Cable nondestructive testing probe design method and system based on tomography

Abstract

The invention relates to a method and a system for designing a cable nondestructive testing probe based on tomography. The method comprises the following steps: firstly, establishing a finite element simulation model of a multi-layer cable structure, annularly configuring a coil sensor array at the periphery of a cable to form a tomography detection device, setting defects between layers of the finite element simulation model, and obtaining boundary voltage response data of the coil sensor array; constructing and solving a two-dimensional sensitive field mathematical model according to the boundary voltage response data, solving a sensitivity matrix of the electromagnetic parameter variation and the boundary voltage through a numerical calculation method, and constructing a nonlinear inverse problem model in combination with the boundary voltage response data; solving the inverse problem model to obtain a reconstructed image; and finally, determining an optimal probe design scheme based on the quality of the reconstructed image. Compared with the prior art, the method has the advantages that the sensitivity and the spatial resolution of defect detection are remarkably improved while non-contact measurement is guaranteed, and the method can meet the nondestructive detection requirements of cables of different specifications.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and in particular to a design method and system for a non-destructive cable detection probe based on tomography. Background Art

[0002] As a core infrastructure in modern power transmission, communication, and various industrial applications, cables undertake important transmission functions. However, during long-term use, cables are often affected by environmental factors, mechanical stress, and electrical loads, which may lead to problems such as insulation layer aging, conductor breakage, and external physical damage. If these defects are not detected in time, they may cause electrical fires, power outages, and even safety accidents. Therefore, how to achieve efficient, non-destructive, and accurate cable defect detection is an urgent problem to be solved in industries such as power and communication.

[0003] As the operating years of cables increase, the causes of cable failures are gradually changing. From initial problems such as cable conductor damage and insulation layer failure, it has gradually transitioned to more complex and hidden failure types, such as water-blocking buffer layer ablation, semiconductor damage, and corroded metal sheaths. These problems, especially in cross-linked polyethylene cables, have gradually become the main causes of cable failures.

[0004] Currently, non-destructive cable detection probes mainly rely on ultrasonic detection, infrared thermal imaging, X-ray detection, and eddy current detection technologies to detect internal defects or damages in cables. These technologies still have some limitations in practical applications. In some cases, it is difficult to accurately identify tiny defects inside cables, especially local damages in multi-layer cable structures. For multi-layer, multi-core, or complex-structured cables, the detection effects of existing probes are often not ideal, and it is difficult to comprehensively cover all potential defects, making complex structure detection difficult. Some detection technologies (such as X-ray detection) require a long data processing time and cannot achieve real-time detection, affecting the detection efficiency. High-precision detection equipment (such as X-ray detectors) has a high cost, which limits its application in small and medium-sized power systems. Most existing cable defect detection probes utilize ultrasonic and X-ray technologies, but they have problems such as low sensitivity, signal attenuation, and radiation safety when detecting internal defects in the insulation layer.

[0005] A cable non-destructive testing probe is disclosed in the invention patent with the publication number of CN112730483A. It applies X-ray detection technology. The detection unit includes a scintillation crystal array layer and a silicon photomultiplier tube array layer. Each scintillation crystal is configured to generate photons under the excitation of X-rays. The silicon photomultiplier tube array layer includes silicon photomultiplier tubes corresponding to each scintillation crystal of the scintillation crystal array layer one by one. Each silicon photomultiplier tube is configured to output a current signal when the corresponding scintillation crystal generates photons. The processor generates a detection result based on the current signal. However, it still has problems such as insufficient sensitivity, small detection range, and low detection accuracy. Summary of the Invention

[0006] The purpose of the present invention is to provide a design method and system for a cable non-destructive testing probe based on tomography to overcome the defects existing in the above-mentioned prior art.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] According to one aspect of the present invention, a design method for a cable non-destructive testing probe based on tomography is provided. The method steps include:

[0009] S1. Establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured. Configure n coil sensor arrays with different scales in a ring around the cable to form a tomography detection device, where n is 8, 12, or 16;

[0010] S2. Set defects between layers in the finite element simulation model and obtain the boundary voltage response data of the coil sensor array by using the electromagnetic coupling principle;

[0011] S3. Construct and solve a two-dimensional sensitive field mathematical model according to the boundary voltage response data. Solve the sensitivity matrix of the electromagnetic parameter change amount and the boundary voltage by using numerical calculation methods, and construct a non-linear inverse problem model in combination with the boundary voltage response data;

[0012] S4. Use an image reconstruction algorithm to solve the inverse problem model to obtain a reconstructed image;

[0013] S5. Determine the optimal probe design scheme by comparing the quality of the reconstructed images under different sensor array configurations.

