of left and right both direction records The peak value of the effective anomaly data that peak value respectively less than current sensor records, then effective anomaly data current sensor recorded Peak value as final peak-peak, and this peak-peak and current sensor are carried out record；By final peak-peak Half value as circumference abnormal data threshold value；

Step 6.2.2: determine circumference abnormal data；

Method is: the effective anomaly that the sensor that the left and right both direction of sensor belonging to described peak-peak is adjacent records The peak value of data is considered as lacking at pipeline section more than the sensor being considered as circumference abnormal data and correspondence thereof of circumference abnormal data threshold value Fall in coverage, obtain circumference abnormal data and the number of sensors of pipeline section defect covering；

And the axial location at the peak value of abnormal data that records of the sensor covering pipeline section defect carries out record, based on Sensing station difference and detection error, the axial location residing for each peak value has difference, utilizes method of least square to determine relatively The position that error is minimum, as the axial location of this group circumference abnormal data peak value；

Step 6.2.3: utilize cubic spline interpolation, carries out cubic spline interpolation to circumference abnormal data respectively, obtains The cubic spline interpolation matched curve of circumference abnormal data, is called for short circumference curve；

Step 6.2.4: three samples of the effective anomaly data that a plurality of sensor that run of designing defect covers records in real time The area of bar interpolation fitting curve and；

Step 6.2.5: take the method identical with step 6.1.1 to step 6.1.6, repeated execution of steps 6.1.1 is to step Rapid 6.1.6, obtains the feature of circumference curve, including: peak-to-valley value, paddy valley, area, energy, flex point spacing, peak-to-valley value and Gu Gu The ratio of the ratio of value, area and the ratio of peak-to-valley value, area and paddy valley；

Step 7: the eigenvalue of the effective anomaly data of output step 6 gained；

Beneficial effect:

Abnormal data in defects detection can quickly be identified and extract with data characteristics by the method utilizing the present invention, And only feature and relevant information to abnormal data carries out record, total data is carried out by the data volume of storage relative to traditional The method of record is greatly decreased.Traditional data recording mode owing to have recorded the data volume of super large, it is generally required to several days even The time of tens days just can obtain complete data analysis report.And adopting said method can internal detector detect after three Analyze major defect position in ten minutes, thus preferably prevent these major defects to leak, cause catastrophic Consequence.

Accompanying drawing explanation

Fig. 1 is the structural representation of the pipe leakage interior detection generating date device of one embodiment of the present invention；

Fig. 2 is the circuit theory diagrams of the Signal-regulated kinase of one embodiment of the present invention；

Fig. 3 is the ADS7844 analog-digital converter interface electricity with EP4CE15F17C8 FPGA of one embodiment of the present invention Lu Tu；

Fig. 4 is the EP4CE15F17C8 FPGA workflow diagram of one embodiment of the present invention；

Fig. 5 is the defective data discrimination module workflow diagram of one embodiment of the present invention；

Fig. 6 is the defective data characteristic extracting module workflow diagram of one embodiment of the present invention；

Fig. 7 (a) is the curve peak valley schematic diagram of one embodiment of the present invention；B () is one embodiment of the present invention Curve paddy valley schematic diagram；C () is the area under the curve schematic diagram of one embodiment of the present invention；D () is that one of the present invention is real Execute the knee of curve spacing schematic diagram of mode；

Fig. 8 is the knee of curve spacing schematic diagram after wavelet transformation of one embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings one embodiment of the present invention is elaborated.

