Cable defect nondestructive testing method and system based on multi-mode non-invasive imaging

Abstract

The invention relates to a cable defect nondestructive testing method and system based on multi-mode non-invasive imaging. The method comprises the following steps: scanning a cable line through an infrared thermal imaging technology to obtain a cable surface temperature distribution condition and a temperature abnormal area; an electromagnetic sensor array is arranged in the temperature abnormal area; exciting an electromagnetic sensor to generate an alternating magnetic field, and collecting an electromagnetic response signal by using the electromagnetic sensor; based on the electromagnetic response signal, an electrical conductivity distribution image in the cable is reconstructed by using an image reconstruction algorithm; and finally, according to the cable surface temperature distribution condition and the cable internal conductivity distribution image, in combination with multi-modal characteristic correlation analysis, determining the fault type and fault point in the cable, and realizing nondestructive detection of cable defects. Compared with the prior art, the device does not need to be in contact with a cable, is suitable for long-distance operation, can further check hidden dangers which cannot be accurately detected by infrared rays, and realizes rapid and efficient nondestructive detection of cable defects.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and in particular to a non-destructive detection method and system for cable defects based on multi-modal non-intrusive imaging. Background Art

[0002] With the wide application of new power systems in various fields, power cables, as key infrastructure for power transmission, play an important role. However, during the operation of power cables, due to the influence of various factors such as construction environment, construction technology, overload operation, and humid environment, various defects are likely to occur, resulting in a decline in the safety performance of the cables and even serious failures. As the operation years of the cables increase, problems such as ablation of the water-blocking buffer layer, damage to semiconductors, and corrosion of metal sheaths are gradually becoming the main causes of faults in high-voltage cables. If these latent defects cannot be detected and handled in a timely manner, it may lead to breakdown phenomena in various structures at the cable accessories, resulting in power outages of the lines. In severe cases, it may even cause major accidents such as tunnel fires that endanger personal safety. Therefore, how to accurately and efficiently detect the defects of power cables has become a key issue for ensuring the safe and stable operation of power systems.

[0003] Non-intrusive imaging is a process industrial detection technology that emerged in the 1990s. Its basic principle is to arrange sensors in a specific form around the area to be measured. After obtaining a certain amount of data through the acquisition system, with the help of mathematical methods, the distribution of material properties inside the object to be measured is obtained through image reconstruction. Since there are significant differences in the conductivity between the defect area and other normal stratified areas when electrical trees, water trees, etc. occur in the cable accessories medium, non-intrusive imaging technology can be used to achieve non-destructive detection of cable accessory defects.

[0004] In the invention patent with the publication number CN115825075A, a method for detecting cable breakage based on infrared signals is disclosed; the thermal distribution state of the energized cable can be directly observed, and the infrared thermal image of the cable can be obtained using the shooting function. It improves the Otsu segmentation method and can find the fault point in the infrared image of the cable breakage, making the system more intelligent in diagnosing the breakage of the cable. However, its detection means are single, only using infrared signals for detection. In practical applications, as a core device for long-distance power transmission, high-voltage cables usually have a long laying distance and a complex and changeable operating environment. Single non-destructive detection technologies often have difficulty in balancing detection efficiency and accuracy. Summary of the Invention

[0005] The purpose of the present invention is to provide a non-destructive detection method and system for cable defects based on multi-modal non-intrusive imaging to overcome the defects existing in the above-mentioned prior art.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] According to one aspect of the present invention, there is provided a non-destructive detection method for cable defects based on multi-modal non-invasive imaging, and the method steps include:

[0008] S1. Implement a full-coverage line scan of the cable line through infrared thermal imaging technology to obtain the surface temperature distribution of the cable and the temperature abnormal area;

[0009] S2. Set up an electromagnetic sensor array at the corresponding position of the temperature abnormal area on the cable surface;

[0010] S3. Excite the electromagnetic sensor to generate an alternating magnetic field, cause the conductive medium in the cable to generate a change in magnetic permeability in the magnetic field, and use the electromagnetic sensor to collect the electromagnetic response signal;

[0011] S4. Based on the electromagnetic response signal, use an image reconstruction algorithm to reconstruct the conductivity distribution image inside the cable;

[0012] S5. According to the surface temperature distribution of the cable and the conductivity distribution image inside the cable, combine multi-modal feature correlation analysis to judge the internal fault type and fault point of the cable, and realize non-destructive detection of cable defects.

