Orthogonal twinning method and apparatus for oil and gas pipeline crack signal, and storage medium

Abstract

An orthogonal twinning method and apparatus for an oil and gas pipeline crack signal, and a storage medium. The method comprises: acquiring a triaxial magnetic flux leakage measurement signal modulus of an inner wall crack or outer wall crack under unidirectional direct current excitation conditions, and acquiring a dynamic magnetic signal or eddy current signal under dynamic magnetic field excitation conditions orthogonal to the direct current excitation; inputting the triaxial magnetic flux leakage measurement signal modulus into a crack signal orthogonal twinning model, and obtaining a magnetic flux leakage enhancement estimation signal; extracting a crack scale initial value from the magnetic flux leakage enhancement estimation signal and a 0-1 binarized signal thereof, and substituting the crack scale initial value into a magnetic dipole model of the inner wall crack or outer wall crack to iteratively calculate an optimal scale intermediate variable; and optionally, under the excitation of virtual orthogonal twinning direct current excitation, using the optimal scale intermediate variable as an input, and, by means of "dynamic magnetic or eddy current signal mapping", outputting a twin dynamic magnetic or eddy current response signal.

Description

Orthogonal twinning method, device, and storage medium for oil and gas pipeline crack signals

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on August 16, 2023, with application number 2023110346378 and invention name “A method, device and storage medium for orthogonal twinning of oil and gas pipeline crack signals”, the contents of which should be understood as incorporated into this application by reference. Technical Field

[0002] The embodiments of the present disclosure relate to, but are not limited to, the field of crack detection technology, and in particular to an orthogonal twinning method, device, and storage medium for oil and gas pipeline crack signals. The applicable objects of twinning include, but are not limited to, pipe body crack signals, elbow crack signals, girth weld crack signals of oil and gas pipelines or hydrogen pipelines, high-speed rail crack signals, tank bottom plate cracks, and other ferromagnetic metal material crack signals. Background Art

[0003] Ferromagnetic metal materials such as oil and gas pipelines, hydrogen pipelines, high-speed rails, and oil storage tanks are subject to long-term operation in complex natural environments and pressure loads. Stress cracks, fatigue cracks, brittle cracks, hydrogen-induced cracks, and other microcracks can develop within the materials or on their inner and outer walls. These cracks can then develop into visible fissures or larger metal losses. Cracks are one of the primary sources of defects in metal materials during service.

[0004] Compared to cracks, metal loss is easier to detect due to its larger size. Currently, several mature technologies are available to accurately detect, identify, and quantify metal loss defects. However, cracks are typically small in size, resulting in weak response signals. This is especially true at detection speeds greater than 2 m / s. Detecting, identifying, and quantifying early-stage microcracks is more challenging, especially if the crack is in its initial stages of development or is in the early stages of service.

[0005] Furthermore, the crack response signal during crack detection is closely related to the excitation signal, excitation direction, and sensing sensor. For example, in unidirectional magnetic flux leakage (MFL) inspection of oil and gas pipelines, based on the excitation direction, for cracks of the same size, the larger the modulus of the crack's tilt angle relative to the excitation direction, the greater the amplitude of the MFL response signal; the smaller the modulus of the crack's tilt angle relative to the excitation direction, the smaller the amplitude of the MFL response signal. Therefore, the axial crack or fissure signal under axial excitation is extremely weak, and is even considered unattainable by the industry at an industrial level. Changing the excitation from axial to circumferential can increase the strength of the axial crack or fissure signal, but circumferential excitation detectors are less effective at detecting circumferential cracks or fissures. Consequently, the industry has developed a serial MFL detector solution that combines an axial excitation MFL detector with a circumferential excitation MFL detector. It is obvious that the combined MFL detector solution significantly increases the equipment manufacturing cost and the detection engineering cost; at the same time, the traditional MFL is restricted by its working principle, and has inherent deficiencies or is simply impossible to detect, identify, and quantify tiny cracks.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure provide an orthogonal twinning method, device, and storage medium for oil and gas pipeline crack signals. The concept of "orthogonal twinning" here is defined as, under the condition that the original response signal of the unidirectional DC excitation is known, a response signal corresponding to a twin magnetic field that is in the same plane, equal in size, and perpendicular to the direction as the unidirectional DC excitation is obtained by transformation. The response signal is defined as a "twin response signal" (such as a twin leakage magnetic response signal or a twin dynamic magnetic / eddy current response signal), which also forms a twin relationship with the response signal of the original unidirectional DC excitation, that is, the "original response signal" (such as the original leakage magnetic response signal or the original dynamic magnetic / eddy current response signal). By jointly processing the "twin response signal" and the "original response signal", the overall signal strength of the crack response signal at any inclination angle can be greatly improved, especially the response signal strength of the crack distributed parallel to the excitation direction. The "orthogonal twinning" method includes but is not limited to a machine learning model mapping method, a mathematical analysis method, a circuit (or electromagnetic) simulation method, a hardware device real signal generation method, etc. The process of transforming the "original response signal" into a "twin response signal" is defined as an "orthogonal twin transformation".

[0008] The technical solutions adopted in the embodiments of the present disclosure are:

[0009] An embodiment of the present disclosure provides an orthogonal twinning method for oil and gas pipeline crack signals, including: obtaining the modulus of a three-axis leakage magnetic measurement signal of an inner wall crack or an outer wall crack under unidirectional DC excitation conditions, and obtaining a dynamic magnetic signal or eddy current signal under a dynamic magnetic field excitation condition orthogonal to the DC excitation, wherein the unidirectional DC excitation refers to a DC excitation method with a single direction; inputting the modulus of the three-axis leakage magnetic measurement signal into an orthogonal twin model of a crack signal to obtain a leakage magnetic enhancement estimation signal, wherein the leakage magnetic enhancement estimation signal is a function of the modulus of a simulated orthogonal twin three-axis leakage magnetic signal, wherein the modulus of the orthogonal twin three-axis leakage magnetic signal is the modulus of a three-axis leakage magnetic response signal under virtual orthogonal twin DC excitation conditions, wherein the virtual orthogonal twin DC excitation is in the same plane as the unidirectional DC excitation, is equal in size, and is perpendicular in direction.

[0010] Optionally, the magnetic flux leakage enhancement estimation signal is a simulated optimal three-axis magnetic flux leakage signal modulus S Ao Compared with the simulated orthogonal twin three-axis magnetic leakage signal modulus S ⊥ The function f(αS Ao ,αS ⊥ ), the simulated optimal three-axis magnetic flux leakage signal modulus S Ao Is to make ||αS A -M A || F The minimum simulated three-axis magnetic leakage signal modulus, M A is the modulus of the three-axis magnetic flux leakage measurement signal, S A Substituting the crack size into the magnetic dipole model to obtain the simulated triaxial magnetic flux leakage signal modulus, ‖·‖ F is the Frobenius norm of the matrix, α is the adjustment factor, where: are respectively the X-axis component, Y-axis component and Z-axis component of the simulated three-axis magnetic leakage signal, the X-axis direction is the same as the excitation direction of the unidirectional DC excitation, the Y-axis direction is perpendicular to the X-axis direction in the plane of the oil and gas pipeline, and the Z-axis direction is perpendicular to the X-axis direction and the Y-axis direction respectively; They are the X-axis component, Y-axis component and Z-axis component of the orthogonal twin three-axis magnetic leakage signal; K≥1; the adjustment factor α is equal to M A The maximum element of the matrix is ​​divided by the maximum element of the initial matrix, where the initial matrix is ​​a matrix composed of the simulated triaxial magnetic flux leakage signal modulus values ​​calculated by substituting the measured crack dimensions into the magnetic dipole model.