In pipe leakage in present embodiment, detection generating date device, is arranged on in-pipeline detector, as Shown in Fig. 1, including: leakage field sensor unit, Signal-regulated kinase, A/D modular converter and CPU；

Leakage field sensor unit in present embodiment includes 96 leakage field sensors, uniform along pipeline section circumferencial direction Being arranged on in-pipeline detector, each leakage field sensor in described leakage field sensor unit is used to detect pipe leakage letter Number and export the signal of telecommunication to Signal-regulated kinase；

The Signal-regulated kinase of present embodiment is for being filtered and processing and amplifying the signal of telecommunication received, and will filter The signal of telecommunication after ripple and processing and amplifying delivers to A/D modular converter；The Signal-regulated kinase of present embodiment, as in figure 2 it is shown, from suddenly The signal that your sensor receives first passes around filter circuit filtering, and being then connected to model through the resistance R2 that resistance is 10K is The inverting input 7 of the operational amplifier of AD824, in-phase input end 8 connects the reference voltage of 2.5V, AD824 operational amplifier One end of resistance R1 and the electric capacity C2 of 0.01pF that outfan 6 connects one end of resistance R3 that resistance is 20K, resistance is 20K One end, the other end of resistance R3 is as the input of the outfan connection A/D conversion chip of Signal-regulated kinase, and resistance R1's is another The inverting input of one end concatenation operation amplifier, the other end ground connection of electric capacity C2, the inverting input of AD824 operational amplifier One end of the 7 electric capacity C1 being also connected with 100pF, the other end ground connection of electric capacity C1.

A/D modular converter in present embodiment is used under the control of the time-sequence control mode in CPU, The pulse electrical signal received from Signal-regulated kinase is carried out analog digital conversion and the digital signal after conversion is sent to central authorities Processing unit；A/D modular converter in present embodiment uses the analog-digital converter that model is ADS7844.

CPU in present embodiment uses the FPGA that model is EP4CE15F17C8, including sequential control Molding block, defective data discrimination module, defective data characteristic extracting module and data memory module；In present embodiment time Sequence control module is for controlling the change over order of each path of A/D modular converter；Data memory module in present embodiment is used In the abnormal data feature received and sequence number thereof are stored.

The interface circuit of EP4CE15F17C8FPGA Yu ADS7844 analog-digital converter in present embodiment, as it is shown on figure 3, Voltage signal is converted to digital signal by ADS7844 analog-digital converter, 5 different outfans of ADS7844 analog-digital converter Connect the self-defined I/O mouth of FPGA time-sequence control mode respectively, i.e. the CS end of ADS7844 analog-digital converter connect I/O.71 end, The BUSY end of ADS7844 analog-digital converter connects the I/ of the DCLK end connection FPGA of I/O.72 end, ADS7844 analog-digital converter O.73 end, the DIN end connection I/O.74 end of ADS7844 analog-digital converter, the DOUT end of ADS7844 analog-digital converter connect I/ O.75 hold.

The workflow of EP4CE15F17C8 FPGA in present embodiment, as shown in Figure 4, starts from step 401.

In step 402, present embodiment, the digital signal feeding defective data after the conversion of ADS7844 analog-digital converter is sentenced Other module, abnormal data and the effectiveness of abnormal data in the defective data discrimination module real time data to receiving are sentenced Not, effective abnormal data is i.e. defect of pipeline data；Determine each effective anomaly data row in the real time data received Ordinal number；The effective anomaly data determined are sent to defective data characteristic extracting module, by row corresponding for effective anomaly data Ordinal number sends to data memory module；

The defective data characteristic extracting module in step 403, the present embodiment effective anomaly data to receiving are extracted Feature, and the eigenvalue of the effective anomaly data extracted is sent to data memory module；

Data memory module in step 404, present embodiment exports the row that its effective anomaly data stored are corresponding The eigenvalue of ordinal sum effective anomaly data；

The workflow of defective data discrimination module, as it is shown in figure 5, start from step 501.