[0013] As a preferred technical solution, the specific process of S1 is: First, use a high-precision thermal imager to perform a full-line scan of the cable surface, capture the local abnormal temperature rise phenomenon of the cable, and thus output the far-infrared radiation information. Then, generate a temperature distribution map according to the far-infrared radiation information, and determine the abnormal area through the temperature threshold; during the process of using the high-precision thermal imager to perform a full-line scan of the cable surface, perform dynamic temperature difference compensation.

[0014] As a preferred technical solution, the electromagnetic sensor array in S2 is specifically eight electromagnetic sensors composed of coils, arranged in an equally spaced circular pattern. Among the eight coils, one is an excitation coil, and the rest are receiving coils.

[0015] As a preferred technical solution, the excitation coil is connected to a signal excitation circuit, and the receiving coil is connected to a signal acquisition circuit.

[0016] As a preferred technical solution, the electromagnetic sensor array is closely attached to the cable surface.

[0017] As a preferred technical solution, the working mode of the electromagnetic sensor array includes single-frequency excitation and multi-frequency scanning.

[0018] As a preferred technical solution, the specific process of S3 is as follows: First, an alternating current is applied to the excitation coil to generate an alternating magnetic field distributed in parallel or fan shape within the cable; then, the receiving coil is used to detect the secondary magnetic field generated after the cable modulates the alternating magnetic field; finally, through the methods of multi-frequency excitation and multi-point detection, the electromagnetic response signal of the cable is obtained.

[0019] As a preferred technical solution, the specific expression for reconstructing the conductivity distribution image inside the cable in S4 is:

[0020] σ(r) = argmin σ ‖‖M(σ) - M means ‖‖ 2 + λR(σ)

[0021] where σ(r) is the conductivity distribution, M(σ) is the electromagnetic field data predicted by the model, M means is the electromagnetic response signal, λ is the regularization parameter, and R(σ) is the regularization term.

[0022] As a preferred technical solution, the specific process of combining multi-modal feature correlation analysis in S5 is as follows: First, obtain the cable surface temperature field distribution data and the internal conductivity distribution data, and locate the co-occurrence area of temperature anomaly and conductivity anomaly through correlation analysis to achieve precise positioning of defects; then use imaging technology to perform three-dimensional reconstruction of the defect structure, and finally achieve non-destructive precise detection of the cable; among them, the multi-modal features include the cable surface temperature field information obtained by infrared thermal imaging technology and the cable internal conductivity distribution data obtained by electromagnetic imaging technology.

[0023] According to another aspect of the present invention, a cable defect non-destructive detection system based on multi-modal non-invasive imaging is provided. The system operates by applying a cable accessory defect detection method based on multi-modal non-invasive imaging as described above. The system includes an infrared imaging module, a multi-modal imaging module, a signal acquisition card, a front-end sensor, and a back-end processor;

[0024] The infrared imaging module is used to perform full-coverage line scanning on the cable line through infrared thermal imaging technology to obtain the cable surface temperature distribution and temperature anomaly areas;

[0025] The front-end sensor is used on the one hand to generate an alternating magnetic field to cause the magnetic permeability change of the conductive medium in the cable in the magnetic field, and on the other hand to collect electromagnetic response signals;

[0026] The multi-modal imaging module is used to reconstruct the conductivity distribution image inside the cable based on the electromagnetic response signal by using an image reconstruction algorithm;

[0027] The backend processor is used to determine the type of internal cable fault and the fault point by combining the cable surface temperature distribution and the conductivity distribution image inside the cable through multi-modal feature correlation analysis;