[0011] Optionally, the method further includes: for multiple crack samples, training the orthogonal twin model of the crack signal by using the modulus value of the triaxial magnetic flux leakage measurement signal under the condition of unidirectional DC excitation and the corresponding magnetic flux leakage enhancement estimation signal, wherein the magnetic flux leakage enhancement estimation signal corresponding to each crack sample is generated by the following method: taking the measured scale of the crack sample as the initial crack scale; dividing the maximum element of the M A matrix by the maximum element of the initial matrix to obtain the adjustment factor α, where M A is the modulus value of the triaxial magnetic flux leakage measurement signal; calculating ||αS A -M A || F , where S A is the modulus value of the simulated triaxial magnetic flux leakage signal obtained by substituting the crack scale into the magnetic dipole model, and ‖·‖ F is the Frobenius norm of the matrix; repeatedly adjusting the crack scale and inputting the crack scale into the crack magnetic dipole model until the optimal crack scale that minimizes ||αS A -M A || F is obtained. The modulus value of the simulated triaxial magnetic flux leakage signal corresponding to the optimal crack scale is the simulated optimal triaxial magnetic flux leakage signal modulus value S Ao ; inputting the optimal crack scale into the crack orthogonal magnetic dipole model to obtain the modulus value of the simulated orthogonal twin triaxial magnetic flux leakage signal S ⊥ ; substituting the S Ao and S ⊥ into the formula K≥1, thereby generating the magnetic flux leakage enhancement estimation signal.

[0012] Optionally, for the multiple crack samples, training the orthogonal twin model of the crack signal by using the modulus value of the triaxial magnetic flux leakage measurement signal under the condition of unidirectional DC excitation and the corresponding magnetic flux leakage enhancement estimation signal includes: marking the modulus value of the triaxial magnetic flux leakage measurement signal and the corresponding magnetic flux leakage enhancement estimation signal of each crack sample under the condition of unidirectional DC excitation as a group of orthogonal twin mapping pairs; randomly splitting the N groups of orthogonal twin mapping pairs into a training set and a test set according to the ratio k:(1-k), where 0 < k < 1; using k×N groups of the orthogonal twin mapping pairs to build and train the orthogonal twin model of the crack signal, and using (1-k)×N groups of the orthogonal twin mapping pairs to test the orthogonal twin model of the crack signal.

[0013] Optionally, the method further includes: calculating an optimal crack scale intermediate variable based on the leakage magnetic enhancement estimation signal; inputting the crack scale into a crack magnetic dipole model and a crack orthogonal magnetic dipole model respectively to obtain a simulated three-axis leakage magnetic signal modulus and a simulated orthogonal twin three-axis leakage magnetic signal modulus; calculating a secondary leakage magnetic enhancement estimation signal based on the obtained simulated three-axis leakage magnetic signal modulus and the simulated orthogonal twin three-axis leakage magnetic signal modulus; and detecting whether the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal is equal to or greater than the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal. is less than a preset threshold; when the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal is less than a preset threshold, the input crack scale is used as an intermediate variable of the optimal crack scale; when the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal is greater than or equal to a preset threshold, the input crack scale is adjusted, and the process returns to the step of inputting the crack scale into the crack magnetic dipole model and the crack orthogonal magnetic dipole model respectively to continue execution.

[0014] Optionally, the initial value of the crack scale is calculated based on the leakage magnetic enhancement estimation signal, including: binarizing the leakage magnetic enhancement estimation signal according to a preset binarization threshold to obtain a binary leakage magnetic enhancement estimation signal; calculating the initial length, width and inclination angle of the crack according to the edge profile of the binary leakage magnetic enhancement estimation signal; calculating the peak value of the leakage magnetic enhancement estimation signal, and using the calculated peak value as the initial depth value of the crack.

[0015] Optionally, the method further includes: under the excitation of virtual orthogonal twin DC excitation, taking the crack optimal scale intermediate variable as input, outputting a twin dynamic magnetic or eddy current response signal through dynamic magnetic or eddy current signal mapping.

[0016] Optionally, the crack signal orthogonal twin model is an autoencoder model implemented by a convolutional neural network, and the autoencoder model includes an encoder part and a decoder part.

[0017] An embodiment of the present disclosure also provides an orthogonal twinning device for oil and gas pipeline crack signals, comprising: a magnetic flux leakage sensor probe under unidirectional DC excitation conditions, a dynamic magnetic field or eddy current sensor probe under unidirectional DC excitation and dynamic magnetic field excitation conditions orthogonal to DC excitation, a memory for storing instructions, algorithms, and models, a processor for executing the orthogonal twinning method for oil and gas pipeline crack signals, and a bus system connecting the various units.

[0018] An embodiment of the present disclosure further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the orthogonal twinning method for oil and gas pipeline crack signals described in any embodiment of the present disclosure.

[0019] The crack signal orthogonal twinning method, device, and storage medium disclosed in this disclosure determine the objective existence of cracks by analyzing crack response signals based on various detection principles. When the signal-to-noise ratio of a crack signal is low, orthogonal twinning is performed on the crack signal to generate an enhanced signal corresponding to the crack. This enhanced crack signal will be useful for subsequent crack re-inspections in oil and gas pipelines, high-precision crack quantification, and crack profile reconstruction.

[0020] The orthogonal twinning method, detection device, and storage medium for oil and gas pipeline crack signals of the embodiments disclosed herein use an orthogonal twinning model of crack signals and, based on unidirectional DC excitation conditions, propose a new method and technology that can enhance the response signal of cracks at arbitrary inclination angles. This can enhance the response signal of cracks at arbitrary inclination angles, help improve the detection capability of tiny cracks, the accuracy of sizing quantification, and the accuracy of contour reconstruction, and thus has important practical significance for the safe operation of major infrastructure such as oil and gas pipelines, hydrogen pipelines, high-speed rails, and oil storage tanks.

[0021] Other features and advantages of the present disclosure will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present disclosure. Other advantages of the present disclosure can be realized and obtained through the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide an understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation to the technical solution of the present disclosure.

[0023] FIG1 is a schematic diagram of an ellipsoidal profile and orthogonal magnetic field of a crack in an oil and gas pipeline according to an exemplary embodiment of the present disclosure;

[0024] FIG2 is a flow chart of an orthogonal twinning method for crack signals according to an exemplary embodiment of the present disclosure;

[0025] FIG3A is a structural diagram of a crack signal orthogonal twinning method in a model training phase according to an exemplary embodiment of the present disclosure;

[0026] FIG3B is a structural diagram of an orthogonal twinning method for oil and gas pipeline crack signals in a model use phase according to an exemplary embodiment of the present disclosure;

[0027] FIG4A is a diagram of an exemplary embodiment of the present disclosure showing a "crack signal orthogonal twin model" (ML OT )’s loss function iteration process;

[0028] FIG4B is a diagram of an exemplary embodiment of the present disclosure showing a "crack signal orthogonal twin model" (ML OT )'s coefficient of determination iterative process;

[0029] FIG5A is a 3-D effect diagram of the magnetic flux leakage signal enhancement of a crack with a 0° tilt angle according to an exemplary embodiment of the present disclosure;

[0030] FIG5B is a top view showing the enhanced magnetic flux leakage signal of a crack with a 0° tilt angle according to an exemplary embodiment of the present disclosure;

[0031] FIG5C is a diagram showing the 3-D effect of dynamic magnetic field / eddy current signal enhancement on a crack with a 0° tilt angle according to an exemplary embodiment of the present disclosure;

[0032] FIG5D is a top view of the dynamic magnetic field / eddy current signal enhancement effect of a crack with a 0° tilt angle according to an exemplary embodiment of the present disclosure;

[0033] FIG6A is a 3-D effect diagram of the magnetic flux leakage signal enhancement of a crack with a 3° tilt angle according to an exemplary embodiment of the present disclosure;

[0034] FIG6B is a top view showing the enhanced magnetic flux leakage signal of a crack with a 3° tilt angle according to an exemplary embodiment of the present disclosure;

[0035] FIG6C is a diagram showing the 3-D effect of dynamic magnetic field / eddy current signal enhancement on a crack with a 3° tilt angle according to an exemplary embodiment of the present disclosure;

[0036] FIG6D is a top view of the dynamic magnetic field / eddy current signal enhancement effect of a crack with a 3° tilt angle according to an exemplary embodiment of the present disclosure;

[0037] FIG7A is a 3-D effect diagram of the magnetic flux leakage signal enhancement of a crack with an inclined angle of 18° according to an exemplary embodiment of the present disclosure;

[0038] FIG7B is a top view of the enhanced magnetic flux leakage signal of a crack with an inclined angle of 18° according to an exemplary embodiment of the present disclosure;

[0039] FIG7C is a diagram showing the 3-D effect of dynamic magnetic field / eddy current signal enhancement on a crack with an inclined angle of 18° according to an exemplary embodiment of the present disclosure;

[0040] FIG7D is a top view of the dynamic magnetic field / eddy current signal enhancement effect of an exemplary embodiment of the present disclosure on a crack with an inclined angle of 18°;

[0041] FIG8 is a schematic structural diagram of an orthogonal twinning device for oil and gas pipeline crack signals according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other in any manner.