In step 502, obtain the interior detection history magnetic flux leakage data of real-time pipeline detection magnetic flux leakage data and normal pipeline section；

In units of duct section between two girth joints, the magnetic leakage signal of the detection normal pipeline section of multistage, and set up normal The interior detection history magnetic flux leakage data record of pipeline section；Normal pipeline section refers to the pipeline section not having defect to occur；

In step 503, according to the interior detection history magnetic flux leakage data of normal pipeline section, determine abnormal data threshold value；

From the interior detection history magnetic flux leakage data of the normal pipeline section of multistage, determine the maximum of each normal pipeline section magnetic flux leakage data respectively Value, tries to achieve the meansigma methods of multistage normal pipeline section magnetic flux leakage data maximumMeanwhile, wherein one section of normal pipeline section leakage field is tried to achieve The meansigma methods of data；Detect one section of spontaneous corrosion pipeline section, and try to achieve the meansigma methods of this spontaneous corrosion pipeline section magnetic flux leakage data；Ask for institute State the ratio of the meansigma methods of spontaneous corrosion pipeline section magnetic flux leakage data and the meansigma methods of described one section of normal pipeline section magnetic flux leakage data；That is,

$\stackrel{\‾}{X_{\max }}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{X}_{\max },\stackrel{\‾}{X}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}{X}_i,\stackrel{\‾}{X_1}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}{X}_{\mathrm{li}},C=\frac{\stackrel{\‾}{X_1}}{\stackrel{\‾}{X}}$

In formula: n is normal pipe hop count amount；X_maxMaximum for every section of normal pipeline section magnetic flux leakage data；For multistage just The often meansigma methods of pipeline section magnetic flux leakage data maximum；It it is one section of normal pipeline section magnetic flux leakage data meansigma methods；X_iIt it is one section of normal pipeline section Magnetic flux leakage data point；N₁It is one section of normal pipeline section magnetic flux leakage data point quantity；For spontaneous corrosion pipeline section magnetic flux leakage data meansigma methods；X_1i For spontaneous corrosion pipeline section magnetic flux leakage data point；N₂For spontaneous corrosion pipeline section magnetic flux leakage data point quantity；C is one section of defect pipeline section leakage field number According to the ratio of meansigma methods and the meansigma methods of one section of normal pipeline section magnetic flux leakage data；

Then abnormal data threshold value is

In step 504: according to abnormal data threshold value, isolate the abnormal data in real-time pipeline detection magnetic flux leakage data, And abnormal data and axial location thereof are carried out record；Axially refer to pipe lengths；

The method separating described abnormal data is: regard more than the real-time pipeline detection magnetic flux leakage data of abnormal data threshold k For abnormal data；

In step 505: utilize correlation analysis, it is adjacent the comparison of data by abnormal data, it determines abnormal The effectiveness of data；Adjacent data refers to the sensor adjacent with sensor abnormal data being detected in same axial position The data of detection；

Use the method calculating the covariance that abnormal data is adjacent data, weigh abnormal data and be adjacent data Between dependency；Because close together between two adjacent groups sensor, so, two adjacent groups signal has certain similarity, If two groups of data have reached certain positive correlation degree, then it is assumed that data acquisition is correct, and isolated abnormal data is effective Data；Otherwise it is assumed that data acquisition is wrong, isolated abnormal data is invalid data；

COV (x, y)=E [(x_i-E(x_i))(y_i-E(y_i))] (i=1,2 ..., n)

In formula, (x y) is the covariance of two groups of data of x, y to COV；x_iFor exceptional data point；y_iFor x_iConsecutive number strong point； n₁For exceptional data point quantity；E (x) is exceptional data point expectation；E (y) is x_iConsecutive number strong point expectation；

What covariance represented is the dependency of two variablees, if the variation tendency of two variablees is consistent, say, that as Really one of them is more than the expected value of self, and another one is also greater than the expected value of self, then the association side between two variablees Difference be exactly on the occasion of.If the variation tendency of two variablees is contrary, i.e. one of them is more than the expected value of self, and another one is the least Expected value in self, then the covariance between two variablees is exactly negative value.If x Yu y is statistical iteration, then the two Between covariance be exactly 0；By the effectiveness of the threshold decision abnormal data of covariance coefficient r, this enforcement originating party formula is passed through The threshold value of the covariance coefficient r that experiment determines is 0.75, then more than the abnormal data corresponding to covariance coefficient of threshold value 0.75 It is effective abnormal data；

