Polyurethane heat-preservation pipe medium layer defect discrimination method and application

Abstract

The application discloses a polyurethane thermal insulation pipe medium layer defect discrimination method and application, and a detection signal for discriminating three medium layer defects is generated when a capacitive imaging detection technology is used for detecting a polyurethane thermal insulation pipe, and the detection signal is applied to a detection signal obtained by using the capacitive imaging detection technology to detect defects. According to a capacitive imaging detection principle, each detection result value of a capacitive imaging detection signal is obtained by integrating a three-dimensional effective detection area and a corresponding detection sensitivity distribution value, so that discrimination information of the three different medium layer defects is contained in the capacitive imaging detection signal. By fusing a detection signal strength, a distortion rate, a gradient value, a compensation factor, a unit coefficient and two preset threshold values, it is found that there is an obvious demarcation phenomenon in a fusion value of the three medium layer defects, so that discrimination of the three medium layer defects of the polyurethane thermal insulation pipe is realized. The method provided by the application can discriminate the three medium layer defects by using a single capacitive sensor at one time, reduces discrimination cost and improves discrimination accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nondestructive testing signal processing, and in particular to a polyurethane heat preservation pipe medium layer defect discrimination method and application. BACKGROUND

[0002] The polyurethane heat preservation pipe is a kind of high-efficiency heat preservation material, which is widely used in the fields of petroleum, chemical industry, metallurgy, electric power and heating. In the fields of petroleum, chemical industry and metallurgy, the polyurethane heat preservation pipe can keep the temperature of the medium in the pipeline stable, improve the production efficiency, and prevent the pipeline from freezing and cracking in a low-temperature environment. In the field of electric power, the polyurethane heat preservation pipe can reduce the energy loss of the electric wire and improve the transmission efficiency. In the field of heating, the polyurethane heat preservation pipe can reduce the heat loss and improve the heating efficiency. The defects of the working pipe, the heat preservation layer and the protective layer in the polyurethane heat preservation pipe will all cause the heat preservation performance of the pipeline to decrease and affect the service life of the pipeline. Discriminating the above three kinds of medium layer defects is helpful to take reasonable maintenance schemes, which is of great significance to ensure the safety of the application field of the polyurethane heat preservation pipe and prolong the service life of the polyurethane heat preservation pipe.

[0003] The capacitive imaging detection technology is a new type of nondestructive testing technology, which can effectively detect defects of insulating materials and defects of metal materials. At present, although the capacitive imaging detection technology can distinguish the three kinds of medium layer defects of the polyurethane heat preservation pipe by using the distance effect or the lift-off effect, the above methods can only effectively distinguish the three kinds of medium layer defects when all of them exist. Moreover, the polyurethane heat preservation pipe needs to be detected multiple times, which increases the detection cost and time. Therefore, it is necessary to propose a method that can accurately discriminate single medium layer defect, two kinds of medium layer defects and three kinds of medium layer defects of the polyurethane heat preservation pipe by using the capacitive sensor at one time. SUMMARY

[0004] In order to overcome the above problems in the prior art, the present application provides a polyurethane heat preservation pipe medium layer defect discrimination method and application.

[0005] The technical scheme adopted by the present application to solve its technical problems is: a polyurethane heat preservation pipe medium layer defect discrimination method based on lift-off effect, comprising the following steps:

[0006] Step 1, receiving the input capacitive imaging detection signal under the same lift-off height and working electrode spacing;

[0007] Step 2, performing low-pass filtering on the capacitive imaging detection signal obtained in step 1 to obtain a low-pass filtering signal A n And AS n ;

[0008] Step 3, obtaining vertical height h and horizontal length l from gradient value C n and AS n deriving distortion rate B n , obtaining gradient value C n from A n = grad(A n ), deriving gradient peak-to-peak vertical height h n and horizontal length l n from gradient value C n , deriving peak value D n from low-pass filtered signal A n ;

[0009] Step 4, performing division operation on vertical height h n and distortion rate B n to obtain E n = h n / B n , performing division operation on horizontal length l n and peak value D n to obtain F n = l n / D n ;