[0043] Unless otherwise defined, the technical or scientific terms used in the embodiments of the present disclosure should have the ordinary meaning understood by people with ordinary skills in the field to which the present disclosure belongs. The words "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. The words "include" or "comprising" and similar words mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0044] The present disclosure proposes the concept of "orthogonal twinning", as shown in FIG1 , which aims to have only one direction of DC excitation H A Under the condition of , the virtual twin generates an excitation magnetic field H that is in the same plane, equal in size and perpendicular to the DC excitation magnetic field. ⊥ The leakage magnetic response signal generated. By jointly processing the original leakage magnetic signal and the twin leakage magnetic signal, the leakage magnetic enhancement estimation signal is obtained, and then the twin front and rear differential dynamic magnetic / eddy current signals are obtained using the leakage magnetic enhancement estimation signal. As shown in Figure 1, the inclination angle of the crack is defined as the angle between the long axis direction of the crack ellipsoid contour and the original excitation direction H. A The angle between them ranges from (-90°, 90°]. The orthogonal twinning method of crack signals disclosed in the present invention is applicable to crack signals with small inclination angles but is not limited to crack signals with small inclination angles. "Small inclination angle cracks" refer to cracks with an inclination angle of no more than 20° with the excitation direction as the reference. In the embodiment of the present invention, unidirectional DC excitation refers to a DC excitation method with a single direction, including but not limited to the existing axial DC excitation method, circumferential DC excitation method, and spiral DC excitation method.

[0045] As shown in Figure 1, a spatial rectangular coordinate system is established on the inner wall of the oil and gas pipeline. The geometric center of the crack ellipsoid outline is used as the coordinate origin, and the excitation direction (H A The direction perpendicular to the X-axis in the pipe wall plane is the Y-axis. The direction perpendicular to the pipe wall and pointing to the inside of the pipe is the Z-axis. The opposite direction of the Y-axis is the H-axis. ⊥ direction; in the plane of the pipe wall, the major axis direction of the crack ellipsoid outline is the X′ axis direction, and the inclination angle relative to the X axis direction is θ; the direction perpendicular to the X′ axis in the plane of the pipe wall is the Y′ axis direction, and its inclination angle relative to the Y axis direction is θ; the direction coinciding with the Z axis is the Z′ axis direction; the flow direction of oil and gas in the pipeline is the axial direction; the direction perpendicular to the axial direction and along the circumference of the pipe wall is the circumferential direction; the direction coinciding with the Z axis is the radial direction.

[0046] As shown in FIG2 , an embodiment of the present disclosure provides an orthogonal twinning method for oil and gas pipeline crack signals, comprising the following steps:

[0047] 201. Establish (train) an orthogonal twin model of oil and gas pipeline crack signals;

[0048] 202. Use (test) the orthogonal twin model of oil and gas pipeline crack signals.

[0049] The crack signal orthogonal twinning method of the embodiment of the present disclosure is described using oil and gas pipelines as an example. The implementation steps in other ferromagnetic metal materials can refer to this method and will not be repeated in this disclosure.

[0050] In some exemplary embodiments, referring to FIG. 3A , given the crack size and its response signal, step 201 may include the following steps:

[0051] 1) According to different detection principles, a weak matrix signal of crack response with a scale of (L, W, D, θ) is obtained under unidirectional DC excitation conditions. According to different detection principles, a 3-axis leakage magnetic field and a 2-axis dynamic magnetic field or eddy current sensor ultra-high resolution integrated probe is used to obtain a weak matrix signal of crack response with a scale of (L, W, D, θ) under unidirectional DC excitation conditions. For example, a DC excitation field H in direction A is obtained. A Three-axis magnetic flux leakage measurement signal modulus under the conditions Get the DC excitation magnetic field H in direction A A Dynamic magnetic or eddy current signal under conditions Where (L, W, D, θ) are the actual measured length, width, depth, and inclination angle of the crack, respectively; Respectively represent the X-axis component, Y-axis component and Z-axis component of the three-axis magnetic flux leakage measurement signal; They represent the dynamic magnetic or eddy current signals of the front and rear coil differential (hereinafter referred to as "front and rear differential") and the left and right coil differential (hereinafter referred to as "left and right differential") of the ultra-high resolution integrated probe circuit board of the 3-axis leakage magnetic field and the 2-axis dynamic magnetic or eddy current sensor respectively. A and D A All are matrix signals. At least include the indicator signal of crack existence, such as and The crack presence indication signal includes the boundary information of the crack. The signal can distinguish whether the crack is an inner wall crack (ID) or an outer wall crack (OD).

[0052] 2) In the same direction A, the DC excitation magnetic field H A Under these conditions, the inner wall crack magnetic dipole model or the outer wall crack magnetic dipole model is applied to generate the scale (L s ,W s ,D s ,θ s ) Simulated triaxial magnetic flux leakage signal modulus of virtual crack Among them, S A is the matrix signal, (L s,W s ,D s ,θ s ) are the length, width, depth and inclination angle of the virtual crack, which are intermediate variables. Their initial values ​​can be the corresponding known crack measured dimensions (L, W, D, θ); They are the X-axis component, Y-axis component and Z-axis component of the simulated three-axis magnetic leakage signal. If it is an inner wall crack, based on the "inner wall crack magnetic dipole model", the scale (L s ,W s ,D s ,θ s )calculate The formulas are (1) to (2); if it is an external wall crack, based on the "external wall crack magnetic dipole model", the scale (L s ,W s ,D s ,θ s )calculate The formulas are (3) to (5). Among them, f xX′ (·), f yX′ (·), f zX′ (·) are respectively in H A The three-axis (X′ axis, Y′ axis, Z′ axis) integral operator of the magnetic dipole under the excitation of the X′ axis component; f xY′ (·), f yY′ (·), f zY′ (·) are respectively in H A The three-axis (X′, Y′, and Z′) integral operator of the magnetic dipole excited by the Y′-axis component of μ0 = 4π×10 -7 (H / m), is the vacuum permeability, μ r is the relative magnetic permeability of the pipe wall under saturation conditions; σ x′ is the magnetic charge surface density of the magnetic dipole in the X′ axis direction, σ y′ is the surface density of the magnetic charge of the magnetic dipole in the Y′-axis direction; if it is an inner wall crack, h is the lift-off value of the sensor integrated probe; if it is an outer wall crack, h is the sum of the remaining wall thickness of the pipe at the crack and the lift-off value of the sensor integrated probe.

[0053] 3) In order to make the effect of crack simulation consistent with the effect of actual crack measurement, the objective function and constraint conditions are established: min||αS A -M A || F (6)

[0054] in,‖·‖ F is the Frobenius norm of the matrix, O s (L s ,W s ,D s ,θ s )=1 is the ellipsoid equation for fitting the crack contour; adjustment factor: α=max(M A ) / maxα(S A_initial ), equal to M A The maximum element of the matrix is ​​divided by the maximum element of the initial matrix, and the initial matrix (S A_initial ) is the simulated triaxial magnetic flux leakage signal modulus obtained by substituting the actual crack size (L, W, D, θ) into the magnetic dipole model. By adjusting the virtual crack size (L s ,W s ,D s ), so that the above objective function (6) is satisfied. The optimal virtual crack size that satisfies (6) is recorded as The corresponding simulated three-axis magnetic leakage signal modulus is recorded as In this paper, crack scale includes crack size and inclination angle.

[0055] Complex crack shapes can be decomposed into a combination of simple strip contours, and the strip contours can be abstracted into simple geometric shapes such as cuboids, ellipsoids, and elliptical cylinders.