$r=\frac{\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}(x_i-\stackrel{\‾}{x})(y_i-\stackrel{\‾}{y})}{\sqrt{\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{(x_i-\stackrel{\‾}{x})}^2{(y_i-\stackrel{\‾}{y})}^2}}=\frac{n_1\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{x}_i{y}_i-\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{x}_i\·\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{y}_i}{\sqrt{n_1\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{x_i}^2-{\left(\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{x}_i\right)}^2}\·\sqrt{n_1\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{y_i}^2-{\left(\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{y}_i\right)}^2}}$

In step 506, utilizing cubic spline interpolation, the effective abnormal data recorded each sensor in real time respectively enters Row cubic spline interpolation, respectively obtains the cubic spline interpolation matched curve of the effective anomaly data that each sensor records in real time, It is called for short each bar curve；

The vertical coordinate of described each bar curve is abnormal data value, abscissa be abnormal data axially detect position；

The workflow of defective data characteristic extracting module, as shown in Figure 6, starts from step 601.

In step 602, ask for the axial eigenvalue of the effective anomaly data that each sensor records in real time；

A. determine peak value and the valley of each bar curve, and calculate the peak-to-valley value Y of each bar curve_p-p；As shown in Fig. 7 (a), peak Valley Y_p-pRefer to the difference of peak value and valley；That is, the maximum, of the effective anomaly data that each sensor records in real time is determined Little value and the difference of maxima and minima；Peak-to-valley value Y_p-pSize relevant with the degree of depth of defect of pipeline；

B. determine abscissa value and the abscissa value of time low valley point of the minimum valley point of each bar curve, and calculate each bar curve Paddy valley X_p-p；As shown in Fig. 7 (b), paddy valley X_p-pRefer to the abscissa value of minimum valley point and the abscissa of time low valley point The difference of value；That is, axial location and the axle of secondary minima of the minima of the effective anomaly data that each sensor records in real time are determined To position, and the difference of the axial location of the axial location of described minima and time minima；Paddy valley X_p-pSize and defect rotten The length of erosion is relevant；

Calculate the area of each bar curve the most respectively；

As shown in Fig. 7 (c), the computing formula of each bar area under the curve is:

$S=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\{x_i\left(t\right)-\min [x_i\left(t\right)\left]\right\}$

In formula, S is area under the curve, x_iT () is exceptional data point, min [x_i(t)] it is minimum abnormal data；Area under the curve Size is relevant with the comprehensive condition of the length depth of defect etching；

D. each bar curve energy is calculated；

The computing formula of each bar curve energy is:Wherein, S_eFor curve energy；Bent The size of heat input is relevant with the comprehensive condition of the length depth of defect etching；

E. the method utilizing wavelet transformation, asks for the flex point spacing on each bar curve；

Method: as shown in Fig. 7 (d), X_k-kRepresent flex point spacing, flex point spacing X_k-kThe length of size and defect etching have Close；Due to the required corner position taken respectively between crest and two troughs, therefore only to each bar curve two the most respectively Part between trough carries out wavelet transformation, and the wavelet basis chosen is the second dervative of smooth function；After continuous wavelet transform The zero point of curve, be the flex point of virgin curve, the present invention chooses mexican hat wavelet as mother wavelet, at yardstick a=4 to a In the range of=32, abnormal data is carried out continuous wavelet transform.Transform effect according to concrete abnormal signal chooses small echo chi Degree, during general yardstick a=8, effect is best, tries to achieve the position of each bar knee of curve, as shown in Figure 8；

F. the peak-to-valley value Y of each bar curve is asked for_p-pWith paddy valley X_p-pRatio, area S and peak-to-valley value Y_p-pRatio, face Long-pending S and paddy valley X_p-pRatio；

In step 603, ask for the circumferential eigenvalue of the effective anomaly data that each sensor records in real time；Circumference refers to pipe Cross-sectional periphery direction, road；

