Thermal wave tomography method and device for polymer insulating material

Abstract

The present invention proposes a thermal wave tomography method and device for polymer insulating materials, which realize fast, non-destructive, high-resolution imaging of the internal structure of the material. The method mainly comprises: first, using the logarithmic frequency modulation method to perform photothermal excitation on the material, concentrating the low-frequency band energy to enhance the deep thermal wave energy. Then, using an infrared thermal imager to collect the infrared thermal image sequence of the thermal diffusion process, and obtain the thermal response signal. Then, by adjusting the stop frequency of the frequency scanning parameter, the frequency difference is introduced, the infrared thermal image sequence is processed, the phase information is calculated, the phase image of a specific frequency is selected as the tomogram, and the depth information is associated. Finally, a super-resolution algorithm is applied to deblur the characteristic thermal map, generate a three-dimensional tomogram, and realize high-resolution imaging. The thermal wave tomography of the present invention has the characteristics of fast, non-destructive, high-resolution, non-contact, large-area, and visualization, and is suitable for the detection of polymer insulating materials.

Description

Technical Field

[0001] The present invention relates to the technical field of nondestructive testing of electric power equipment, and in particular to a thermal wave tomography method and device for polymer insulating materials. Background Art

[0002] Polymer insulation materials, with their excellent insulating properties, mechanical strength, and stable physical and chemical properties, play a vital role in numerous areas of modern industry. Many electrical equipment in power grids, such as insulators, capacitors, and reactors, utilize polymer insulation materials. However, during manufacturing and operational service, polymer insulation materials are subject to numerous factors, such as high temperature, high pressure, impurities, and high humidity, which can lead to material defects. The further development of these defects can affect the overall performance of the polymer insulation material and even lead to serious accidents such as power equipment failure. Practical engineering practice has necessitated the rapid and effective identification of material defects. Compared to two-dimensional imaging, three-dimensional tomography provides more intuitive results and can obtain more accurate defect information, including depth and size. Therefore, employing appropriate nondestructive testing techniques to perform rapid and intuitive tomographic imaging of polymer insulation materials and obtain effective structural information within them is a current research hotspot and future development trend in polymer insulation material testing.

[0003] Currently, the main methods for tomographic imaging of polymer insulating materials are X-ray tomography and ultrasonic tomography. These methods usually have certain limitations when responding to actual engineering needs. Among them, X-ray tomography relies on X-rays to irradiate the sample from multiple angles, which takes a long time to detect, the detection equipment is expensive, and attention must be paid to radiation protection. In addition, the samples tested are often small in size. Ultrasonic tomography requires an ultrasonic probe to be in close contact with the surface of the polymer insulating material through a coupling agent, collects reflected echoes, and uses a cross-sectional image reconstruction method to detect the internal structure of the material. This method requires contact offline detection, usually point-to-point scanning detection, and the detection rate is slow.

[0004] Compared with the above two detection methods, thermal wave tomography has the advantages of large detection area, fast detection speed and non-contact detection. The principle of this technology is to apply photothermal excitation to the material (usually using pulse linear frequency modulation), collect the infrared thermal image sequence during the heat loading process through an infrared thermal imager, and use the cross-correlation matching algorithm on the time-delayed excitation signal to obtain truncated images at different depths. However, due to the low thermal conductivity of polymer insulating materials, the attenuation of thermal waves in them is large. The existing thermal wave tomography method is difficult to be effectively applied in the tomography of polymer insulating materials. It has the limitations of low depth resolution, low lateral resolution and low detection depth.

[0005] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology, provide a thermal wave tomography method and device for polymer insulating materials, and solve the problems of low imaging resolution and detection accuracy in existing technology.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A thermal wave tomography method for polymer insulating materials comprises the following steps:

[0009] A1. Use logarithmic frequency modulation to photothermally excite polymer insulating materials. During the sweep from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to follow a logarithmic function over time, concentrating the energy in the low-frequency range to enhance the thermal wave energy in this region.