[0028] The signal acquisition card is used to transmit the signals collected by the front-end sensors, the infrared imaging module and the multi-modal imaging module to the backend processor.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In the present invention, first, the cable line is scanned comprehensively by infrared thermal imaging technology; then, an electromagnetic sensor array is used to excite the electromagnetic sensors to generate an alternating magnetic field, so that the conductive medium in the cable generates a change in magnetic permeability in the magnetic field, and the electromagnetic response signals are collected by the electromagnetic sensors to reconstruct the conductivity distribution image inside the cable; through the multi-modal strategy of "infrared rapid preliminary screening + electromagnetic precise re-inspection", the detection efficiency is greatly improved. Infrared thermal imaging can quickly scan the entire cable segment through non-contact, and quickly lock the area with abnormal surface temperature; non-invasive imaging only performs in-depth scanning on the abnormal area, avoiding redundant operations for the whole segment. Thus, a full-dimensional defect coverage from the cable surface to the deep structure is achieved.

[0031] 2. The present invention uses infrared thermal imaging technology to scan the cable line comprehensively; and uses electromagnetic sensors to reconstruct the conductivity distribution image inside the cable, applies an alternating current to the excitation coil to generate an alternating magnetic field with a parallel or fan-shaped distribution inside the cable; then, uses the receiving coil to detect the secondary magnetic field generated after the cable modulates the alternating magnetic field; finally, through the method of multi-frequency excitation and multi-point detection, the electromagnetic response signals of the cable are obtained. The operator has no contact with the cable throughout the process and there is no need to cut off the power supply, reducing the losses caused by power supply interruption. It is suitable for high-risk scenarios such as high-voltage cables and submarine cables. And the present invention effectively suppresses the change of ambient temperature through dynamic temperature difference compensation and can still operate stably under harsh conditions.

[0032] 3. In the present invention, the conductivity distribution image inside the cable is reconstructed through the electromagnetic field data predicted by the model, the electromagnetic response signals, the electromagnetic response signals and the regularization term. The obtained conductivity distribution image can intuitively reflect the changes in the electromagnetic characteristics of different structural media of the cable accessories. By analyzing the conductivity distribution characteristics, the abnormal area of the cable accessories is identified, improving the accuracy and intuitiveness of the detection.

[0033] 4. In the present invention, the electromagnetic sensor array is specifically eight electromagnetic sensors composed of coils, arranged in an equally spaced circular pattern, and the working modes of the electromagnetic sensor array include single-frequency excitation and multi-frequency scanning, making it widely applicable and highly practical.

[0034] 5. In the present invention, first, the surface temperature field distribution data and the internal conductivity distribution data of the cable are obtained. Through correlation analysis, the co-occurrence area of temperature anomalies and conductivity anomalies is located to achieve precise positioning of defects. Subsequently, imaging technology is used to perform three-dimensional reconstruction of the defect structure, and finally, non-destructive precise detection of the cable is realized. Among them, the multi-modal features include the cable surface temperature field information obtained by infrared thermal imaging technology and the cable internal conductivity distribution data obtained by electromagnetic imaging technology. An improvement is made to the problem that traditional detection requires damaging the cable surface. Through a non-invasive detection method, precise positioning of faults is carried out to accurately guide the repair location and avoid replacing the entire section. In addition, the fault point can be accurately detected at the initial stage of the fault, realizing the transformation from "fault repair" to "preventive maintenance". BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the steps of a non-destructive cable defect detection method based on multi-modal non-invasive imaging in the present invention;

[0036] Figure 2 Schematic diagram of the principle of the sensor array of the multi-modal non-invasive imaging system in the present invention;

[0037] Figure 3 Schematic diagram of the typical structure of the sensor of the multi-modal non-invasive imaging system in the present invention;

[0038] Figure 4 Schematic diagram of the cable defect results detected by infrared thermal scanning in the embodiment;

[0039] Figure 5 Schematic diagram of the results of the first cable defect detected by non-invasive imaging technology in the embodiment;

[0040] Figure 6 Schematic diagram of the results of the second cable defect detected by non-invasive imaging technology in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. 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.