H. circumference abnormal data threshold value is determined；

Method flow is: the peak value of the effective anomaly data recorded with current sensor is as basic point, corresponding at this peak value Near axial location, first the peak value of the effective anomaly data that the sensor of on the left of current sensor or right side next-door neighbour records is entered Row judges, if the peak value of its effective anomaly data recorded more than or equal to current sensor, then using this peak value as peak-peak, After the same method, the peak value continuing on the effective anomaly data that follow-up adjacent sensors is recorded by the direction successively is carried out Judge, until the effective anomaly data that record less than previous sensor of the peak value of effective anomaly data that a rear sensor records Till peak value, obtain final peak-peak, and the sensor corresponding to this peak-peak and this peak-peak is carried out record； If the peak value of its effective anomaly data recorded less than current sensor, the then sensing on the right side of current sensor or left side next-door neighbour The peak value of the effective anomaly data that device records judges, if its effective anomaly data recorded more than or equal to current sensor Peak value, then using this peak value as peak-peak, after the same method, continue on the direction successively to follow-up adjacent sensors The peak value of the effective anomaly data recorded judges, until the peak value of effective anomaly data that a rear sensor records is less than front Till the peak value of the effective anomaly data that one sensor records, obtain final peak-peak, and to this peak-peak and this Big sensor corresponding to peak value carries out record；If the effective anomaly data that the adjacent sensors of left and right both direction records The peak value of the effective anomaly data that peak value respectively less than current sensor records, then effective anomaly data current sensor recorded Peak value as final peak-peak, and this peak-peak and current sensor are carried out record；By final peak-peak Half value as circumference abnormal data threshold value；

I. circumference abnormal data is determined；

Method is: the effective anomaly that the sensor that the left and right both direction of sensor belonging to described peak-peak is adjacent records The peak value of data is considered as lacking at pipeline section more than the sensor being considered as circumference abnormal data and correspondence thereof of circumference abnormal data threshold value Fall in coverage, obtain circumference abnormal data and the number of sensors of pipeline section defect covering；

And the axial location at the peak value of abnormal data that records of the sensor covering pipeline section defect carries out record, based on Sensing station difference and detection error, the axial location residing for each peak value has difference, utilizes method of least square to determine relatively The position that error is minimum, as the axial location of this group circumference abnormal data peak value；

J. utilize cubic spline interpolation, respectively circumference abnormal data is carried out cubic spline interpolation, obtain circumference abnormal The cubic spline interpolation matched curve of data, is called for short circumference curve；

The cubic spline interpolation of the effective anomaly data that a plurality of sensor that K. run of designing defect covers records in real time is intended Close curve area and；

L. take the method identical with A to F, repeat A to F, obtain the feature of circumference curve, including: peak-to-valley value, paddy Valley, area, energy, flex point spacing, peak-to-valley value and the ratio of paddy valley, area and the ratio of peak-to-valley value, area and paddy valley Ratio；

Although the foregoing describing the detailed description of the invention of the present invention, but the those skilled in the art in this area should managing Solving, these are merely illustrative of, and these embodiments can be made various changes or modifications, without departing from the principle of the present invention And essence.The scope of the present invention is only limited by the claims that follow.

Claims (3)

1. a method for detection generating date in pipe leakage, uses detection generating date device in pipe leakage Realizing, this device is arranged on in-pipeline detector, comprising: leakage field sensor unit, Signal-regulated kinase, A/D modulus of conversion Block and CPU；

Described leakage field sensor unit includes multiple leakage field sensor, and the plurality of leakage field sensor is along pipeline section circumferencial direction Being evenly arranged on in-pipeline detector, each leakage field sensor in described leakage field sensor unit is used to detect pipeline leakage Magnetic signal also exports the signal of telecommunication to Signal-regulated kinase；

Described Signal-regulated kinase is for being filtered and processing and amplifying the signal of telecommunication received, and will filter and processing and amplifying After the signal of telecommunication and deliver to A/D modular converter；