[0010] A2. Use an infrared thermal imager to capture a sequence of infrared thermal images of the thermal diffusion process caused by photothermal excitation to obtain the thermal response signal of the material's internal structure;

[0011] A3. Perform signal processing on the acquired infrared thermal image sequence. Frequency differences are introduced by adjusting the end frequency of the frequency scanning parameters during the data processing phase to obtain transformed signals at different frequencies. The set start frequency and the adjusted end frequency generate a series of transformed signals with different frequency differences. Phase information of the transformed signals is calculated, and the phase image at a specific frequency corresponding to the average of the start and end frequencies is selected as a tomogram. Based on the relationship between frequency and material depth, the characteristic thermal image is associated with the corresponding depth information.

[0012] A4. Use a super-resolution algorithm to perform deblurring calculation on the feature heat map to obtain a super-resolution feature heat map, and perform three-dimensional tomography based on the super-resolution feature heat map.

[0013] Furthermore, in step A1, an array-type laser thermal excitation source is used to load photothermal excitation on the specimen; the array-type laser thermal excitation source includes multiple near-infrared semiconductor laser generators with a wavelength range of 700 to 2500nm, each generator outputs laser light through an optical fiber, and is equipped with a multi-directional motor adjustment system at the output array end to adjust the output of laser arrays of various shapes.

[0014] Furthermore, in step A1, the logarithmic frequency modulated excitation signal conforms to the following equation:

[0015]

[0016] Where Q LogFM represents the logarithmic frequency modulated photothermal excitation function, Q0 is the energy density of the heat source, f s and f e are the FM start and end frequencies, T is the FM sweep time, and t is the signal loading time.

[0017] Furthermore, in step A3, the infrared thermal image sequence collected in step A2 is processed using Chirp-Z transform, and the frequency difference is introduced by changing the termination frequency of the Chirp-Z transform basis function to form a series of Chirp-Z transform signals with different frequency differences. The phase information of the Chirp-Z transform signal is calculated, and the phase diagram of the average value of the starting frequency and the termination frequency is selected as the tomogram.

[0018] Furthermore, step A3 specifically includes:

[0019] Adjust the frequency scanning parameters in the data processing algorithm to change the end frequency, and combine it with the preset start frequency to generate a series of transformed signals with different frequency differences;

[0020] The acquired infrared thermal image sequence is processed using the conversion signals with different frequency differences to obtain a series of conversion signals reflecting the temperature distribution of the internal structure of the material at different depths;

[0021] Calculate the phase information of the transformed signal reflecting the temperature distribution to extract the phase change of the signal at different frequencies;

[0022] From the calculated phase information, a phase map of a specific frequency corresponding to the average of the starting frequency and the ending frequency is selected as a tomogram;

[0023] Exploiting the relationship between frequency and material depth, the selected phase map is correlated with the depth information to generate a three-dimensional tomogram.

[0024] Furthermore, step A4 specifically includes:

[0025] By measuring the relevant thermophysical parameters of the material and performing simulation calculations, the thermal diffusion fuzzy function of the material is obtained;

[0026] Deconvolution algorithm is used to deblur the infrared thermal image to improve the lateral resolution of the characteristic thermal image;

[0027] The characteristic heat map processed by the heat diffusion blur function and the deconvolution algorithm is combined to generate a high-resolution three-dimensional tomogram.

[0028] The fuzzy function of material thermal diffusion satisfies the following form:

[0029]

[0030] Where ρ, C, and α are the density, specific heat capacity, and thermal conductivity of the material, respectively; a0 is a variable parameter obtained by fitting the simulation data; r represents the internal position of the material; and t represents the thermal diffusion time.

[0031] Furthermore, in step A4, a microscope lens is first used to magnify the image captured by the infrared thermal imager;

[0032] The method further includes: measuring the size of the sample and calculating the actual size represented by each pixel based on camera parameters and experimental configuration, the calculation method being as follows:

[0033]

[0034] Where p is the pixel size, l T is the detection distance, f is the focal length, and a1 is the actual size represented by the pixel of the acquired image;

[0035] Configure the corresponding microscope lens according to the size of the sample, the actual size represented by each pixel and the required measurement accuracy.

[0036] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the thermal wave tomography method of polymer insulating materials.