[0056] In the method of the embodiment of the present disclosure, the crack contour is approximated as an ellipsoid. In other exemplary embodiments, the crack contour may also be approximated as a rectangular parallelepiped, an elliptical cylinder, etc., which is not limited in the embodiment of the present disclosure.

[0057] 4) Apply the inner wall crack orthogonal magnetic dipole model or the outer wall crack orthogonal magnetic dipole model to calculate the orthogonal twin triaxial magnetic leakage signal modulus. A Twin magnetic fields H in the same plane, equal in magnitude, and perpendicular in direction ⊥ Under the conditions of incentive (|H ⊥ |=|H A |), for scale The virtual crack is used to generate the corresponding magnetic flux leakage response signal modulus using the magnetic dipole model, that is, the simulated orthogonal twin triaxial magnetic flux leakage signal modulus in They are the X-axis component, Y-axis component and Z-axis component of the simulated orthogonal twin three-axis magnetic leakage signal. If it is an inner wall crack, based on the "inner wall crack orthogonal magnetic dipole model", the scale calculate The formula of is calculated as shown in formulas (10) to (12). Among them, σ xOT′ is the orthogonal magnetic charge surface density in the X'-axis direction of the magnetic dipole, and σ yOT′ is the orthogonal magnetic charge surface density in the Y'-axis direction of the magnetic dipole.

[0058] 5) For inner wall cracks and outer wall cracks, N orthogonal twin mapping pairs with different sizes and different inclination angles are obtained respectively. M A , αS Ao , αS ⊥ in the above steps 1) to 4) form a one-to-one correspondence. It is defined that the i-th group of orthogonal twin mapping pairs is expressed as Then, by changing the values of (L, W, D, θ), that is, replacing with a new measured crack, steps 1) to 4) are repeatedly executed until N orthogonal twin mapping pairs with different sizes and different inclination angles are obtained for inner wall cracks and outer wall cracks respectively, that is

[0059] 6) For inner wall cracks and outer wall cracks, the N groups of orthogonal twin mapping pairs are randomly split into a training set and a test set according to the ratio k:(1 - k) (0 < k < 1), and their numbers are k×N and (1 - k)×N respectively. In addition, for inner wall cracks and outer wall cracks, a part is randomly selected from the k×N training sets as the validation set during model training.

[0060] 7) For inner wall cracks and outer wall cracks, for the training set, with as the input and as the output, i = 1, 2,..., k×N, a machine learning model ML OT is built and trained, and it is named "orthogonal twin model for crack signals", including "orthogonal twin model for inner wall crack signals" and "orthogonal twin model for outer wall crack signals". Among them, represents performing a logical operation on and , for example K≥1; obviously, that is, the output magnetic leakage signal after orthogonal twin transformation is enhanced compared with the input magnetic leakage signal.

[0061] At this point, the "orthogonal twin model for crack signals" is trained.

[0062] In some exemplary embodiments, the use or testing of the "crack signal orthogonal twin model" is primarily applicable to a test set that has not undergone stage 1 model training or a new crack on a real oil and gas pipeline, where only the crack signal is obtained in advance and the geometric dimensions of the crack are unknown. Referring to FIG3B , step 202 may include the following steps:

[0063] 1) Based on different detection principles, the weak matrix signal of crack response under unidirectional DC excitation conditions is obtained.

[0064] For example, based on different detection principles, a 3-axis magnetic leakage and a 2-axis dynamic magnetic / eddy current sensor ultra-high resolution integrated probe are used to obtain a weak crack response signal with a scale of (L, W, D, θ) under unidirectional DC excitation conditions.

[0065] If we obtain a DC excitation magnetic field H in direction A A Three-axis magnetic flux leakage measurement signal modulus under the conditions Get the DC excitation magnetic field H in direction A A Dynamic magnetic / eddy current signal under conditions At least include indicators of crack presence, such as and The crack presence indication signal includes the boundary information of the crack. The signal can distinguish whether the crack is an inner wall crack (ID) or an outer wall crack (OD).

[0066] 2) For inner wall cracks or outer wall cracks, M A The signal is input to the ML trained in the first stage OT ("Inner wall crack signal orthogonal twin model" or "Outer wall crack signal orthogonal twin model"), output leakage magnetic enhancement estimation signal

[0067] 3) is the threshold value, Perform 0-1 binary processing and get c is between 0 and 1, and for example, c=0.5.

[0068] 4) Pass and Calculate the initial value of the crack size. The major axis of the edge profile is a, the minor axis is b, and the inclination angle of the major axis is The peak value of the crack is p, and the initial value of the crack size is

[0069] 5) (L s ,W s ,D s ,θ s) is substituted into the inner wall crack magnetic dipole model or the outer wall crack magnetic dipole model to calculate f(S A ,S ⊥ Specifically, (L s ,W s ,D s ,θ s ) is substituted into formulas (1) to (5) and (8) to (12) to obtain and Then calculate K≥1.

[0070] 6) Determine f(S A ,S ⊥ )relatively Is the error less than the threshold ε? The crack size at this time is recorded as the intermediate variable of the optimal crack size, which can be expressed as Then jump to step 7); otherwise, update (L s ,W s ,D s ,θ s ), then skip to step 5).

[0071] 7) In |H ⊥ |=|H A | incentive conditions, the scale is The dynamic magnetic / eddy current signal mapping is performed on the crack, and the orthogonal twin front and rear differential dynamic magnetic / eddy current response signals are output. The methods for dynamic magnetic / eddy current signal mapping include but are not limited to electromagnetic simulation method, ARMA model method with trend term, mathematical analysis method, dynamic magnetic signal waveform fitting method and other existing methods. Taking electromagnetic simulation method as an example, a scale of The crack model is constructed and the magnetic circuit and circuit environment are built. ⊥ |=|H A | Under the condition of excitation, let the simulation software automatically output the front and rear differential dynamic magnetic / eddy current response signals of the orthogonal twin according to the finite element method

[0072] 8) Repeat steps 1) to 7) until all the inner / outer wall crack signals of the test set in the first phase or the new crack signals on the real oil and gas pipeline, including the leakage magnetic signal and the front and rear differential dynamic magnetic / eddy current signals, are orthogonally twinned.

[0073] In step 6), a nested heuristic optimization algorithm is recommended to speed up the solution of the optimal crack scale intermediate variable. Heuristic optimization algorithms include, but are not limited to, genetic algorithms, particle swarm optimization algorithms, ant colony optimization algorithms, artificial immune algorithms, and their improved versions (existing technologies). The following uses the particle swarm optimization (PSO) algorithm as an example to describe the steps of embedding the PSO algorithm in step 6):

[0074] S1, initialization parameters. Define the number of particles N P , particle dimension dim=4 (because scale (L s ,W s ,D s ,θ s ) has 4 parameters), the maximum number of iterations maxIter, the inertia weight coefficient ω, the acceleration constants c1 and c2, the random coefficients r1 and r2; the particle position matrix Velocity matrix v d =zeros(N P ,dim), the optimal position matrix p of individual particles best =zeros(N P ,dim), particle swarm optimal position matrix g best =zeros(1,dim), the optimal fitness of individual particles p fit =zeros(N P ,1), the global optimal fitness value of the particle g fit =10 10 ; Among them, ones(X,Y) means generating a matrix with all 1s in X rows and Y columns, and zeros(X,Y) means generating a matrix with all 0s in X rows and Y columns.

[0075] S2. Initialize the population. Calculate v d is (N P ,dim)-dimensional random number matrix between 0 and 1; p best =x d ; When j=1,…,N P When x d Each line Substitute the inner wall crack magnetic dipole model or the outer wall crack magnetic dipole model for calculation Then p fit The j-th row element of Then p fit The smallest element in the column vector is assigned to g fit , and the smallest element corresponds to Assign to g best .

[0076] S3. Calculate the fitness of each particle. When j=1,…,N P When xd Each line Substitute the inner wall crack magnetic dipole model or the outer wall crack magnetic dipole model for calculation Then the fitness of the jth particle is expressed as

[0077] S4, update the individual optimum and group optimum according to the fitness value of the particle. P When Δ j <p fit (j), then p fit (j) Use Δ j Instead, and If p fit (j) <g fit ,but And g fit =p fit (j).