[0042] With the wide application of the new power system in various fields, power cables, as the key infrastructure for power transmission, play an important role. However, during the operation of power cables, due to various factors such as construction environment, construction technology, overload operation, and humid environment, various defects are likely to occur, leading to a decline in the safety performance of the cables and even serious failures. As the operation years of the cables increase, problems such as ablation of the water-blocking buffer layer, damage to the semiconductor, and corrosion of the metal sheath are gradually becoming the main causes of failures in high-voltage cables. If these latent defects cannot be detected and handled in a timely manner, it may lead to breakdown phenomena in the structures at the cable accessories, resulting in power outages in the line, and in severe cases, even major accidents endangering personal safety such as tunnel fires. Therefore, how to accurately and efficiently detect the defects of power cables has become a key issue in ensuring the safe and stable operation of the power system.

[0043] Non-invasive imaging is a process industrial inspection technology that emerged in the 1990s. Its basic principle is to arrange sensors in a specific form around the area to be measured. After obtaining a certain amount of data through the acquisition system, with the help of mathematical methods, the distribution of the material properties inside the object to be measured is obtained through image reconstruction. Since there are significant differences in the conductivity between the defect areas and other normal stratified areas when electrical trees, water trees, etc. occur in the cable accessories medium, non-invasive imaging technology can be used to achieve non-destructive detection of cable accessory defects.

[0044] However, in practical applications, as the core equipment for long-distance power transmission, high-voltage cables usually have a long laying distance and a complex and changeable operating environment. A single non-destructive detection technology often has difficulty in taking into account both detection efficiency and accuracy. Therefore, there is an urgent need for a phased and multi-level comprehensive detection method to effectively cope with complex scenarios in practical applications and ensure the comprehensiveness and reliability of detection results.

[0045] Embodiment 1

[0046] In this embodiment, a non-destructive detection method for cable defects based on multi-modal non-invasive imaging is applied. The method steps are as Figure 1 shown and specifically include:

[0047] S1. Perform a full-coverage line scan on the cable line through infrared thermal imaging technology to obtain the surface temperature distribution of the cable and the temperature abnormal areas;

[0048] S2. Set up an electromagnetic sensor array at the corresponding positions of the temperature abnormal areas on the cable surface;

[0049] S3. Excite the electromagnetic sensors to generate an alternating magnetic field, cause the magnetic permeability of the conductive medium in the cable to change in the magnetic field, and use the electromagnetic sensors to collect electromagnetic response signals;

[0050] S4. Based on the electromagnetic response signal, use an image reconstruction algorithm to reconstruct the conductivity distribution image inside the cable;

[0051] S5. According to the cable surface temperature distribution and the conductivity distribution image inside the cable, combined with multi-modal feature correlation analysis, to judge the internal fault type and fault point of the cable, and realize non-destructive detection of cable defects.

[0052] Among them, the specific process of S1 is as follows: First, use a high-precision thermal imager to scan the entire cable surface, capture the local abnormal temperature rise phenomenon of the cable, so as to output far-infrared radiation information, then generate a temperature distribution map according to the far-infrared radiation information, and determine the abnormal area through the temperature threshold; during the process of using the high-precision thermal imager to scan the entire cable surface, dynamic temperature difference compensation is carried out. The results of cable defects detected by infrared thermal scanning are shown as Figure 4 shown.

[0053] The specific scheme for detecting the local overheating part of the cable by using non-invasive imaging technology in S2 is: design a coil sensor, and complete non-invasive imaging based on this coil sensor. The coil sensor can be used both as an excitation coil and to receive the response magnetic field signal. The electromagnetic sensor array is specifically eight electromagnetic sensors composed of coils, arranged in an equally spaced circular pattern; the principle of the sensor array is as Figure 2 shown. The electromagnetic sensor array is closely attached to the cable surface, making the detected signal more stable and the detection result more accurate.