[0037] A thermal wave tomography device of a polymer insulating material, comprising:

[0038] Sample stage, used to place samples;

[0039] The semiconductor laser array excitation source consists of multiple near-infrared laser generators, which can control the size, position, power and waveform of the heating spot, and adapt to samples of different shapes and sizes through a multi-directional motor adjustment system;

[0040] Excitation source control system, used to set and control parameters of the semiconductor laser array excitation source, and synchronously trigger the operation of the excitation source and thermal wave imaging system;

[0041] Thermal wave imaging system, using medium and long wave infrared thermal imagers, is used to collect thermal image sequences of the sample surface and convert them into electrical signals for storage and analysis;

[0042] Microscopic imaging system, which magnifies the image collected by the infrared thermal imager through a microscope lens;

[0043] A post-processing computing system is used to perform super-resolution imaging, feature extraction, and 3D tomography generation algorithms, thereby achieving high-resolution, high-precision tomography of the internal structure of polymer insulation materials.

[0044] The present invention has the following beneficial effects:

[0045] The present invention proposes a thermal wave tomography method and apparatus for polymer insulating materials, enabling rapid, non-destructive, high-resolution, non-contact, large-area, and visual tomography of the internal structure of polymer insulating materials, thereby effectively detecting, identifying, and locating defects in polymer insulating materials. The present invention constructs a tomography method capable of visualizing the internal structure of polymer insulating materials, and proposes innovative photothermal excitation modulation methods, signal processing methods, and imaging enhancement algorithms, which can effectively achieve high-resolution, high-precision tomography of the internal structure of polymer insulating materials. By constructing a corresponding hardware system and integrating algorithms, the present invention can quickly and intuitively present internal structural information of polymer insulating materials over a long distance with high precision.

[0046] Compared with existing methods, the embodiments of the present invention have made innovations in multiple aspects, such as the arrangement of thermal excitation sources, the modulation method of thermal excitation sources, the post-processing algorithm, and the super-resolution hardware and algorithm, targeting the characteristics of polymer insulating materials. In terms of the arrangement of thermal excitation sources, an array-type laser thermal excitation source is adopted to improve the efficiency, area, and uniformity of thermal excitation loading. In terms of the modulation method of the thermal excitation source, a logarithmic frequency modulation method is adopted to make the deep structural information of the polymer insulating material in tomography imaging more complete, thereby improving the detection accuracy and depth. In terms of the post-processing algorithm, a Chirp-Z transform algorithm with staggered frequency is adopted to obtain tomography images with higher resolution. In terms of super-resolution imaging, the resolution of detection is improved by adding a microscope lens and combining it with a deconvolution algorithm with a function to eliminate thermal diffusion blur. Through the innovations in the above aspects, the present invention overcomes the limitations of thermal wave tomography, improves the detection capability of thermal wave tomography, and ensures the reliability, objectivity, and effectiveness of detection.

[0047] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for detecting polymer insulation materials by thermal wave tomography according to an embodiment of the present invention.

[0049] Figure 2 It is a schematic structural diagram of a detection device for thermal wave tomography of polymer insulating materials according to an embodiment of the present invention.

[0050] Figure 3 1 is a power spectrum density distribution diagram of different frequency modulation methods according to an embodiment of the present invention.

[0051] In the figure: 1 is the excitation source control system, 2 is the control computer, 3 is the laser generator, 4 is the laser fiber, 5 is the semiconductor laser array excitation source, 6 is the thermal wave imaging system, 7 is the infrared thermal imager, 8 is the thermal imager supporting control computer, 9 is the post-processing calculation system, 10 is the microscopic imaging system, 11 is the polymer insulating material to be tested, and 12 is the sample stage. DETAILED DESCRIPTION

[0052] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0053] During the production, transportation, storage and service of polymer insulating materials, defects may occur inside them due to the influence of many factors such as process, environment, and human operation. If the defects are allowed to develop, they may lead to safety accidents such as material failure and equipment failure, which will have a serious impact on the safety and stability of industrial systems. Therefore, timely detection, identification and positioning of defects are crucial for the safe and stable operation of industrial systems. At present, existing non-destructive testing technologies have certain limitations in the detection of polymer insulating materials, and it is difficult to meet the actual needs of long-distance, rapid and intuitive visual detection in engineering. Thermal wave tomography has become a potential solution. However, the detection resolution of existing thermal wave tomography technology in polymer insulating materials is low, which makes it difficult to meet the actual needs of engineering projects.