[0078] S5, update the speed and position of each particle. P When , the following formula (13) and formula (14) are used to update the speed and position of each particle in turn. d (j) = ω·v d (j)+c1·r1·(p best (j)-x d (j))+c2·r2·(g best (j)-x d (j)) (13) x d (j) = x d (j)+v d (j) (14)

[0079] S6. Determine whether the convergence condition is met. If the maximum number of iterations maxIter is reached, or if g is satisfied fit <ε, then the crack size is Then jump to step 7 of the second stage (using the "crack signal orthogonal twin model") and end the particle swarm optimization algorithm; otherwise jump to step S3.

[0080] In order to better understand the orthogonal twinning method of oil and gas pipeline crack signal provided by the present disclosure, the following (X80 steel, 15.30 mm wall thickness) The exemplary embodiment of orthogonal twinning of oil and gas pipeline crack signals further illustrates the technical solution of the present disclosure.

[0081] This embodiment uses an ultra-high-resolution integrated probe based on 3-axis magnetic flux leakage and 2-axis dynamic magnetic / eddy current sensors to collect weak crack signals. 3-axis magnetic flux leakage refers to the axial, radial, and circumferential components of the leakage magnetic field; 2-axis dynamic magnetic / eddy current refers to the ability of the probe circuit board to simultaneously collect front-to-back differential dynamic magnetic / eddy current signals and left-to-right differential dynamic magnetic / eddy current signals. The front-to-back differential dynamic magnetic / eddy current signals can detect and identify internal and external cracks in a 360° omnidirectional manner. However, as the crack tilt angle modulus decreases, the amplitude of the front-to-back differential dynamic magnetic / eddy current signals gradually decreases; whereas the amplitude of the left-to-right differential dynamic magnetic / eddy current signals increases as the crack tilt angle modulus decreases. The two complement each other and make up for each other's shortcomings. Therefore, using an ultra-high-resolution integrated probe with 3-axis magnetic flux leakage and 2-axis dynamic magnetic / eddy current sensors can determine whether a crack exists, distinguish internal from external cracks, and identify suspected crack areas.

[0082] The entire implementation process is divided into two stages. The first stage is the establishment stage of the "crack signal orthogonal twin model" (model training stage); the second stage is the use stage of the "crack signal orthogonal twin model" (model testing stage).

[0083] In the first stage, the crack size and its response signal are known, and the steps to establish the "crack signal orthogonal twin model" are as follows:

[0084] According to step 1), the 3-axis magnetic flux leakage detector and the 2-axis dynamic magnetic field / eddy current detector of the axial excitation method are used to carry out the A pulling experiment was conducted on a pipe with artificial cracks (X80 steel, 15.30mm wall thickness), thereby obtaining the pulling data of the artificial cracks. The use of axial excitation means that the excitation direction (X-axis direction) coincides with the axial direction, and the established coordinate system is as shown in Figure 1. First, 2160 artificial cracks of different sizes and different inclination angles were processed on the pulling pipe as samples for model training, including 1080 inner wall cracks (ID) and 1080 outer wall cracks (OD); for the inner wall cracks and outer wall cracks, 75% of the samples (810) were randomly assigned as the training set (marked by train), and the other 25% of the samples (270) were assigned as the test set (marked by test). 25% of the samples (203) randomly selected from the training set were defined as the validation set (marked by val), and their scale distribution ranges are shown in Table 1. Import the above pulling data into the "Crack Original Sample Calibration Software", select and export the crack original sample data file in the known area of ​​the machined crack, and save it in txt format. Each data file is named with the measured crack "length (L) - width (W) - depth (D) - tilt angle (θ) - inside / outside", thereby obtaining the cracks of different scales (L) under axial excitation conditions. i ,W i ,D i ,θ i ) crack response weak signal as well as i=1,2,…,2160. Each crack original sample data file consists of 5 matrix data, namely, leakage magnetic axial data matrix Magnetic Flux Leakage Radial Data Matrix Magnetic Flux Leakage Toroidal Data Matrix Dynamic magnetic / eddy current before and after differential data matrix and dynamic magnetic / eddy current left and right differential data matrix and, For inner wall cracks or outer wall cracks, 810 samples in the training set are prepared for the first stage training of the "crack signal orthogonal twin model", and 270 samples in the test set are prepared for the second stage testing of the "crack signal orthogonal twin model".

[0085] Table 1 Sample distribution of model training

[0086] According to step 2), the axial excitation field H of the pulling experiment is measured in advance. A Then, under the same axial excitation field conditions, for the inner wall crack and the outer wall crack, 1080 artificial crack samples of different sizes and different inclination angles were measured with the measured scale recorded in their file names. As the initial value, the magnetic dipole model is applied to generate the simulated triaxial magnetic leakage signal modulus of the virtual crack i=1,2,…,1080; if it is an inner wall crack, based on the “inner wall crack magnetic dipole model”, the scale calculate The formulas are (15) to (16); if it is an external wall crack, based on the “external wall crack magnetic dipole model”, the scale calculate The formulas are (17) to (19). Among them, f xX′ (·), f yX′ (·), f zX′ (·) are respectively in H A The three-axis (X′ axis, Y′ axis, Z′ axis) integral operator of the magnetic dipole under the excitation of the X′ axis component; f xY′ (·), f yY′ (·), f zY′ (·) are respectively in H A The three-axis (X′, Y′, and Z′) integral operator of the magnetic dipole excited by the Y′-axis component of μ0 = 4π×10 -7 (H / m), is the vacuum permeability, μ r is the relative magnetic permeability of the pipe wall under saturation conditions; is the magnetic charge surface density of the magnetic dipole in the X′ axis direction corresponding to the i-th artificial crack sample, is the magnetic charge surface density of the magnetic dipole in the Y′ axis direction corresponding to the i-th artificial crack sample; if it is an inner wall crack, h i is the lift-off value of the sensor integrated probe at the i-th artificial crack sample. If it is an outer wall crack, h i It is the sum of the remaining wall thickness of the pipe at the i-th artificial crack sample and the lift-off value of the sensor integrated probe.

[0087] According to step 3), in order to make the effect of crack simulation consistent with the actual crack measurement effect, for the inner wall crack and outer wall crack, 1080 artificial crack samples of different sizes and different inclination angles were respectively established, and the objective function and constraint conditions were established one by one:

[0088] Among them, when i=1,2,…,1080, is the ellipsoid equation of the fitting crack profile corresponding to the i-th artificial crack sample; adjustment factor: express The maximum element of the matrix is ​​divided by the maximum element of the i-th initial matrix, and the i-th initial matrix That is, the measured crack size (L i ,W i ,D i ,θ i ) is substituted into the simulated three-axis magnetic leakage signal modulus calculated by the magnetic dipole model. i ,W i ,D i ,θ i ) is the initial value and the virtual crack size is adjusted The optimal virtual crack size that satisfies (20) is denoted as The corresponding simulated three-axis magnetic flux leakage signal is recorded as

[0089] According to step 4), the orthogonal magnetic dipole model of the inner wall crack and the orthogonal magnetic dipole model of the outer wall crack are used to calculate the orthogonal twin triaxial magnetic leakage signal modulus of 2160 artificial crack samples with different sizes and different inclination angles. A Twin magnetic fields H in the same plane, equal in magnitude, and perpendicular in direction ⊥ Under the conditions of incentive (|H ⊥ |=|H A|), for inner wall cracks or outer wall cracks, when i=1,2,…,1080, for scale The virtual crack is used to generate the corresponding magnetic flux leakage response signal modulus using the magnetic dipole model, that is, the simulated orthogonal twin triaxial magnetic flux leakage signal modulus If it is an inner wall crack, based on the "inner wall crack orthogonal magnetic dipole model", the scale calculate The formulas are shown in (22) to (23); if it is an external wall crack, based on the “external wall crack orthogonal magnetic dipole model”, the scale calculate The formulas are shown in (24) to (26). is the surface density of the orthogonal magnetic charge of the magnetic dipole in the X′ axis direction corresponding to the i-th artificial crack sample, is the surface density of the magnetic charge orthogonal to the Y′ axis of the magnetic dipole corresponding to the i-th artificial crack sample.