[0054] In this embodiment, the typical structure of the sensor of the multi-modal non-invasive imaging system is as Figure 3 shown.

[0055] The specific process of completing non-invasive imaging based on the coil sensor is as follows: First, apply an alternating current through the excitation coil to generate an alternating magnetic field with a parallel or fan-shaped distribution in the measurement space; second, use the receiving coil to detect the secondary magnetic field generated after the cable accessory modulates the alternating magnetic field; finally, through the method of multi-frequency excitation and multi-point detection, collect the electromagnetic field data of the measured area, and perform preprocessing and inversion calculation on the data to extract the electromagnetic characteristic parameters of the cable accessory.

[0056] This solution realizes the rapid scanning to refined detection of cable accessories by combining infrared thermal imaging and multi-modal non-invasive imaging technology.

[0057] In this embodiment, first, use an infrared thermal sensor to scan the cable accessory in a large range to obtain the temperature distribution data of the measured area. By analyzing the temperature distribution characteristics, initially identify the overheating abnormal area that may have defects. The infrared thermal imaging technology can quickly locate the local overheating part of the cable accessory and provide the target area for subsequent refined detection.

[0058] According to the temperature anomaly area detected by infrared detection, non-invasive imaging technology is used for refined detection. Based on the actual application requirements, the number of turns and size of the coil sensor are designed to ensure that the sensor can adapt to the geometric characteristics and detection requirements of the area to be measured.

[0059] Then, excitation measurement is carried out on the coil sensor. An alternating current is applied through the excitation coil to generate an alternating magnetic field with a parallel or fan-shaped distribution in the measurement space. The frequency of the alternating magnetic field can be adjusted in multiple frequency bands according to the detection requirements to obtain richer electromagnetic response information. Secondly, the receiving coil is used to detect the secondary magnetic field generated after the cable accessory modulates the alternating magnetic field. The receiving coils are arranged in an array to ensure that all directions of the area to be measured can be covered, and multi-dimensional electromagnetic field data can be obtained. Finally, through the methods of multi-frequency excitation and multi-point detection, the electromagnetic field data of the area to be measured are collected, and the data are preprocessed and inversely calculated to extract the electromagnetic characteristic parameters of the cable accessory.

[0060] Furthermore, the collected electromagnetic field data are used to reconstruct the conductivity distribution image of the layered structure of the cable accessory in the measurement space through an inversion algorithm. The conductivity distribution image can intuitively reflect the changes in the electromagnetic characteristics of different structural media of the cable accessory. By analyzing the conductivity distribution characteristics, the abnormal area of the cable accessory can be identified.

[0061] Finally, through the multi-modal data fusion algorithm, the temperature anomaly characteristics of infrared thermal imaging and the conductivity anomaly characteristics of conductivity distribution are comprehensively analyzed to extract multi-dimensional characteristics of the defect, realizing the accurate identification and positioning of the cable accessory defect.

[0062] To sum up, this solution realizes a phased detection process from large-range rapid scanning to local refined detection by combining infrared thermal imaging technology and multi-modal non-invasive imaging technology. Infrared thermal imaging technology can quickly locate the temperature anomaly area of the cable accessory, avoiding the low efficiency problem of comprehensively detecting the entire cable accessory in traditional methods and significantly improving the detection efficiency.

[0063] This solution uses multi-modal non-invasive imaging technology. Through the methods of multi-frequency excitation and multi-point detection, rich electromagnetic field data are obtained. Based on electromagnetic field theory, the distribution of the electromagnetic field can be described by Maxwell's equations:

[0064]

[0065] E is the electric field strength, H is the magnetic field strength, B is the magnetic induction intensity, D is the electric displacement vector, J is the current density, and ρ is the free charge volume density. At the same time, combined with the inversion algorithm, the conductivity distribution image of the cable accessory is reconstructed. The inversion problem can be expressed as:

[0066] σ(r)=argminσ ‖‖M(σ) - M means ‖‖ 2 + λR(σ)

[0067] where σ(r) is the conductivity distribution, M(σ) is the electromagnetic field data predicted by the model, M means is the measured data, λ is the regularization parameter, and R(σ) is the regularization term.