Described A/D modular converter for carrying out analog digital conversion and by after conversion to the signal of telecommunication received from Signal-regulated kinase Digital signal is sent to CPU；

Described CPU, including time-sequence control mode, defective data discrimination module, defective data characteristic extracting module and Data memory module；

Described time-sequence control mode is for controlling the change over order of each path of A/D modular converter；

Described defective data discrimination module is used for receiving the digital signal that A/D modular converter transmits, and to abnormal data therein And the effectiveness of abnormal data differentiates；Determine each effective anomaly data sequence number in the real time data received；Will The effective anomaly data determined send to defective data characteristic extracting module, are sent by sequence number corresponding for effective anomaly data To data memory module；

Described defective data characteristic extracting module is for extracting feature to the effective anomaly data received and effective by extract The feature of abnormal data sends to data memory module；

Described data memory module is for storing the effective anomaly data characteristics received and sequence number thereof and export；

It is characterized in that: comprise the following steps:

Step 1: obtain the interior detection history magnetic flux leakage data of real-time pipeline detection magnetic flux leakage data and normal pipeline section；

The magnetic leakage signal of the detection normal pipeline section of multistage, and set up the interior detection history magnetic flux leakage data record of normal pipeline section；Normal pipe Section refers to the pipeline section not having defect to occur；

Step 2: according to the interior detection history magnetic flux leakage data of normal pipeline section, determine abnormal data threshold value；

Step 3: according to abnormal data threshold value, isolate the abnormal data in real-time pipeline detection magnetic flux leakage data, and to exception Data and axial location thereof carry out record；Axially refer to pipe lengths；

Step 4: utilize correlation analysis, is adjacent the comparison of data by abnormal data, it determines having of abnormal data Effect property；Adjacent data refers to the number that the sensor adjacent with sensor abnormal data being detected detects in same axial position According to；

Use the method calculating the covariance that abnormal data is adjacent data, weigh abnormal data and be adjacent between data Dependency；The computing formula of covariance coefficient r is as follows, then corresponding to the covariance coefficient of the threshold value being more than covariance coefficient r Abnormal data is effective abnormal data；The threshold value of covariance coefficient r is determined by experiment；

$r=\frac{\underset{i=1}{\overset{n_1}{\Σ}}(x_i-\stackrel{\‾}{x})(y_i-\stackrel{\‾}{y})}{\sqrt{\underset{i=1}{\overset{n_1}{\Σ}}{(x_i-\stackrel{\‾}{x})}^2{(y_i-\stackrel{\‾}{y})}^2}}=\frac{n_1\underset{i=1}{\overset{n_1}{\Σ}}{x}_i{y}_i-\underset{i=1}{\overset{n_1}{\Σ}}{x}_i\·\underset{i=1}{\overset{n_1}{\Σ}}{y}_i}{\sqrt{n_1\underset{i=1}{\overset{n_1}{\Σ}}{x_i}^2-{\left(\underset{i=1}{\overset{n_1}{\Σ}}{x}_i\right)}^2}\·\sqrt{n_1\underset{i=1}{\overset{n_1}{\Σ}}{y_i}^2-{\left(\underset{i=1}{\overset{n_1}{\Σ}}{y}_i\right)}^2}},i=1,2,...,n_1$

In formula, x_iFor effective anomaly data point；For x_iMeansigma methods；y_iFor x_iConsecutive number strong point；For y_iMeansigma methods；n₁For Exceptional data point quantity；

Step 5: utilize cubic spline interpolation, the effective abnormal data recorded each sensor in real time respectively carries out three samples Bar interpolation, respectively obtains the cubic spline interpolation matched curve of the effective anomaly data that each sensor records in real time, is called for short each bar Curve；

The vertical coordinate of described each bar curve is abnormal data value, abscissa be abnormal data axially detect position；

Step 6: respectively according to each bar curve, ask for the eigenvalue of the effective anomaly data that each sensor records in real time；