[0014] As a preferred technical solution, the coil sensor array in S1 is arranged in a circumferentially uniform manner in a ring to form a full-area coverage detection structure for the cross-section of the cable; the working modes of the coil sensors include an excitation coil mode and a receiving coil mode. Among them, the excitation coil mode is used to generate an alternating excitation magnetic field, and the receiving coil mode is used to receive the change amount of the magnetic induction signal caused by the cable defect.

[0015] As a preferred technical solution, the cable interlayer defects in S2 include hole defects, crack defects, delamination defects, and mechanical damages, and these defects will all cause discontinuity of the cable metal interlayer structure or deterioration of performance.

[0016] As a preferred technical solution, the sensitivity matrix of the boundary voltage in S3 includes a permeability sensitivity matrix and a conductivity sensitivity matrix.

[0017] As a preferred technical solution, the permeability sensitivity matrix is used to obtain the magnetic field strength of each subdivision unit, and its specific formula is:

[0018] S μ =-jωH A ·H B

[0019] Among them, S μ is the permeability sensitivity matrix; H A is the magnetic field strength of each subdivision unit when coil A is used as the excitation coil; H B is the magnetic field strength of each subdivision unit when coil B is used as the excitation coil; j is the imaginary unit; ω is the excitation signal frequency.

[0020] As a preferred technical solution, the conductivity sensitivity matrix is used to obtain the magnetic vector potential of each subdivision unit, and its specific formula is:

[0021] S σ =-ω 2 A A ·A B

[0022] Among them, S σ is the conductivity sensitivity matrix; ω is the excitation signal frequency; A A is the magnetic vector potential of each subdivision unit when coil A is used as the excitation coil; A B is the magnetic vector potential of each subdivision unit when coil B is used as the excitation coil.

[0023] As a preferred technical solution, the image imaging algorithm in S4 includes the LBP algorithm, the Landweber iterative algorithm, and the Tikhonov regularization algorithm.

[0024] As a preferred technical solution, the specific formula for the assumed minimization objective function of the Landweber iterative algorithm is:

[0025]

[0026] Among them, f(g) is the minimization objective function; U is the observation vector; g is the vector to be solved; S is the system matrix; |||| represents the Euclidean norm, which is used to measure the length of the vector.

[0027] As a preferred technical solution, the specific formula for the solution process of the Tikhonov regularization algorithm is as follows:

[0028] g = (S T S + μI) -1 S T U

[0029]

[0030] where min represents solving the minimization problem; f(g) is the minimization objective function; U is the observation vector; g is the vector to be solved; S is the system matrix; |||| represents the Euclidean norm, which is used to measure the length of the vector; is the estimated solution obtained according to the prior information; μ is the regularization parameter; I is the identity matrix; L is the matrix corresponding to a specific operation.

[0031] According to another aspect of the present invention, there is provided a cable non-destructive testing probe design system based on tomography, which operates by applying a cable non-destructive testing probe design method as described above. The system includes a coil sensor design module, a defect simulation module, an electromagnetic data acquisition module, an inverse problem solving module, and a probe optimization module;

[0032] Among them, the coil sensor design module is used to establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured, and configure n different-scale coil sensor arrays around the cable in a ring to form a tomography detection device, where n is 8, 12, or 16;

[0033] The defect simulation module is used to set defects between layers in the finite element simulation model and obtain the boundary voltage response data of the coil sensor array by using the electromagnetic coupling principle;

[0034] The electromagnetic data acquisition module constructs and solves a two-dimensional sensitive field mathematical model according to the boundary voltage response data, solves the sensitivity matrix of the electromagnetic parameter change amount and the boundary voltage by a numerical calculation method, and constructs a non-linear inverse problem model in combination with the boundary voltage response data;

[0035] The inverse problem solving module uses an image reconstruction algorithm to solve the inverse problem model and obtain a reconstructed image;

[0036] The probe optimization module is used to determine the optimal probe design scheme by comparing the reconstructed image quality under different sensor array configurations.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In the present invention, through five steps of coil sensor design, defect simulation, electromagnetic data acquisition, inverse problem solution, and probe optimization, the design of the optimized sensor probe is completed. While ensuring non-contact measurement, the sensitivity and spatial resolution of defect detection are significantly improved, and it can also adapt to the non-destructive testing requirements of different specifications of cables.