[0068] It is this multi-dimensional and multi-physical quantity detection method that can more accurately identify the type, location, and severity of defects, effectively avoiding the limitations of single detection techniques. Moreover, the designed coil sensor can adjust the number of turns and size according to the actual application scenario, and is suitable for detecting cable accessories of different specifications and shapes.

[0069] In summary, this solution adopts a non-contact detection method, avoiding the electric field interference and safety risks that may be brought by electrode contact in traditional methods. It is especially suitable for on-line detection of high-voltage cable accessories, ensuring the safety and reliability of the detection process.

[0070] Embodiment 2

[0071] In this embodiment, a non-destructive cable defect detection system based on multi-modal non-invasive imaging is applied. The system includes an infrared imaging module, a multi-modal imaging module, a signal acquisition card, a front-end sensor, and a back-end processor;

[0072] The infrared imaging module is used to perform a full-coverage line scan on the cable line through infrared thermal imaging technology to obtain the cable surface temperature distribution and temperature anomaly areas;

[0073] The front-end sensor is used on the one hand to generate an alternating magnetic field to cause the magnetic permeability change of the conductive medium in the cable in the magnetic field, and on the other hand to collect electromagnetic response signals;

[0074] The multi-modal imaging module is used to reconstruct the conductivity distribution image inside the cable based on the electromagnetic response signals using an image reconstruction algorithm;

[0075] The back-end processor is used to judge the type and location of internal cable faults by combining the cable surface temperature distribution and the conductivity distribution image inside the cable with multi-modal feature correlation analysis;

[0076] The signal acquisition card is used to transmit the signals collected by the front-end sensor, the infrared imaging module, and the multi-modal imaging module to the back-end processor.

[0077] In this embodiment, for high-voltage cables with long laying distances and complex and changeable operating environments, a multi-level detection method is adopted. A coil sensor is designed to measure by utilizing the symmetry of the receiving sensor, improving the sensitivity of the sensor to weak conductivity changes inside the cable and ensuring high-precision cable defect detection.

[0078] In this embodiment, first, an infrared thermal sensor is used to perform a large-scale scan of the cable accessories in operation. After obtaining the temperature distribution data of the measured area, through the temperature distribution characteristics, the overheating abnormal areas that may have defects are initially identified. Infrared thermal imaging technology can quickly locate the local overheating parts of the cable accessories, providing a target area for subsequent refined detection.

[0079] In this embodiment, the non-invasive detection system is divided into three main parts. Its hardware components include a signal acquisition card, a front-end sensor array (8 channels), and a back-end processor. The front-end sensor array is initially configured with 8 channels, which collects the conductivity distribution information of the cable body and converts it into a voltage signal for transmission to the signal acquisition card. The signal acquisition card is responsible for converting the external analog signal into a digital signal and transmitting it to the back-end processor. The back-end software processes the sensor data from the acquisition card through a series of algorithms, further analyzes the defect location and nature inside the cable, and finally realizes high-precision defect detection and imaging.

[0080] In addition, the detection system of this solution includes an infrared thermal imaging module and a multi-modal imaging module. The phased and multi-level comprehensive detection method can complete the whole process from data acquisition to defect recognition, and finally output the defect location of the cable accessories, effectively coping with complex scenarios in practical applications.

[0081] In this embodiment, the steps for non-destructive detection of cable defects using this system are as follows:

[0082] Step 1: Use an infrared thermal sensor to perform a large-scale scan of the cable accessories to obtain the temperature distribution data of the measured area. By analyzing the temperature distribution characteristics, initially identify the overheating abnormal areas that may have defects. Infrared thermal imaging technology can quickly locate the local overheating parts of the cable accessories, providing a target area for subsequent refined detection.