[0010] Step 5, introducing compensation factor a c , performing division operation on E n and compensation factor a c to obtain G n = E n / a c , performing multiplication operation on F n and compensation factor a c to obtain H n = F n ×a c ;

[0011] Step 6, introducing unit coefficient b c , performing multiplication operation on H n and unit coefficient b c to obtain I n = H n ×b c ;

[0012] Step 7, performing fusion processing on G n and I n to obtain fusion value J n = G n +I n ;

[0013] Step 8, judging whether fusion value J n is less than or equal to preset threshold Pn1 If yes, it is determined to be a defect in the protective layer tube; if no, proceed to the next step of judgment.

[0014] Step 9, determine the fusion value J n Is it less than or equal to the preset threshold P? n2 If yes, it is determined to be a defect in the insulation layer; if no, it is determined to be a defect in the working pipe.

[0015] The above-mentioned method for identifying defects in the dielectric layer of a polyurethane insulation pipe, wherein the capacitance imaging detection signal at the same lift-off height and working electrode spacing in step 1 includes the lift-off height H m and working electrode spacing D m The capacitance sensor detects the detection signal Y of the polyurethane insulation pipe containing dielectric layer defects. n Lifting height H m and working electrode spacing D m The detection signal YS of the capacitive sensor under the condition of detecting defect-free polyurethane insulation pipe. n .

[0016] The above-mentioned method for identifying defects in the medium layer of a polyurethane insulation pipe, wherein the distortion rate BY in step 3... n =(A n -AS n ) / AS n .

[0017] In the above-mentioned method for identifying defects in the medium layer of a polyurethane insulation pipe, the compensation factor a in step 5 is... c It is the square of the charge amplification factor.

[0018] The above-mentioned method for identifying defects in the medium layer of a polyurethane insulation pipe, wherein the unit coefficient b c Related to the application scenarios of capacitance imaging detection technology, the unit coefficient b is used in simulation applications. c The value is 1pF, and the unit coefficient b is used in experimental applications. c It is 1mV.

[0019] The above-mentioned method for identifying defects in the medium layer of a polyurethane insulation pipe, wherein the preset threshold P in step 8... n1 and the preset threshold P in step 9 n2 It is based on the lift height H m and working electrode spacing D m The lower capacitance sensor effectively detects the maximum and minimum volume defects set in different dielectric layers, and the preset threshold P n1 Less than the preset threshold P n2 .

[0020] The above-mentioned method for identifying defects in the medium layer of polyurethane insulation pipes is applied to determine defects in polyurethane insulation pipes containing defects in the medium layer.

[0021] The beneficial effects of this invention are that the method provided by this invention can identify three types of dielectric layer defects at one time using a single capacitive sensor. By combining distortion rate, gradient value, compensation factor, unit coefficient and two preset threshold judgments, the method achieves the goal of identifying three types of dielectric layer defects at one time, reducing the identification cost and improving the identification accuracy. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Figure 1 This is a flowchart of the defect identification method of the present invention;

[0024] Figure 2 This is a flowchart of the preset threshold setting process of the present invention;

[0025] Figure 3 This is a structural diagram of the polyurethane insulation pipe test specimen in Embodiment 1 of the present invention;

[0026] Figure 4 A fusion value diagram of the capacitive sensor detected by the test piece (with 10 different defect widths) according to Embodiment 1 of the present invention;

[0027] Figure 5 The image shows the fusion values ​​of the capacitive sensor detected by the test piece (with 10 different defect depths) according to Embodiment 1 of the present invention.