[0039] 2. In the present invention, based on the principle of tomography, a ring-shaped distributed sensor array design is adopted, and the visualization reconstruction of internal defects of the cable is realized through non-invasive detection technology. Its non-contact detection mechanism is realized through the cable coupling principle, and its non-destructive testing characteristics keep a certain distance between the coil and the cable surface, completely avoiding the insulation layer wear caused by mechanical contact, and are especially suitable for on-site detection of laid cables.

[0040] 3. In the present invention, by establishing a finite element simulation model based on the geometric parameters and material properties of the cable, it can dynamically adapt to the cable structures with different numbers of layers, sizes, and materials (such as insulation layers and shielding layers), and solves the problem of insufficient modeling of complex multi-layer cables by traditional detection methods. Combining the electromagnetic coupling principle, it can accurately capture the micro-variation of the dielectric constant caused by interlayer hole defects and improve the defect location accuracy.

[0041] 4. In the present invention, the constructed two-dimensional sensitive field matrix includes two core components, the permeability sensitivity matrix and the conductivity sensitivity matrix. The permeability sensitivity matrix is used to calculate the magnetic field intensity distribution of each subdivision unit, and the conductivity sensitivity matrix is used to solve the magnetic vector potential parameters of each subdivision unit. By accurately characterizing the dynamic response characteristics of the sensor to the internal conductivity distribution of the cable, combining the inverse problem solution algorithm and the image reconstruction technology, it effectively overcomes the ill-posed problem in the inversion of the conductivity distribution by traditional methods. Through this fusion technology, not only the reconstruction accuracy of the material conductivity distribution is significantly improved, but also a breakthrough improvement in the spatial location ability of internal defects of the cable is achieved.

[0042] 5. In the present invention, the imaging algorithms include the LBP algorithm, the Landweber iterative algorithm, and the Tikhonov regularization algorithm; the minimization objective functions of the Landweber iterative algorithm and the Tikhonov regularization algorithm and their solution processes are also given. By comprehensively comparing the reconstruction effects of different imaging algorithms, the accuracy and reliability of the conductivity distribution are evaluated, and then the design parameters of the sensor probe are optimized to solve the most suitable probe design method, improving the detection sensitivity and imaging quality, and providing technical support for the accurate detection of internal defects of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of the steps of a non-destructive testing probe design method based on tomography in the present invention;

[0044] Figure 2aSensitivity map corresponding to the excitation of Coil 1 and Coil 2 in the sensitive field between electrodes in the embodiment;

[0045] Figure 2b Sensitivity map corresponding to the excitation of Coil 1 and Coil 3 in the sensitive field between electrodes in the embodiment;

[0046] Figure 3 Schematic diagram of the 8-channel sensor array and corresponding defects in the embodiment;

[0047] Figure 4a Imaging schematic diagram obtained by using the LBP algorithm in the case of the 8-channel sensor array in the embodiment;

[0048] Figure 4b Imaging schematic diagram obtained by using the Tikhonov algorithm in the case of the 8-channel sensor array in the embodiment;

[0049] Figure 4c Imaging schematic diagram obtained by using the Landweber algorithm in the case of the 8-channel sensor array in the embodiment;

[0050] Figure 5 Schematic diagram of the 12-channel sensor array and corresponding defects in the embodiment;

[0051] Figure 6a Imaging schematic diagram obtained by using the LBP algorithm in the case of the 12-channel sensor array in the embodiment;

[0052] Figure 6b Imaging schematic diagram obtained by using the Tikhonov algorithm in the case of the 12-channel sensor array in the embodiment;

[0053] Figure 6c Imaging schematic diagram obtained by using the Landweber algorithm in the case of the 12-channel sensor array in the embodiment;