[0054] The present invention proposes a thermal wave tomography method and device for polymer insulating materials, which realizes rapid, non-destructive, high-resolution, non-contact, large-area, and visual tomography of the internal structure of polymer insulating materials, thereby effectively detecting, identifying and locating defects in polymer insulating materials.

[0055] See Figure 1 , an embodiment of the present invention provides a thermal wave tomography method for polymer insulating materials, comprising the following steps:

[0056] A1. Modulated Thermal Excitation Source: This method uses logarithmic frequency modulation to photothermally excite polymer insulating materials. During the sweep from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to follow a logarithmic function over time, concentrating the energy in the low-frequency range. This enhances the thermal wave energy in this region, thereby improving the depth and resolution of the material's deep structure detection.

[0057] A2. Acquiring an infrared thermal image sequence: Using an infrared thermal imager, acquire an infrared thermal image sequence of the heat diffusion process caused by the photothermal excitation in step A1 to obtain the thermal response signal of the material's internal structure;

[0058] A3. Post-processing: Signal processing is performed on the infrared thermal image sequence acquired in step A2. Frequency differences are introduced by adjusting the end frequency of the frequency scanning parameters during the data processing phase to obtain transformed signals at different frequencies. The set start frequency and the adjusted end frequency generate a series of transformed signals with different frequency differences. Phase information of the transformed signals is calculated, and the phase image at a specific frequency corresponding to the average of the start and end frequencies is selected as a tomogram. Based on the relationship between frequency and material depth, the tomogram is used as a characteristic thermal map and associated with the corresponding depth information, thereby improving the depth resolution of internal structural defect detection in the material.

[0059] A4. Super-resolution Imaging and Result Output: A super-resolution algorithm is used to deblur the characteristic heat map processed in step A3 to obtain a super-resolution characteristic heat map. Three-dimensional tomography is then performed based on this heat map to achieve high-resolution, high-precision imaging of the internal structure of the polymer insulating material. The resulting three-dimensional tomogram is then output to demonstrate the material's internal structure and potential defects.

[0060] See Figure 2 An embodiment of the present invention further provides a thermal wave tomography device for polymer insulating materials, comprising: a sample stage 12 for placing a sample; a semiconductor laser array excitation source 5, composed of multiple near-infrared laser generators 3, capable of controlling the size, position, power and waveform of the heating spot, and adapting to samples of different shapes and sizes through a multi-directional motor adjustment system; an excitation source control system 1, for setting and controlling parameters of the semiconductor laser array excitation source 5, and synchronously triggering the operation of the excitation source and the thermal wave imaging system; a thermal wave imaging system 6, using a medium- and long-wave infrared thermal imager, controlled by a matching control computer 8, for collecting a thermal image sequence of the sample surface and converting it into electrical signals for storage and analysis; a microscopic imaging system 10, for magnifying the image collected by the infrared thermal imager through a microscope lens; and a post-processing computing system 9, for executing super-resolution imaging, feature extraction, and three-dimensional tomography generation algorithms, thereby achieving high-resolution, high-precision tomographic imaging of the internal structure of the polymer insulating material.

[0061] Specific embodiments of the present invention are further described below.

[0062] A device and method for thermal wave tomography of polymer insulating materials. The device is a thermal wave imaging system equipped with a controllable semiconductor laser array. The semiconductor laser array can control the size and position of the heating spot and is connected to an infrared thermal imager via a control system. A microscope lens is used to magnify and identify polymer materials. Subsequently, combined with a post-processing algorithm system, effective tomographic imaging of polymer materials and devices is achieved. The tomographic imaging device consists of six components: a sample stage, a semiconductor laser array excitation source, an excitation source control system, a thermal wave imaging system, a microscopic imaging system, and a post-processing calculation system. These components work together to achieve high-resolution, high-precision tomographic imaging of the internal structure of polymer insulating materials.