Step 6.1: ask for the axial eigenvalue of effective anomaly data；

Step 6.1.1: determine peak value and the valley of each bar curve, and calculate the peak-to-valley value of each bar curve；Peak-to-valley value refers to peak Value and the difference of valley；That is, determine the maximum of effective anomaly data, minima and maximum that each sensor records in real time with The difference of little value；The size of peak-to-valley value is relevant with the degree of depth of defect of pipeline；

Step 6.1.2: determine abscissa value and the abscissa value of time low valley point of the minimum valley point of each bar curve, and calculate each bar The paddy valley of curve；Paddy valley refers to the abscissa value of minimum valley point and the difference of the abscissa value of time low valley point；That is, determine respectively The axial location of the minima of the effective anomaly data that sensor records in real time and the axial location of secondary minima, and described minimum The axial location of value and the difference of the axial location of time minima；The size of paddy valley is relevant with the length of defect etching；

Step 6.1.3: calculate the area of each bar curve respectively；

The computing formula of each bar area under the curve is:

$S=\underset{i=1}{\overset{N}{\Σ}}\{x_i\left(t\right)-min\[x_i\left(t\right)\]\}$

In formula, S is area under the curve, x_iT () is exceptional data point, min [x_i(t)] it is minimum abnormal data；N represents each bar curve Exceptional data point quantity；

Step 6.1.4: calculate each bar curve energy；

The computing formula of each bar curve energy is:Wherein, S_eFor curve energy；

Step 6.1.5: the method utilizing wavelet transformation, asks for the flex point spacing on each bar curve；

Method: due to the required corner position taken respectively between crest and two troughs, therefore only respectively to each bar curve Minimum valley point and secondary low valley point between part carry out wavelet transformation, the wavelet basis chosen is the second dervative of smooth function； The zero point of the curve after continuous wavelet transform, is the flex point of virgin curve；

Step 6.1.6: ask for peak-to-valley value and the ratio of paddy valley, area and the ratio of peak-to-valley value, area and the paddy of each bar curve The ratio of valley；

Step 6.2: ask for the circumferential eigenvalue of effective anomaly data；Circumference refers to pipeline section circumferencial direction；

Step 6.2.1: determine circumference abnormal data threshold value；

Method flow is: the peak value of the effective anomaly data recorded with current sensor is as basic point, in corresponding axial of this peak value Near position, first the peak value of the effective anomaly data that the sensor of on the left of current sensor or right side next-door neighbour records is sentenced Disconnected, if the peak value of its effective anomaly data recorded more than or equal to current sensor, then using this peak value as peak-peak, press According to same method, the peak value continuing on the effective anomaly data that follow-up adjacent sensors is recorded by the direction successively is sentenced Disconnected, until the peak of effective anomaly data that the peak value of effective anomaly data that a rear sensor records records less than previous sensor Till value, obtain final peak-peak, and the sensor corresponding to this peak-peak and this peak-peak is carried out record；If The peak value of its effective anomaly data recorded less than current sensor, the then sensor on the right side of current sensor or left side next-door neighbour The peak value of the effective anomaly data recorded judges, if its effective anomaly data recorded more than or equal to current sensor Peak value, then using this peak value as peak-peak, after the same method, continue on the direction successively to follow-up adjacent sensors The peak value of the effective anomaly data recorded judges, until the peak value of effective anomaly data that a rear sensor records is less than front Till the peak value of the effective anomaly data that one sensor records, obtain final peak-peak, and to this peak-peak and this Big sensor corresponding to peak value carries out record；If what the adjacent sensors of the left and right both direction of current sensor recorded has The peak value of the effective anomaly data that the peak value respectively less than current sensor of effect abnormal data records, then record current sensor The peak value of effective anomaly data is as final peak-peak, and this peak-peak and current sensor are carried out record；Will be The half value of whole peak-peak is as circumference abnormal data threshold value；