[0054] Figure 7 Schematic diagram of the 16-channel sensor array and corresponding defects in the embodiment;

[0055] Figure 8a Imaging schematic diagram obtained by using the LBP algorithm in the case of the 16-channel sensor array in the embodiment;

[0056] Figure 8b Imaging schematic diagram obtained by using the Tikhonov algorithm in the case of the 16-channel sensor array in the embodiment;

[0057] Figure 8c Imaging schematic diagram obtained by using the Landweber algorithm in the case of the 16-channel sensor array in the embodiment. Detailed implementation manner

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] As the core infrastructure in modern power transmission, communication, and various industrial applications, cables undertake important transmission functions and play an important role in the fields of energy transmission, communication, and industrial automation in modern society. With the increase in power demand and the acceleration of the industrialization process, the use scope of cables is getting wider and wider, especially in high-voltage power transmission and communication systems. However, during long-term use, cables are often affected by environmental factors, mechanical stress, and electrical loads, which may lead to problems such as insulation layer aging, conductor fracture, and external physical damage. If these defects are not detected in time, they may cause electrical fires, power outages, and even safety accidents. Therefore, how to achieve efficient, non-destructive, and accurate cable defect detection is an urgent problem to be solved in the power, communication, and other industries.

[0060] As the operating years of cables increase, the causes of cable failures are gradually changing. Initially, problems such as cable conductor damage and insulation layer failure have gradually transitioned to more complex and hidden fault types, such as water-blocking buffer layer ablation, semiconductor damage, and corroded metal sheaths. These problems, especially in cross-linked polyethylene cables, have gradually become the main causes of cable failures.

[0061] Specifically, as the service life of cables increases, the internal structure of cables will be affected by long-term environmental factors (such as temperature changes, humidity, mechanical stress, etc.) and electrical loads, and gradually show aging and deterioration phenomena. First, the ablation problem of the water-blocking buffer layer is mainly due to the decline in the thermal stability of the buffer layer material under the long-term action of high temperature and electric field in the cable, resulting in a gradual attenuation of its performance, unable to effectively isolate the intrusion of moisture inside the cable, and then triggering cable failures. The function of the water-blocking buffer layer is mainly to prevent moisture from entering the cable interior, and the ablation or damage of the water-blocking buffer layer will cause moisture to penetrate into the cable interior, triggering hydrolysis of the insulation layer and further accelerating the aging of the cable.

[0062] Existing cable defect detection technologies (such as eddy current, ultrasonic, and X-ray) have problems such as low sensitivity, signal attenuation, and radiation safety when detecting internal defects of the insulation layer.

[0063] To this end, the present application proposes a design method for a non-destructive testing probe for cables based on tomography, which optimizes the defect recognition accuracy and realizes three-dimensional reconstruction through electromagnetic tomography combined with a deep learning image reconstruction algorithm, solves the bottleneck problems of traditional technologies, has the advantages of high precision and high efficiency, meets the high standards required for the safe operation of cables in the power industry, and has important engineering application value and market prospects.

[0064] Example 1

[0065] A design method for a non-destructive testing probe for cables based on tomography, which realizes high-sensitivity non-destructive testing of cables by optimizing the design of electromagnetic sensor probes. The steps of the method are as Figure 1 shown and specifically include:

[0066] A design method for a non-destructive testing probe for cables based on tomography, the method steps include:

[0067] S1. Establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured, and configure n coil sensor arrays of different scales in a ring around the cable to form a tomography detection device, where n is 8, 12 or 16;

[0068] S2. Set defects between the layers of the finite element simulation model, and use the principle of electromagnetic coupling to obtain the boundary voltage response data of the coil sensor array;

[0069] S3. Construct and solve a two-dimensional sensitive field mathematical model according to the boundary voltage response data, solve the sensitivity matrix of the electromagnetic parameter change amount and the boundary voltage through numerical calculation methods, and construct a non-linear inverse problem model in combination with the boundary voltage response data;

[0070] S4. Use an image reconstruction algorithm to solve the inverse problem model to obtain a reconstructed image;

[0071] S5. Determine the optimal probe design scheme by comparing the reconstructed image quality under different sensor array configurations.