[0028] Figure 6 This is a fusion value diagram of the capacitance sensor detected by the test piece (with 9 different insulation layer thicknesses) provided in Embodiment 1 of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] like Figure 1 As shown, this invention discloses a method for identifying defects in the dielectric layer of polyurethane insulation pipes. This method can be applied to identify defects in polyurethane insulation pipes containing dielectric layer defects at multiple lift heights. Based on the principle of capacitive imaging detection, each detection result value of the capacitive imaging detection signal is derived by integrating the three-dimensional effective detection area with the corresponding detection sensitivity distribution value. This allows the identification information of three different dielectric layer defects to be contained within the capacitive imaging detection signal. By fusing the detection signal intensity, distortion rate, gradient value, compensation factor, unit coefficient, and two preset thresholds, a clear boundary phenomenon is found between the fused values ​​of the three dielectric layer defects, thereby achieving the identification of the three dielectric layer defects in the polyurethane insulation pipe. Specifically, the method includes the following steps:

[0031] S101, receiving the inputted capacitance imaging detection signal under the same lift-off height and working electrode spacing, wherein the capacitance imaging detection signal under the same lift-off height and working electrode spacing comprises detection signal (Y1, Y2, …, Y m , Y m ) of the polyurethane insulation pipe with medium layer defect detected by the capacitance sensor under the lift-off height H n-1 and working electrode spacing D n , and detection signal (YS1, YS2, …, YS m , YS m ) of the polyurethane insulation pipe without defect detected by the capacitance sensor under the lift-off height H n-1 and working electrode spacing D n .

[0032] Specifically, the signal and mathematical processing software receives the inputted capacitance imaging detection signal under the same lift-off height and working electrode spacing, wherein the capacitance imaging detection signal under the same lift-off height and working electrode spacing comprises detection signal (Y1, Y2, …, Y m , Y m ) of the polyurethane insulation pipe with medium layer defect detected by the capacitance sensor under the lift-off height H n-1 and working electrode spacing D n , and detection signal (YS1, YS2, …, YS m , YS m ) of the polyurethane insulation pipe without defect detected by the capacitance sensor under the lift-off height H n-1 and working electrode spacing D n .

[0033] S102, low-pass filtering the capacitance imaging detection signal to obtain low-pass filtering signal A n and AS n .

[0034] Specifically, after the signal or mathematical processing software receives the detection signal, the low-pass filtering function in the software is used to process the detection signal to obtain filtering signal A n and AS n , and the noise signal doped in the detection signal is filtered out.

[0035] S103, calculating the distortion rate B n = (A n - AS n ) / AS n of the low-pass filtering signal A n and AS n .

[0036] S104, calculating the distortion rate B nGradient value C n = grad(A n );

[0037] S105, the gradient value C n is calculated for the vertical height h n and the horizontal length l n between the gradient peaks.

[0038] S106, the peak D n at the center of the defect is calculated for the low-pass filtered signal A n .

[0039] Specifically, the distortion rate B n = (A n - AS n ) / AS n is calculated for the low-pass filtered signal A n and AS n by signal or mathematical processing software. The gradient value C n = grad(A n ) is calculated for the low-pass filtered signal A n by signal or mathematical processing software, and the vertical height h n and the horizontal length l n between the gradient peaks are calculated for the gradient value C n . The peak D n at the center of the defect is calculated for the low-pass filtered signal A n .

[0040] Preferably, the distortion rate B n = (A n - AS n ) / AS n is calculated for the low-pass filtered signal A n and AS n , the distortion rate B n = (A n - AS n ) / AS n is calculated for the low-pass filtered signal A n and AS n , which includes calculating the distortion rate B n by excel software, calculating the distortion rate B n by labview software, calculating the distortion rate B n by mathtype software, calculating the distortion rate B n by matlab software, and calculating the distortion rate B n by Python software.

[0041] Preferably, the low-pass filtered signal An Gradient value C n = grad(A n ), characterized in that the low-pass filtered signal A n Gradient value C n = grad(A n ) comprises obtaining the gradient value C n by means of excel software, obtaining the gradient value C n by means of labview software, obtaining the gradient value C n by means of mathtype software, obtaining the gradient value C n by means of matlab software and obtaining the gradient value C n by means of Python software.

[0042] S107, performing a division operation on the vertical height h n and the distortion rate B n to obtain E n = h n / B n ;

[0043] S108, performing a division operation on the horizontal length l n and the peak value D n to obtain F n = l n / D n .