[0090] According to step 5), for the inner wall crack and the outer wall crack, 1080 orthogonal twin mapping pairs of different sizes and different inclination angles are obtained respectively. In the above steps 1) to 4), for the inner wall crack or the outer wall crack, There are 1080 orthogonal twin mapping pairs of different sizes and tilt angles.

[0091] According to step 6), for inner wall cracks and outer wall cracks, the ratio coefficient of splitting the training set and the test set in this embodiment is set to k=0.75, that is, the number of training set samples accounts for 75% of the total number of samples; and the proportion of the validation set in the training set is 25%.

[0092] According to step 7), for the inner wall crack and outer wall crack, for the training set samples (810), As input, For output, i = 1, 2, ..., 810, build and train the machine learning model ML OT , namely the "orthogonal twin model of crack signal", including the "orthogonal twin model of inner wall crack signal" and the "orthogonal twin model of outer wall crack signal". This embodiment specifically uses the convolutional neural network autoencoder (CNN Autoencoder) algorithm to build the orthogonal twin model of crack signal. The model requires that when i = 1, 2, ..., 810, the input For a 200×200 matrix, the output The matrix is ​​also 200×200, with a physical step of 0.5 mm between two points. If the physical step exceeds 0.5 mm, cubic spline interpolation can be used. If the matrix dimensions do not meet the requirements, the matrix center is used as the matrix center, and edges exceeding 200 dimensions are pruned, and edges less than 200 dimensions are padded with zeros.

[0093] The convolutional neural network autoencoder designed in this embodiment has a total of 37 layers, of which the encoding part contains 18 layers and the decoding part contains 19 layers. The network architecture built using Python is as follows:

[0094] In the above Python code, Conv2D represents a two-dimensional convolutional layer, SpatialDropout2D represents a two-dimensional spatial random zeroing layer, BatchNormalization represents a batch normalization layer, ReLU represents an activation function layer, MaxPooling2D represents a two-dimensional pooling layer, UpSampling2D represents a two-dimensional upsampling layer, and Activation represents an activation layer. Each line of Python represents a layer, and the output of each layer serves as the input to the next layer, which in turn serves as the input to the next layer.

[0095] Taking the “Orthogonal Twin Model of Inner Wall Crack Signal” as an example, the number of iteration steps of model training is set to 500. The iteration process of model training is shown in Figures 4A and 4B. Figure 4A is the “Orthogonal Twin Model of Crack Signal” (ML OT ) loss function iteration process, Figure 4B is the “crack signal orthogonal twin model” (ML OT ) coefficient of determination (R 2 ) iterative process, the iteration ends "crack signal orthogonal twin model" (ML OT ) are shown in Table 2. OT After the model converged, the loss function of the training set and the validation set was finally reduced to 0.0002, indicating that ML OT The final mean square error (MSE) of the model is very small and there is no overfitting. On the other hand, ML OT The coefficient of determination of the model training set finally reached 0.9371, and the coefficient of determination of the validation set finally reached 0.9281, indicating that ML OT The final fitting effect of the model is good and basically meets the requirements of orthogonal twin transformation.

[0096] Table 2 The “crack signal orthogonal twin model” (MLOT )

[0097] In the second stage, the model was tested on 270 test set samples that were not trained in the first stage for inner wall cracks or outer wall cracks. Only crack signals were obtained in advance, but the geometric scale of the cracks was unknown. The model application (testing) steps of the nested particle swarm optimization (PSO) algorithm are as follows:

[0098] According to step 1), the weak matrix signal of crack response under unidirectional DC excitation is obtained according to different detection principles. After completing step 1) of the first stage, the weak matrix signal of crack response of 270 test set samples has been obtained under axial excitation conditions for inner wall cracks or outer wall cracks. as well as i=1,2,…,270. Each crack original sample data file consists of 5 matrix data, namely, leakage magnetic axial data matrix Magnetic Flux Leakage Radial Data Matrix Magnetic Flux Leakage Toroidal Data Matrix Dynamic magnetic or eddy current before and after differential data matrix and dynamic magnetic / eddy current left and right differential data matrix

[0099] According to step 2), for inner wall cracks or outer wall cracks, for the test set samples (270), when i = 1, 2, ..., 270, Signal input to ML OT ("Inner wall crack signal orthogonal twin model" or "Outer wall crack signal orthogonal twin model"), output Signal.

[0100] According to step 3), for inner wall cracks or outer wall cracks, for the test set samples (270), when i = 1, 2, ..., 270, is the threshold value, Perform 0-1 binary processing and get

[0101] According to step 4), for inner wall cracks or outer wall cracks, for the test set samples (270), when i = 1, 2, ..., 270, by and Calculate the initial value of crack size. The edge contour is usually an ellipse. The major axis of the edge profile is a i , the minor axis is b i , the inclination angle of the long axis is The peak value is p i; When i=1,2,…,270, the initial value of the crack size is

[0102] According to step 5), for inner wall cracks or outer wall cracks, when i=1,2,…,270, Substitute the inner wall crack magnetic dipole model or the outer wall crack magnetic dipole model for calculation Specifically, Substituting into formulas (1) to (5) and (8) to (12) we can calculate and Then calculate K≥1.

[0103] According to step 6), for inner wall cracks or outer wall cracks, when i = 1, 2, ..., 270, carry out the following sub-steps:

[0104] According to step S1, initialize the parameters. For the i-th test set sample, define the number of particles N P =30, particle dimension dim=4, maximum number of iterations maxIter=100, inertia weight coefficient ω=0.8, acceleration constant c1=c2=2, random coefficients r1=0.6 and r2=0.3; particle position matrix Velocity matrix v d_i =zeros(30,4), the optimal position matrix p of individual particles best_i =zeros(30,4), particle swarm optimal position matrix g best_i =zeros(1,4), the optimal fitness of individual particles p fit_i =zeros(30,1), the global optimal fitness value of the particle g fit_i =10 10 ; Among them, ones(X,Y) means generating a matrix with all 1s in X rows and Y columns, and zeros(X,Y) means generating a matrix with all 0s in X rows and Y columns.

[0105] According to step S2, initialize the population. For the i-th test set sample, calculate v d_i is (N P ,dim)-dimensional random number matrix between 0 and 1; p best_i =x d_i ; When j=1,…,N P When x d_i Each line Substitute into the magnetic dipole model to calculate Then p fit_i The j-th row element of Then pfit_i The smallest element in the column vector is assigned to g fit_i , and the smallest element corresponds to Assign to g best_i .

[0106] According to step S3, calculate the fitness of each particle. For the i-th test set sample, when j=1,…,N P When x d_i Each line Substitute into the magnetic dipole model to calculate Then the fitness of the jth particle of the i-th test set sample is expressed as

[0107] According to step S4, the individual optimum and the group optimum are updated according to the fitness value of the particle. P When Δ ij <p fit_i (j), then p fit_i (j) Use Δ ij Instead, and If p fit_i (j) <g fit_i ,but And g fit_i =p fit_i (j).

[0108] According to step S5, update the velocity and position of each particle. For the i-th test set sample, when j=1,…,N P When , the following formula (27) and formula (28) are used to update the speed and position of each particle in turn. d_i (j) = ω·v d_i (j)+c1·r1·(p best_i (j)-x d_i (j))+c2·r2·(g best_i (j)-x d_i (j)) (27) x d_i (j) = x d_i (j)+v d_i (j) (28)

[0109] According to step S6, it is determined whether the convergence condition is met. For the i-th test set sample, if the maximum number of iterations maxIter = 100 is reached, or if g fit_i <ε, then the crack size is Then jump to step 7); otherwise jump to step S3.

[0110] According to step 7), electromagnetic simulation method is used to establish the scale of inner wall crack or outer wall crack in ANSYS software when i=1,2,…,270. The crack model is constructed and the magnetic circuit and circuit environment are built. ⊥ |=|H A | Under the condition of excitation, let the simulation software automatically output the front and rear differential dynamic magnetic / eddy current response signals of the orthogonal twin according to the finite element method

[0111] According to step 8), steps 1) to 7) are repeated until all the inner / outer wall crack magnetic flux leakage signals and front and rear differential dynamic magnetic / eddy current signals of the 270 test sets in the first phase are orthogonally twinned.