[0072] The coil sensor array in S1 is arranged in a circumferentially uniform manner in a ring to form a full-area coverage detection structure for the cross-section of the cable; the working modes of the coil sensors include an excitation coil mode and a receiving coil mode, where the excitation coil mode is used to generate an alternating excitation magnetic field, and the receiving coil mode is used to receive the change amount of the magnetic induction signal caused by cable defects.

[0073] The cable interlayer defects in S2 include hole defects, crack defects, delamination defects and mechanical damage.

[0074] The sensitivity matrix of the boundary voltage in S3 includes a permeability sensitivity matrix and a conductivity sensitivity matrix. In this embodiment, the sensitivity maps corresponding to the excitation of coil 1 and coil 2 in the sensitive field between electrodes are as shown in Figure 2a ; the sensitivity maps corresponding to the excitation of coil 1 and coil 3 in the sensitive field between electrodes are as shown in Figure 2b .

[0075] The permeability sensitivity matrix is used to obtain the magnetic field strength of each subdivision unit, and its specific formula is:

[0076] S μ =-jωH A ·H B

[0077] where S μ is the permeability sensitivity matrix; H A is the magnetic field strength of each subdivision unit when coil A is used as the excitation coil; H B is the magnetic field strength of each subdivision unit when coil B is used as the excitation coil; j is the imaginary unit; ω is the excitation signal frequency.

[0078] The conductivity sensitivity matrix is used to obtain the magnetic vector potential of each subdivision unit, and its specific formula is:

[0079] S σ =-ω 2 A A ·A B

[0080] where S σ is the conductivity sensitivity matrix; ω is the excitation signal frequency; A A is the magnetic vector potential of each subdivision unit when coil A is used as the excitation coil; A B is the magnetic vector potential of each subdivision unit when coil B is used as the excitation coil.

[0081] The image imaging algorithm in S4 includes the LBP algorithm, the Landweber iterative algorithm, and the Tikhonov regularization algorithm.

[0082] The specific formula for the assumed minimization objective function of the Landweber iterative algorithm is:

[0083]

[0084] where f(g) is the minimization objective function; U is the observation vector; g is the vector to be solved; S is the system matrix; |||| represents the Euclidean norm, which is used to measure the length of the vector.

[0085] In this embodiment, the specific formula for the solution process of the Tikhonov regularization algorithm is:

[0086] g = (S T S + μI) -1 S T U

[0087]

[0088] where min represents solving a minimization problem; f(g) is the minimization objective function; U is the observation vector; g is the vector to be solved; S is the system matrix; ‖‖ represents the Euclidean norm, which is used to measure the length of a vector; is the estimated solution obtained based on prior information; μ is the regularization parameter; I is the identity matrix; L is the matrix corresponding to a specific operation.

[0089] In this embodiment, an optimized image reconstruction algorithm and a sensor probe design are carried out; by comparing the reconstruction effects of different imaging algorithms, the parameters of the optimized image reconstruction algorithm and the sensor probe design (such as the number of array elements, arrangement pattern, excitation frequency, etc.) are optimized to improve the detection sensitivity and imaging quality, providing technical support for the accurate detection of internal defects of cables.

[0090] In summary, this solution is based on tomography technology to perform high-precision detection of internal defects of cables, and combines an image reconstruction algorithm to provide the best design solution for cable detection probes. Through the annular array layout and multi-frequency excitation, synchronous detection of the depth and surface of internal defects of cables is achieved, resulting in high detection sensitivity. Combining the inverse problem solving algorithm and image reconstruction technology significantly improves the accuracy of conductivity distribution inversion and the defect location accuracy, and realizes the three-dimensional visualization of internal defects of cables. Combining the sensitive field matrix and image reconstruction technology solves the ill-posedness of the inverse problem and improves the defect location accuracy.