[0083] Step 2: For the temperature abnormal areas of the cable accessories found in the infrared detection in Step 1, use non-invasive imaging technology for refined detection, and design the number of turns and size of the coil sensor according to the actual application scenario.

[0084] Step 3: Excite and measure the designed coil sensor in Step 2. First, apply an alternating current through the excitation coil to generate an alternating magnetic field with a parallel or fan-shaped distribution in the measurement space. The frequency of the alternating magnetic field can be adjusted in multiple frequency bands according to the detection requirements to obtain richer electromagnetic response information. Second, use the receiving coil to detect the secondary magnetic field generated after the cable accessory modulates the alternating magnetic field. The receiving coils are arranged in an array to ensure that all directions of the area to be measured can be covered and multi-dimensional electromagnetic field data can be obtained. Finally, collect the electromagnetic field data of the area to be measured through multi-frequency excitation and multi-point detection, and perform preprocessing and inversion calculations on the data to extract the electromagnetic characteristic parameters of the cable accessory.

[0085] Step 4: Based on the electromagnetic field data collected in Step 3, reconstruct the conductivity distribution image of the layered structure of the cable accessory in the measurement space through an inversion algorithm. The conductivity distribution image can intuitively reflect the changes in the electromagnetic characteristics of different structural media of the cable accessory. By analyzing the conductivity distribution characteristics, identify the abnormal areas of the cable accessory.

[0086] In this embodiment, the schematic diagram of the complete cable detected by the non-invasive imaging technology is as Figure 5 shown, while the schematic diagram of the cable defect is as Figure 6 shown. It can be inferred from the figure that it shows the distribution of the defect degrees at different positions of the cable; the colors in the figure represent different numerical magnitudes, and the color bar on the right side of each figure is the corresponding scale. The redder areas in the figure indicate a greater possibility of cable defects or more serious defect degrees, while the bluer areas indicate relatively normal conditions.

[0087] To sum up, this solution innovatively combines infrared detection and multi-modal non-invasive imaging technology. The core of the system is to use an electromagnetic sensor as the detection element to accurately measure the conductivity parameters of different structures of the cable accessory based on the principle of electromagnetic induction. The system adopts a non-contact working mode of coil excitation and receiving measurement, effectively avoiding the problem of electric field interference that may be caused by electrode contact. In order to obtain as many independent measurement data as possible, the system will adopt a measurement method of multi-frequency excitation and multi-point detection, and arrange multiple sensors around the area to be measured to form an array layout. In practical applications, first, the system uses an infrared thermal sensor to perform a large-scale scan of the cable accessory to initially locate the area with abnormal heat generation. Second, use non-invasive imaging technology to perform refined detection on the abnormal part, so as to realize the multi-structure defect detection of the cable accessory.

[0088] As described above, it 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 all 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 non-destructive testing method for cable defects based on multi-modal non-invasive imaging, characterized in that The method steps include: S1. Implement full-coverage line scanning of the cable line through infrared thermal imaging technology to obtain the surface temperature distribution of the cable and the temperature abnormal area; S2. Set up an electromagnetic sensor array at the corresponding position of the cable surface temperature abnormal area; S3. Excite the electromagnetic sensor to generate an alternating magnetic field, cause the conductivity medium in the cable to generate a change in magnetic permeability in the magnetic field, and use the electromagnetic sensor to collect electromagnetic response signals; S4. Based on the electromagnetic response signals, use an image reconstruction algorithm to reconstruct the conductivity distribution image inside the cable; S5. According to the cable surface temperature distribution and the conductivity distribution image inside the cable, combined with multi-modal feature correlation analysis, thereby judge the internal fault type and fault point of the cable, and realize non-destructive detection of cable defects.