[0044] Specifically, by means of signal or mathematical processing software, a division operation is performed on the vertical height h n and the distortion rate B n to obtain E n = h n / B n ; by means of signal or mathematical processing software, a division operation is performed on the horizontal length l n and the peak value D n to obtain F n = l n / D n .

[0045] S109, introducing a compensation factor a c ;

[0046] S110, performing a division operation on the E n and the compensation factor a c to obtain G n = E n / a c ;

[0047] S111, performing a multiplication operation on the F n and the compensation factor a c to obtain Hn = F n × a c .

[0048] Specifically, a compensation factor a c is introduced by signal or mathematical processing software, wherein the compensation factor a c is the square of the charge amplification multiple. By signal or mathematical processing software, division operation is performed on the E n and the compensation factor a c to obtain G n = E n / a c . By signal or mathematical processing software, multiplication operation is performed on the F n and the compensation factor a c to obtain H n = F n × a c .

[0049] S112, a unit coefficient b c is introduced;

[0050] S113, multiplication operation is performed on the H n and the unit coefficient b c to obtain I n = H n × b c .

[0051] Specifically, a unit coefficient b c is introduced by signal or mathematical processing software, wherein the unit coefficient b c is related to the application scenario of the capacitive imaging detection technology, and in the simulation application, the unit coefficient b c is 1 pF, and in the experimental application, the unit coefficient b c is 1 mV. By signal or mathematical processing software, multiplication operation is performed on the H n and the unit coefficient b c to obtain I n = H n × b c .

[0052] S114, fusion processing is performed on the G n and the I n to obtain a fusion value J n = G n + I n .

[0053] Specifically, fusion processing is performed on the G n and the I n by signal or mathematical processing software to obtain a fusion value J n = G n + In .

[0054] Preferably, the G n and I n are fused to obtain a fusion value J n = G n + I n , the G n and I n are fused to obtain a fusion value J n = G n + I n , the fusion value J n is obtained by means of excel software, the fusion value J n is obtained by means of labview software, the fusion value J n is obtained by means of mathtype software, the fusion value J n is obtained by means of matlab software, and the fusion value J n is obtained by means of Python software.

[0055] S115, it is judged whether the fusion value J n is less than or equal to a preset threshold P n1 ;

[0056] S116, if yes, it is determined that the protective layer is defective; if no, the next step is performed.

[0057] S117, it is judged whether the fusion value J n is less than or equal to a preset threshold P n2 ;

[0058] S118, if yes, it is determined that the heat preservation layer is defective;

[0059] S119, if no, it is determined that the working tube is defective.

[0060] Specifically, the preset threshold P n1 and the preset threshold P n2 are set according to the lift-off height H m and the working electrode spacing D m , and the preset threshold P n1 is less than the preset threshold P n2 , for example, the fusion values of the protective layer maximum bulk defect, the heat preservation layer maximum bulk defect and the working tube maximum bulk defect detected by the capacitive sensor are 1, 3.3 and 5.2 respectively, and the fusion values of the protective layer minimum bulk defect, the heat preservation layer minimum bulk defect and the working tube minimum bulk defect detected by the capacitive sensor are -3, 2.7 and 3.8 respectively, the preset threshold P n1 may be selected as a value between 1 and 2.7, and the preset threshold P n2A value between 3.3 and 3.8 can be selected, such as Figure 2 As shown, comprising: a01, obtaining the detection results of the capacitive sensor effectively detecting the maximum and minimum body defects of different medium layers; a02, setting a preset threshold P n1 and the preset threshold P n2 , and the preset threshold P n1 is less than the preset threshold P n2 .