[0091] Embodiment 2

[0092] In this embodiment, a cable non-destructive testing probe design system based on tomography is applied. This system works by applying a cable non-destructive testing probe design method based on tomography. The system includes a coil sensor design module, a defect simulation module, an electromagnetic data acquisition module, an inverse problem solving module, and a probe optimization module;

[0093] Among them, the coil sensor design module is used to establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured, and configure n different-scale coil sensor arrays around the cable in a ring to form a tomography detection device, where n is 8, 12, or 16;

[0094] The defect simulation module is used to set defects between layers in the finite element simulation model and obtain the boundary voltage response data of the coil sensor array using the electromagnetic coupling principle;

[0095] The electromagnetic data acquisition module constructs and solves a two-dimensional sensitive field mathematical model based on the boundary voltage response data, solves the sensitivity matrix of the electromagnetic parameter change and the boundary voltage through a numerical calculation method, and constructs a nonlinear inverse problem model in combination with the boundary voltage response data;

[0096] The inverse problem solving module uses an image reconstruction algorithm to solve the inverse problem model and obtain a reconstructed image;

[0097] The probe optimization module is used to determine the optimal probe design scheme by comparing the reconstructed image quality under different sensor array configurations.

[0098] In this embodiment, according to physical characteristics such as the cable cross-section size and the interlayer dielectric distribution, a finite element analysis method is used to construct an electromagnetic simulation model including multiple layers such as conductor-insulation-sheath, and an expandable coil array (supporting three configuration modes of 8 / 12 / 16 units) is arranged in a circular shape around the model to form a tomography detection system; then, by setting hole defects between the internal structures of the cable layer, the boundary voltage response data of the sensor array is obtained using the electromagnetic coupling principle; a two-dimensional sensitive field mathematical model is constructed and solved, the sensitivity matrix of the electromagnetic parameter change and the boundary voltage is solved through a numerical calculation method, and a nonlinear inverse problem model is constructed in combination with the measured data; finally, image reconstruction algorithms such as Tikhonov regularization and Landweber iteration are used for solution, and the optimal probe design scheme is determined by comparing the dielectric constant distribution image quality under different sensor array configurations.

[0099] In this embodiment, an improved Landweber algorithm is adopted.

[0100] In this embodiment, an 8-channel sensor array is designed, and corresponding defects are made, such as Figure 3 shown. Subsequently, for the 8-channel sensor array, three algorithms, namely LBP, Tikhonov, and Landweber, are respectively used for imaging, as shown in Figure 4a 、 Figure 4b and Figure 4c shown. In addition, a 12-channel sensor array is designed and corresponding defects are made, as shown in Figure 5 shown, and the same three algorithms are used for imaging, as shown in Figure 6a 、 Figure 6b and Figure 6c shown. Finally, a 16-channel sensor array is also designed, and corresponding defects are made, as shown in Figure 7 shown, and the same three algorithms, namely LBP, Tikhonov, and Landweber, are also used for imaging, with reference to Figure 8a 、 Figure 8b and Figure 8cAs shown in the figure. The core advantage of this solution lies in establishing the optimal solution through the comparison of three algorithms. Although the LBP algorithm has high computational efficiency, it is vulnerable to noise interference and has obvious background artifacts. Although Tikhonov regularization suppresses noise, it sacrifices resolution and increases the degree of edge blurring. The improved Landweber algorithm finally adopted in the present invention, through the design of adaptive iteration parameters, significantly enhances the detail restoration while maintaining the noise resistance, verifying the comprehensive performance advantage of the present invention in applying this algorithm in complex imaging scenarios.

[0101] In summary, this solution significantly improves the detection accuracy and resolution of cable internal defects by optimizing the sensor array design and combining single excitation technology.

[0102] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A design method for a non-destructive testing probe of a cable based on tomography, characterized in that, The method steps include: S1. Establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured. Arrange n coil sensor arrays of different scales in a ring around the cable to form a tomography detection device, where n is 8, 12 or 16; S2. Set defects between the layers of the finite element simulation model, and use the electromagnetic coupling principle to obtain the boundary voltage response data of the coil sensor array; S3. Construct and solve a two-dimensional sensitive field mathematical model based on the boundary voltage response data, solve the sensitivity matrix of the electromagnetic parameter change amount and the boundary voltage through numerical calculation methods, and construct a non-linear inverse problem model in combination with the boundary voltage response data; S4. Use an image reconstruction algorithm to solve the inverse problem model to obtain a reconstructed image; S5. Determine the optimal probe design scheme by comparing the quality of the reconstructed images under different sensor array configurations.