[0112] Figures 5A to 5D, 6A to 6D, and 7A to 7D are orthogonal twinning effect diagrams of three groups of inner wall crack signals of different scales in the test set samples. The three figures from left to right in Figures 5A, 5B, 6A, 6B, 7A, and 7B are the original input magnetic flux leakage measurement signal modulus M A , the picture title is "Input Signal"; after ML OT Output leakage magnetic enhancement estimation signal The image is titled "Predicting Output Signals"; and ML OT The ideal output signal f(αS Ao ,αS ⊥ ), the image title is “Real Output Signal.”

[0113] The two figures from left to right in Figures 5C, 5D, 6C, 6D, 7C and 7D are the original input front and back differential moving magnetic field / eddy current signals. The image is titled "Raw differential moving magnetic field / eddy current signals" and the front and rear differential moving magnetic field / eddy current signals output by orthogonal twinning. The image is titled "Front and rear differential moving magnetic / eddy current enhanced signals."

[0114] Figures 5A to 5D show the enhanced signals obtained by orthogonal twinning of an inner wall crack with L = 30 mm, W = 0.3 mm, D = 1.4 mm, and θ = 0° in the test set. A The signal passes through ML OT (“Inner wall crack signal orthogonal twin model”) Signal enhancement effect diagram. It can be seen that the original input signal M A The leakage magnetic field peak is only 10Gs. After orthogonal twin transformation, ML OTThe peak value of the leakage magnetic field of the output signal reaches 120Gs, and the signal is enhanced by 12 times. In addition, since the inclination angle θ of the crack sample is 0°, it is an axial crack. Figure 5B shows that the original input signal M A It shows a double peak feature, and it is even difficult to distinguish it as an axial crack. However, after orthogonal twin transformation, ML OT Output signal It clearly reflects its axial crack characteristics, so The signal provides favorable conditions for the subsequent scale quantification and contour reconstruction of the axial crack. Figure 5C and Figure 5D are the differential dynamic magnetic / eddy current signals before and after the axial crack. The signal enhancement effect of the axial crack is shown in Figure 2. The signal-to-noise ratio (SNR) is low and even drowned in the background noise; The peak-to-peak value of the signal reached 120mV, and the SNR was improved by about 10dB. The signal characteristics of the axial crack can be clearly seen from the top-down effect diagram, which provides strong support for further improving the scale quantification and shape reconstruction accuracy of the axial crack.

[0115] Figures 6A to 6D show the enhanced signals obtained by orthogonal twinning of an inner wall crack with L = 40 mm, W = 0.3 mm, D = 2.5 mm, and θ = 3° in the test set. A The signal passes through ML OT (“Inner wall crack signal orthogonal twin model”) Signal enhancement effect diagram. It can be seen that the original input signal M A The leakage magnetic field peak is only 12Gs. After orthogonal twin transformation, ML OT The leakage magnetic field peak of the output signal reaches 150Gs, and the signal is enhanced by 12.5 times. Similarly, since the inclination angle of the crack sample is θ = 3°, it belongs to a small inclination angle crack. Figure 6B shows that after the orthogonal twin transformation, the ML OT Output signal The crack signal characteristics are enhanced, creating favorable conditions for high-precision quantification of its scale. Figures 6C and 6D are the differential dynamic magnetic / eddy current signals before and after the crack. The signal enhancement effect of the crack is obvious. The signal-to-noise ratio (SNR) is low and even drowned in the background noise; The peak-to-peak value of the signal reached 120mV, and the SNR was improved by about 15dB. The slight tilt feature of the crack signal can be clearly seen from the top-down effect diagram, which lays the foundation for further improving the scale quantification and shape reconstruction accuracy of the crack.

[0116] Figures 7A to 7D show the enhanced signals obtained by orthogonal twinning of an inner wall crack with L = 60 mm, W = 0.4 mm, D = 2.9 mm, and θ = 18° in the test set. A The signal passes through ML OT (“Inner wall crack signal orthogonal twin model”) Signal enhancement effect diagram. It can be seen that the original input signal M A The leakage magnetic field peak is only 60Gs. After orthogonal twin transformation, ML OT The peak value of the leakage magnetic field of the output signal reaches 200Gs, and the signal is enhanced by 3.3 times, so ML OT The output signal is also conducive to the high-precision quantification of the crack. Figure 7C and Figure 7D are the differential dynamic magnetic / eddy current signals before and after the crack. The signal enhancement effect of the crack is obvious. The signal-to-noise ratio (SNR) is low and even drowned in the background noise; The peak-to-peak value of the signal reached 170mV, and the SNR was improved by about 13dB. The tilt feature of the crack signal can be seen from the top-down effect diagram, which helps to further improve the scale quantification and shape reconstruction accuracy of the crack.

[0117] The disclosed embodiments also provide an orthogonal twinning device for oil and gas pipeline crack signals, comprising: 1) an ultra-high-resolution integrated probe of 3-axis magnetic leakage and 2-axis dynamic magnetic / eddy current sensors, capable of collecting front and rear differential dynamic magnetic / eddy current signals and left and right coil differential dynamic magnetic / eddy current signals; 2) a memory for storing instructions, algorithms, and models; 3) a processor for executing the orthogonal twinning method for oil and gas pipeline crack signals; 4) a display for displaying signal enhancement results; and 5) a bus system for connecting various units.

[0118] In one example, as shown in FIG8 , an orthogonal twinning device for oil and gas pipeline crack signals may include: an ultra-high resolution integrated probe 810 of a 3-axis magnetic flux leakage and 2-axis dynamic magnetic / eddy current sensor, a memory 820 , a processor 830 , a display 840 , and a bus system 850 , wherein the ultra-high resolution integrated probe 810 of the 3-axis magnetic flux leakage and 2-axis dynamic magnetic / eddy current sensor, the memory 820 , the processor 830 , and the display 840 are connected via the bus system 850 ; the ultra-high resolution integrated probe 810 of the 3-axis magnetic flux leakage and 2-axis dynamic magnetic / eddy current sensor is used to detect and obtain a weak crack response signal with a scale of (L, W, D, θ) under unidirectional DC excitation conditions, including a 3-axis magnetic flux leakage signal and front and rear differential dynamic magnetic / eddy current signals and left and right differential dynamic magnetic / eddy current signals; the memory 820 is used to store instructions and a “crack signal orthogonal twinning model”, etc., and the processor 830 is used to execute the instructions stored in the memory 820 to perform signal orthogonal twinning through the orthogonal twinning method for oil and gas pipeline crack signals. Specifically, the processor 830 can train the "crack signal orthogonal twin model"; can enhance weak crack signals at different inclination angles; and finally display the enhanced results through the display 840.

[0119] It should be understood that memory 820 may include read-only memory and random access memory, and provides instructions and data, including the aforementioned "crack signal orthogonal twin model," to processor 830. A portion of memory 820 may also include non-volatile random access memory. For example, memory 820 may also store device type information.

[0120] The processor 830 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor, or the processor 830 may be any conventional processor.

[0121] In addition to displaying the signal enhancement results, the display 840 can also display the crack detection data in the memory 820 through the crack analysis software.

[0122] In addition to the data bus, the bus system 850 may also include a power bus, a control bus, a status signal bus, and the like.

[0123] During implementation, the processing performed by an orthogonal twin device for crack signals in an oil and gas pipeline can be completed by hardware integrated logic circuits or software instructions in the processor 830. That is, the steps of an orthogonal twin device for crack signals in an oil and gas pipeline according to an embodiment of the present disclosure can be performed by a hardware processor or by a combination of hardware and software modules in the processor 830. The software module can be located in a storage medium such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 820, and the processor 830 reads the information in the memory 820 and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0124] An embodiment of the present disclosure also provides a storage medium storing executable instructions. When the executable instructions are executed by a processor, the orthogonal twinning method of oil and gas pipeline crack signals provided by any of the above embodiments of the present disclosure can be implemented; in addition, the storage medium can also store a "crack signal orthogonal twinning model."

[0125] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0126] Although the embodiments disclosed in this disclosure are as described above, the contents described are merely embodiments adopted to facilitate understanding of the disclosure and are not intended to limit the disclosure. Any person skilled in the art to which the disclosure belongs may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope of the disclosure. However, the scope of protection of the disclosure shall still be based on the scope defined by the appended claims.