[0061] The application provides a method for distinguishing three medium layer defects of polyurethane insulation pipes based on capacitive imaging detection technology. After receiving input capacitive imaging detection signals under the same lift-off height and working electrode spacing, the capacitive imaging detection signals under the same lift-off height and working electrode spacing are obtained, including the detection signals (Y1, Y2, …, Y m , Y m ) of the polyurethane insulation pipe containing medium layer defects detected by the capacitive sensor under the lift-off height H n-1 and the working electrode spacing D n , the detection signals (YS1, YS2, …, YS m , YS m ) of the defect-free polyurethane insulation pipe detected by the capacitive sensor under the lift-off height H n-1 and the working electrode spacing D n ; through signal and mathematical processing software, low-pass filter processing is performed on the capacitive imaging detection signals to obtain low-pass filter processing signals A n and AS n , and the noise signals doped in the detection signals are filtered out; through signal and mathematical processing software, the distortion rate B n =(A n -AS n ) / AS n of the low-pass filter processing signals A n and AS n is calculated; through signal and mathematical processing software, the gradient value C n =grad(A n ) of the low-pass filter processing signal A n is calculated, and the vertical height h n and the horizontal length l n between the gradient peaks of the gradient value C n are calculated; the peak value D n at the defect center of the low-pass filter processing signal A n is calculated; through signal and mathematical processing software, the vertical height h n and the distortion rate B n are divided to obtain E n =h n / B n; through signal and mathematical processing software, the horizontal length l n and peak D n Division operation F n = l n / D n ; in order to avoid the influence of different charge amplification multiple of different capacitive imaging detection system on the discrimination method, the compensation factor a c , wherein the compensation factor a c is the square of the charge amplification multiple; through signal and mathematical processing software, the E n and compensation factor a c Division operation G n = E n / a c ; through signal and mathematical processing software, the F n and compensation factor a c Multiplication operation H n = F n ×a c ; in order to make the discrimination method suitable for different application scenarios, the unit coefficient b c , wherein the unit coefficient b c Related to capacitive imaging detection technology application scene, simulation application unit coefficient b c 1 pF, experimental application unit coefficient b c 1 mV; through signal and mathematical processing software, the H n and unit coefficient b c Multiplication operation I n = H n ×b c ; through signal and mathematical processing software, the G n and I n Fusion processing to obtain fusion value J n = G n +I n , the obtained fusion value J n Fusion of detection signal intensity, distortion rate, gradient value and other information, so as to maximize the discrimination information of three kinds of medium layer defects; whether the fusion value J n Is less than or equal to the preset threshold P n1 ; if yes, it is determined that the protective layer is defective; if not, the next step is performed; whether the fusion value J n Is less than or equal to the preset threshold P n2If yes, it is determined that the insulation layer is defective; if no, it is determined that the working tube is defective. The method provided by the application can determine three kinds of medium layer defects at one time by using a single capacitive sensor, and the method for determining the three kinds of medium layer defects is realized by combining the distortion rate, the gradient value, the compensation factor, the unit coefficient, and the two preset threshold values, so as to achieve the purposes of determining the three kinds of medium layer defects at one time, reducing the determination cost, and improving the determination accuracy.

[0062] Example 1

[0063] Based on the above determination method, the present embodiment gives the lift-off height H m and the working electrode spacing D m are specific numerical experimental implementation methods to verify the effectiveness of the method. The schematic diagram of the polyurethane insulation tube tested is shown in Figure 3 The polyurethane insulation tube is composed of a protective layer, an insulation layer, and a working tube, and the three kinds of medium layers are respectively composed of high-density polyethylene, rigid polyurethane foam plastic, and steel three different materials. Each medium layer has a defect. A capacitive sensor with a working electrode spacing D m of 4 mm is used to detect the three kinds of medium layer defects of the polyurethane insulation tube under the experimental conditions of a lift-off height H m of 3 mm. In addition, in order to avoid the influence of the medium layer defect width, the medium layer defect depth, and the medium layer thickness on the present application, 10 different defect widths, 10 different defect depths, and 9 different medium layer thicknesses are detected in the experimental process, and the fusion value graphs obtained by using the present application are shown in Figures 4 to 6 In Figure 4 , the fusion values of the 10 different defect widths are judged by using a preset threshold P n1 = 2.5 and a preset threshold P n2 = 3.5, and according to the present application, the number I is determined as a protective layer defect, the number II is determined as an insulation layer defect, and the number III is determined as a working tube defect. The three kinds of medium layer defect determination results are completely consistent with the icons. In Figure 5 , the fusion values of the 10 different defect depths are also judged by using a preset threshold P n1 = 2.5 and a preset threshold P n2 = 3.5, and according to the present application, the number I is determined as a protective layer defect, the number II is determined as an insulation layer defect, and the number III is determined as a working tube defect. The three kinds of medium layer defect determination results are completely consistent with the icons. In Figure 6 , the fusion values of the 9 different medium layer thicknesses are also judged by using a preset threshold P n1 = 2.5 and a preset threshold P n2 = 3.5, and according to the present application, the number I is determined as a protective layer defect, the number II is determined as an insulation layer defect, and the number III is determined as a working tube defect. The three kinds of medium layer defect determination results are completely consistent with the icons.