Step 6.2.2: determine circumference abnormal data；

Method is: the effective anomaly data that the sensor that the left and right both direction of sensor belonging to described peak-peak is adjacent records Peak value more than circumference abnormal data threshold value be considered as circumference abnormal data and the sensor of correspondence is considered as covering in pipeline section defect In the range of lid, obtain circumference abnormal data and the number of sensors of pipeline section defect covering；

And the axial location at the peak value of abnormal data that records of the sensor covering pipeline section defect carries out record, based on sensing Device position difference and detection error, the axial location residing for each peak value has difference, utilizes method of least square to determine relative error Minimum position, as the axial location of circumference abnormal data peak value；

Step 6.2.3: utilize cubic spline interpolation, carries out cubic spline interpolation to circumference abnormal data respectively, obtains circumference The cubic spline interpolation matched curve of abnormal data, is called for short circumference curve；

Step 6.2.4: according to the result of step 6.1.3, it is effective that a plurality of sensor that run of designing defect covers records in real time The area of the cubic spline interpolation matched curve of abnormal data and；

Step 6.2.5: taking the method identical with step 6.1.1 to step 6.1.6, repeated execution of steps 6.1.1 is to step 6.1.6, obtain the feature of circumference curve, including: peak-to-valley value, paddy valley, area, energy, flex point spacing, peak-to-valley value and paddy valley Ratio, area and the ratio of peak-to-valley value, the ratio of area and paddy valley；

Step 7: the eigenvalue of the effective anomaly data of output step 6 gained.

The method of detection generating date in pipe leakage the most according to claim 1, it is characterised in that: described step The interior detection history magnetic flux leakage data according to normal pipeline section described in 2 determines that the method for abnormal data threshold value is as follows:

From the interior detection history magnetic flux leakage data of the normal pipeline section of multistage, determine the maximum of each normal pipeline section magnetic flux leakage data respectively, Try to achieve the meansigma methods of multistage normal pipeline section magnetic flux leakage data maximumMeanwhile, wherein one section of normal pipeline section magnetic flux leakage data is tried to achieve Meansigma methods；Detect one section of defect pipeline section, and try to achieve the meansigma methods of this defect pipeline section magnetic flux leakage data；Ask for the leakage of described defect pipeline section The ratio of the meansigma methods of the meansigma methods of magnetic data and described one section of normal pipeline section magnetic flux leakage data；That is,

$\stackrel{\‾}{X_{\max }}=\frac{1}{n}\underset{i=1}{\overset{n}{\Σ}}{X}_{\max },\stackrel{\‾}{X}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\Σ}}{X}_i,\stackrel{\‾}{X_1}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\Σ}}{X}_{1i},C=\frac{\stackrel{\‾}{X_1}}{\stackrel{\‾}{X}}$

In formula: n is normal pipe hop count amount；X_maxMaximum for every section of normal pipeline section magnetic flux leakage data；For the normal pipeline section of multistage The meansigma methods of magnetic flux leakage data maximum；It it is one section of normal pipeline section magnetic flux leakage data meansigma methods；X_iIt it is one section of normal pipeline section leakage field number Strong point；N₁It is one section of normal pipeline section magnetic flux leakage data point quantity；It it is one section of existing defects pipeline section magnetic flux leakage data meansigma methods；X_1iIt is one Section existing defects pipeline section magnetic flux leakage data point；N₂It is one section of existing defects pipeline section magnetic flux leakage data point quantity；C is one section of defect pipeline section leakage The ratio of the meansigma methods of magnetic data and the meansigma methods of one section of normal pipeline section magnetic flux leakage data；

Then abnormal data threshold value is

The method of detection generating date in pipe leakage the most according to claim 1, it is characterised in that: described step The method of the abnormal data isolated in real-time pipeline detection magnetic flux leakage data described in 3 is as follows:

The method separating described abnormal data is: be considered as exception more than the real-time pipeline detection magnetic flux leakage data of abnormal data threshold value Data.