Claims

1. An orthogonal twinning method for oil and gas pipeline crack signals, comprising: Obtain the modulus of the three-axis magnetic leakage measurement signal of the inner wall crack or the outer wall crack under the condition of unidirectional DC excitation, and obtain the dynamic magnetic signal or eddy current signal under the condition of dynamic magnetic field excitation orthogonal to the DC excitation, wherein the unidirectional DC excitation refers to a DC excitation mode with a single direction; Input the modulus of the three-axis magnetic leakage measurement signal into the orthogonal twin model of the crack signal to obtain a magnetic leakage enhancement estimation signal, wherein the magnetic leakage enhancement estimation signal is a function of the modulus of the simulated orthogonal twin three-axis magnetic leakage signal, and the modulus of the orthogonal twin three-axis magnetic leakage signal is the modulus of the three-axis magnetic leakage response signal under the condition of virtual orthogonal twin DC excitation, and the virtual orthogonal twin DC excitation is in the same plane as the unidirectional DC excitation, is equal in size and perpendicular in direction; The orthogonal twin is defined as, under the condition that the original response signal of unidirectional DC excitation is known, a response signal corresponding to a twin magnetic field that is in the same plane, equal in size, and perpendicular to the unidirectional DC excitation is obtained. The obtained response signal is defined as a twin response signal. The twin response signal and the original response signal form a twin relationship. The process of transforming the original response signal into the twin response signal is defined as an orthogonal twin transformation.

2. The method according to claim 1, wherein: The leakage magnetic enhancement estimation signal is the simulated optimal three-axis leakage magnetic signal modulus S Ao Compared with the simulated orthogonal twin three-axis magnetic leakage signal modulus S ⊥ The function f(αS Ao ,αS ⊥ ), the simulated optimal three-axis magnetic leakage signal modulus S Ao Is to make ||αS A -M A || F The minimum simulated triaxial magnetic flux leakage signal modulus, M A is the modulus of the three-axis magnetic flux leakage measurement signal, S A Substituting the crack size into the magnetic dipole model to obtain the simulated triaxial magnetic flux leakage signal modulus, ‖·‖ F is the Frobenius norm of the matrix, α is the adjustment factor, where: They are respectively the X-axis component, Y-axis component and Z-axis component of the simulated three-axis magnetic leakage signal, the X-axis direction is the same as the excitation direction of the unidirectional DC excitation, the Y-axis direction is perpendicular to the X-axis direction in the plane of the oil and gas pipeline, and the Z-axis direction is perpendicular to the X-axis direction and the Y-axis direction respectively; They are the X-axis component, Y-axis component and Z-axis component of the orthogonal twin three-axis magnetic flux leakage signal; The adjustment factor α is equal to M A The maximum element of the matrix is ​​divided by the maximum element of the initial matrix, where the initial matrix is ​​a matrix composed of the simulated triaxial magnetic flux leakage signal modulus values ​​calculated by substituting the measured crack dimensions into the magnetic dipole model.

3. The method according to claim 1, further comprising: For multiple crack samples, the crack signal orthogonal twin model is trained using the modulus of the three-axis magnetic flux leakage measurement signal under unidirectional DC excitation and the corresponding magnetic flux leakage enhancement estimation signal, wherein the magnetic flux leakage enhancement estimation signal corresponding to each crack sample is generated by the following method: Taking the measured scale of the crack sample as the initial crack scale; Use M A The maximum element of the matrix is ​​divided by the maximum element of the initial matrix to obtain the adjustment factor α, M A is the modulus of the three-axis magnetic flux leakage measurement signal; Calculate ||αS A -M A || F , where S A Substituting the crack size into the magnetic dipole model to obtain the simulated triaxial magnetic flux leakage signal modulus, ‖·‖ F is the Frobenius norm of the matrix; The crack size is repeatedly adjusted and input into the crack magnetic dipole model until the ||αS A -M A || F The minimum optimal crack size, and the simulated triaxial magnetic flux leakage signal modulus corresponding to the optimal crack size is the simulated optimal triaxial magnetic flux leakage signal modulus S Ao ; The optimal crack size is input into the crack orthogonal magnetic dipole model to obtain the simulated orthogonal twin triaxial magnetic leakage signal modulus S ⊥ ; The S Ao With S ⊥ Substitute into the formula K≥1, thereby generating the leakage magnetic field enhancement estimation signal.

4. The method according to claim 3, wherein: For the plurality of crack samples, the orthogonal twin model of the crack signal is trained using the modulus of the three-axis magnetic flux leakage measurement signal under the unidirectional DC excitation condition and the corresponding magnetic flux leakage enhancement estimation signal, including: The modulus of the triaxial magnetic flux leakage measurement signal of each crack sample under the condition of unidirectional DC excitation and the corresponding magnetic flux leakage enhancement estimation signal are marked as a set of orthogonal twin mapping pairs; The N sets of orthogonal twin mapping pairs are randomly split into training sets and test sets according to the ratio k:(1-k), where 0 <k<1; The orthogonal twin model of the crack signal is built and trained using k×N groups of the orthogonal twin mapping pairs, and the orthogonal twin model of the crack signal is tested using (1-k)×N groups of the orthogonal twin mapping pairs.

5. The method according to claim 1, further comprising: Calculating the optimal scale intermediate variable of the crack according to the leakage magnetic enhancement estimation signal; The crack scale is input into the crack magnetic dipole model and the crack orthogonal magnetic dipole model respectively to obtain the simulated triaxial magnetic leakage signal modulus and the simulated orthogonal twin triaxial magnetic leakage signal modulus; Calculate the secondary magnetic flux leakage enhancement estimation signal according to the obtained simulated three-axis magnetic flux leakage signal modulus value and the simulated orthogonal twin three-axis magnetic flux leakage signal modulus value; Detecting whether the Frobenius norm of the difference between the magnetic flux leakage enhancement estimation signal and the secondary magnetic flux leakage enhancement estimation signal is less than a preset threshold; When the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal is less than a preset threshold, the input crack scale is used as an intermediate variable of the optimal crack scale; When the Frobenius norm of the difference between the leakage magnetic enhancement estimation signal and the secondary leakage magnetic enhancement estimation signal is greater than or equal to a preset threshold, the input crack scale is adjusted, and the step of inputting the crack scale into the crack magnetic dipole model and the crack orthogonal magnetic dipole model is returned to continue execution.

6. The method according to claim 5, wherein: Calculating the initial value of the crack size according to the leakage magnetic enhancement estimation signal includes: According to a preset binarization threshold, binarization processing is performed on the leakage magnetic enhancement estimation signal to obtain a binarized leakage magnetic enhancement estimation signal; Calculating the initial value of the length, the initial value of the width and the initial value of the inclination angle of the crack according to the edge profile of the binary magnetic leakage enhancement estimation signal; The peak value of the leakage magnetic enhancement estimation signal is calculated, and the calculated peak value is used as the initial value of the crack depth.

7. The method according to claim 5, further comprising: Under the excitation of virtual orthogonal twin DC excitation, the crack optimal scale intermediate variable is used as input, and through dynamic magnetic or eddy current signal mapping, a twin dynamic magnetic or eddy current response signal is output.

8. The method according to claim 1, wherein: The crack signal orthogonal twin model is an autoencoder model implemented by a convolutional neural network, and the autoencoder model includes an encoder part and a decoder part.

9. An orthogonal twinning device for oil and gas pipeline crack signals, comprising: A magnetic flux leakage sensor probe under unidirectional DC excitation conditions, a dynamic magnetic field or eddy current sensor probe under unidirectional DC excitation and dynamic magnetic field excitation conditions orthogonal to DC excitation, a memory for storing instructions, algorithms, and models, a processor for executing the orthogonal twinning method for oil and gas pipeline crack signals as described in any one of claims 1 to 8, and a bus system connecting the various units.

10. A storage medium having stored thereon a program of an orthogonal twinning method for oil and gas pipeline crack signals, wherein when the program is executed by a processor, the orthogonal twinning method for oil and gas pipeline crack signals as claimed in any one of claims 1 to 8 is implemented.

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