2. The non-destructive detection method for cable defects based on multi-modal non-invasive imaging according to claim 1, characterized in that, The specific process of S1 is: First, use a high-precision thermal imager to perform full-line scanning of the cable surface, capture the local abnormal temperature rise phenomenon of the cable, thereby output far-infrared radiation information, then generate a temperature distribution map according to the far-infrared radiation information, and determine the abnormal area through a temperature threshold; During the process of using the high-precision thermal imager to perform full-line scanning of the cable surface, dynamic temperature difference compensation is performed.

3. A non-destructive testing method for cable defects based on multimodal non-invasive imaging according to claim 1, characterized in that, The electromagnetic sensor array in S2 is specifically eight electromagnetic sensors composed of coils, arranged in an equally spaced circular pattern. Among the eight coils, one is an excitation coil and the others are receiving coils.

4. The non-destructive detection system for cable defects based on multi-modal non-invasive imaging according to claim 3, characterized in that, The excitation coil is connected to a signal excitation circuit, and the receiving coil is connected to a signal acquisition circuit.

5. A non-destructive testing method for cable defects based on multi-modal non-invasive imaging according to claim 3, characterized in that, The electromagnetic sensor array is closely attached to the cable surface.

6. The non-destructive detection method for cable defects based on multi-modal non-invasive imaging according to claim 3, characterized in that, The working mode of the electromagnetic sensor array includes single-frequency excitation and multi-frequency scanning.

7. A non-destructive testing method for cable defects based on multi-modal non-invasive imaging according to claim 1, characterized in that The specific process of S3 is: First, apply an alternating current to the excitation coil to generate an alternating magnetic field distributed in parallel or fan shape inside the cable; Then, use the receiving coil to detect the secondary magnetic field generated after the cable modulates the alternating magnetic field; Finally, through the method of multi-frequency excitation and multi-point detection, obtain the electromagnetic response signals of the cable.

8. A non-destructive detection method for cable defects based on multi-modal non-invasive imaging according to claim 1, characterized in that, The specific expression for reconstructing the conductivity distribution image inside the cable in S4 is: σ(r) = argmin σ ||M(σ) - M means || 2 + λR(σ) where σ(r) is the conductivity distribution, M(σ) is the electromagnetic field data predicted by the model, M means is the electromagnetic response signal, λ is the electromagnetic response signal, and R(σ) is the regularization term.

9. A non-destructive detection method for cable defects based on multi-modal non-invasive imaging according to claim 1, characterized in that, The specific process of combining multi-modal feature correlation analysis in S5 is: First, obtain the cable surface temperature field distribution data and the internal conductivity distribution data, locate the co-occurrence area of temperature abnormality and conductivity abnormality through correlation analysis, and realize precise positioning of defects; Subsequently, use imaging technology to perform three-dimensional reconstruction of the defect structure, and finally realize non-destructive precise detection of the cable; Among them, the multi-modal features include the cable surface temperature field information obtained by infrared thermal imaging technology and the cable internal conductivity distribution data obtained by electromagnetic imaging technology.

10. A non-destructive testing system for cable defects based on multi-modal non-invasive imaging, characterized in that, The system works by using a cable accessory defect detection method based on multi-modal non-invasive imaging as described in any one of claims 1-9. The system includes an infrared imaging module, a multi-modal imaging module, a signal acquisition card, a front-end sensor, and a back-end processor; The infrared imaging module is used to implement full-coverage line scanning of the cable line through infrared thermal imaging technology to obtain the surface temperature distribution of the cable and the temperature abnormal area; The front-end sensor is used, on the one hand, to generate an alternating magnetic field, causing the conductive medium in the cable to produce a change in magnetic permeability in the magnetic field, and on the other hand, to collect electromagnetic response signals; The multi-modal imaging module is used to reconstruct the conductivity distribution image inside the cable based on the electromagnetic response signals using an image reconstruction algorithm; The back-end processor is used to determine the internal fault type and fault point of the cable by combining the multi-modal feature correlation analysis according to the surface temperature distribution of the cable and the conductivity distribution image inside the cable; The signal acquisition card is used to transmit the signals collected by the front-end sensor, the infrared imaging module and the multi-modal imaging module to the back-end processor.

Images