[0064] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, and the scope of protection of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements are also considered to fall within the protection scope of the present application.

Claims

1. A method for judging defects of a polyurethane thermal insulation pipe medium layer based on lift-off effect, characterized in that, The method comprises the following steps: Step 1, receiving inputted capacitance imaging detection signals under the same lift-off height and working electrode spacing; Step 2, low-pass filtering the capacitive imaging detection signal obtained in step 1 to obtain a low-pass filtered signal A n and AS n ; Step 3: Process the low-pass filtered signal A obtained in Step 2. n and AS n Find the distortion rate B n , for A n Find the gradient value C n =grad(A n ), for the gradient value C n Find the vertical height h between gradient peaks. n and horizontal length l n ; for the low-pass filtered signal A n Find the peak value D at the defect center. n ; Step 4, the vertical height h of the result obtained in Step 3 n and the distortion rate B n Division operation E is obtained n = h n / B n ; the horizontal length l n and the peak value D n Division operation F is obtained n = l n / D n ; Step 5, introducing the compensation factor a c , the E n , and the compensation factor a c are divided to obtain G n = E n / a c ; the F n , and the compensation factor a c are multiplied to obtain H n =F n ×a c ; Step 6, introduce the unit coefficient b c For the H n and unit coefficient b c Performing multiplication yields I n =H n ×b c ; Step 7, fusing the G n and I n to obtain a fusion value J n =G n +I n ; Step 8, judging fusion value J n whether less than or equal to preset threshold P n1 ; If yes, determining that the protective layer pipe is defective; if no, proceeding to the next step of judgment; Step 9, judging fusion value J n whether less than or equal to preset threshold P n2 ; If yes, determining that the thermal insulation layer is defective; if no, determining that the working pipe is defective; In step 1, the capacitance imaging detection signal at the same lift-off height and working electrode spacing includes the lift-off height H. m and working electrode spacing D m The capacitance sensor detects the detection signal Y of the polyurethane insulation pipe containing dielectric layer defects. n and lift-off height H m and working electrode spacing D m The detection signal YS of the capacitive sensor under the condition of detecting defect-free polyurethane insulation pipe. n .

2. The method according to claim 1, wherein the method is characterized by, The distortion rate BY in step 3 n = (A n - AS n ) / AS n .

3. The method according to claim 1, wherein the method is characterized by: The compensation factor a in step 5 c is the square of the charge amplification.

4. The method according to claim 1, wherein the method is characterized by, The unit coefficient b c With the application scene of capacitive imaging detection technology, the unit coefficient b c in simulation application is 1 pF, and the unit coefficient b c in test application is 1 mV.

5. The method according to claim 1, wherein the method is characterized by: The preset threshold P in step 8 n1 and the preset threshold P in step 9 n2 is according to the lift-off height H m and the working electrode spacing D m The capacitance sensor effectively detects the maximum and minimum bulk defect settings of different medium layers, and the preset threshold P n1 is less than the preset threshold P n2 .

6. Use of a method for detecting defects in a polyurethane thermal pipe medium layer based on the lift-off effect according to any one of claims 1 to 5, characterized in that, The method is applied to the defect judgment of the polyurethane thermal insulation pipe containing a medium layer defect.

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