2. The design method of a cable non-destructive testing probe based on tomography according to claim 1, characterized in that The coil sensor array in S1 is arranged in a circumferentially uniform ring to form a full-area coverage detection structure for the cross-section of the cable; the working modes of the coil sensors include an excitation coil mode and a receiving coil mode. Among them, the excitation coil mode is used to generate an alternating excitation magnetic field, and the receiving coil mode is used to receive the change amount of the magnetic induction signal caused by the cable defect.

3. A method for designing a non-destructive testing probe for cables based on tomography according to claim 1, characterized in that The cable interlayer defects in S2 include hole defects, crack defects, delamination defects and mechanical damage.

4. A method for designing a non-destructive testing probe for cables based on tomography, characterized in that, The sensitivity matrix of the boundary voltage in S3 includes a permeability sensitivity matrix and a conductivity sensitivity matrix.

5. A design method for a non-destructive testing probe of a cable based on tomography according to claim 4, characterized in that The permeability sensitivity matrix is used to calculate the magnetic field intensity of each subdivision unit, and its specific formula is: S μ = -jωH A ·H B Among them, S μ is the permeability sensitivity matrix; H A is the magnetic field strength of each subdivision unit when coil A is used as the excitation coil; H B is the magnetic field strength of each subdivision unit when coil B is used as the excitation coil; j is the imaginary unit; ω is the excitation signal frequency.

6. A method for designing a non-destructive testing probe for cables based on tomography according to claim 4, characterized in that, The conductivity sensitivity matrix is used to calculate the magnetic vector potential of each subdivision unit, and its specific formula is: S σ = -ω 2 A A ·A B Among them, S σ is the conductivity sensitivity matrix; ω is the excitation signal frequency; A A is the magnetic vector potential of each subdivision unit when coil A is used as the excitation coil; A B is the magnetic vector potential of each subdivision unit when coil B is used as the excitation coil.

7. A design method of a non-destructive testing probe for cables based on tomography according to claim 1, characterized in that, The image imaging algorithm in S4 includes the LBP algorithm, the Landweber iterative algorithm and the Tikhonov regularization algorithm.

8. A design method of a non-destructive testing probe for cables based on tomography according to claim 7, characterized in that, The specific formula for assuming the minimization objective function of the Landweber iterative algorithm is: Among them, f(g) is the minimization objective function; U is the observation vector; g is the vector to be solved; S is the system matrix; || || represents the Euclidean norm, which is used to measure the length of the vector.

9. A design method for a non-destructive testing probe of a cable based on tomography, characterized in that, The specific formula for the solution process of the Tikhonov regularization algorithm is: g = (S T S + μI) -1 S T U Among them, min represents solving a minimization problem; f(g) is the objective function to be minimized; U is the observation vector; g is the vector to be solved; S is the system matrix; ||||| represents the Euclidean norm, which is used to measure the length of a vector; is the estimated solution obtained based on prior information; μ is the regularization parameter; I is the identity matrix; L is the matrix corresponding to a specific operation.

10. A cable non-destructive testing probe design system based on tomography, characterized in that, This system operates using a cable non-destructive testing probe design method according to any one of claims 1-9. The system includes a coil sensor design module, a defect simulation module, an electromagnetic data acquisition module, an inverse problem solving module and a probe optimization module; The coil sensor design module is used to establish a finite element simulation model of a multi-layer cable structure according to the geometric parameters and material properties of the cable to be measured. Arrange n coil sensor arrays of different scales in a ring around the cable to form a tomography detection device, where n is 8, 12 or 16; The defect simulation module is used to set defects between the layers of the finite element simulation model, and use the electromagnetic coupling principle to obtain the boundary voltage response data of the coil sensor array; The electromagnetic data acquisition module constructs and solves a two-dimensional sensitive field mathematical model based on the boundary voltage response data, solves the sensitivity matrix of the electromagnetic parameter variation and the boundary voltage through a numerical calculation method, and constructs a nonlinear inverse problem model in combination with the boundary voltage response data; The inverse problem solving module uses an image reconstruction algorithm to solve the inverse problem model and obtain a reconstructed image; The probe optimization module is used to determine the optimal probe design scheme by comparing the quality of the reconstructed images under different sensor array configurations.

Images