[0063] Sample stage. This part is used to place samples. It can be customized according to the sample size and shape. It has an adjustable range of 6 degrees of freedom (height, height, front, back, left, and right) to adjust the sample position to the optimal detection position.

[0064] Semiconductor laser array excitation source. This part is composed of multiple near-infrared (wavelength range of 700 to 2500nm, generally 808nm) semiconductor laser generators, each of which outputs laser light via optical fiber. At the output array end, the laser output optical fibers are arranged in a specific shape to form a laser array of a specific shape. Through the arrangement of the optical fibers, the laser output from this excitation source has a high degree of uniform heating, adjustable spot area, adjustable power, and adjustable waveform. To facilitate the detection of polymer insulating materials and equipment of different shapes and sizes, the output array end of the excitation source is equipped with a six-way motor adjustment system (up, down, left, right, front, and back) to effectively detect polymer insulating materials and equipment of various shapes and sizes.

[0065] Excitation source control system. This section is primarily used to control the semiconductor laser array excitation source, setting and controlling its parameters, and outputting trigger signals to synchronize the excitation source and thermal wave imaging system. Specifically, the excitation source control system controls the power and waveform of the laser output from the semiconductor laser array source, while also enabling six-way adjustment of the excitation source output array. Furthermore, the control system can output trigger signals to the thermal wave imaging system, ensuring that the system can acquire data from the polymer insulation material at the set time.

[0066] Thermal wave imaging system. This part consists of a medium- and long-wave infrared thermal imager and a supporting control computer. The supporting control computer sets the relevant parameters of the infrared thermal imager. The infrared thermal imager converts the infrared radiation emitted by the polymer material into an electrical signal, and converts the temperature distribution of the material into images, text data, video and other information, and stores this information in the supporting control computer. The device must meet multiple functions such as adjustable integration time, adjustable acquisition frequency (range of at least 0 to 200Hz), adjustable acquisition window, adjustable focal length, and fast data transmission. The system can automatically control the acquisition time of the infrared thermal imager through the supporting computer, and can also realize the start and end of the thermal image sequence acquisition by receiving the trigger signal of the excitation source control system. This thermal wave imaging system is usually used to collect the temperature change process and obtain the data of the infrared thermal image sequence.

[0067] The microscopic imaging system, primarily composed of a microscope lens, amplifies the resolution of images captured by the infrared thermal imager, improving the accuracy of the detection and imaging. The principle behind configuring a microscope lens is to first calculate the actual pixel size based on the infrared thermal imager's parameters and the experimental configuration. Then, based on the accuracy required for the experiment, configure a microscope lens that meets the requirements (e.g., ×10 magnification) to improve the resolution of the captured image.

[0068] Post-processing computing system. This part is composed of a high-performance computer with built-in software, and is mainly used to perform subsequent processing on the acquired infrared thermal image sequence, including super-resolution imaging, feature extraction, and three-dimensional tomography generation algorithm. Specifically, the core of the super-resolution algorithm is to obtain the thermal diffusion fuzzy function of the material and obtain the deblurred thermal map through the deconvolution algorithm. The feature extraction algorithm is based on the Chirp-Z transform algorithm of the dislocation frequency to obtain characteristic thermal maps at different depths, and combines it with the super-resolution algorithm to obtain high-resolution characteristic thermal maps. The three-dimensional tomography generation algorithm generates a three-dimensional tomography map from the characteristic thermal maps at different depths, thereby realizing tomographic imaging inside the polymer material.

[0069] The detection method of the embodiment of the present invention can be performed by the system through the following steps:

[0070] Step S1, measure the size of the sample and calculate the actual measured size a1 of the pixel value, as shown in the following formula (11). According to the required measurement accuracy, configure the corresponding microscope lens.

[0071]

[0072] Step S2: Place the polymer insulating material to be tested on the sample stage, adjust the position of the sample stage, and observe the image captured by the infrared thermal imager in real time to ensure that the sample is in the optimal testing position of the infrared thermal imager.

[0073] Step S3: Set the infrared thermal imager's acquisition parameters, including acquisition frequency, window size, integration time, and acquisition time. Set the excitation waveform, power, and loading time of the thermal excitation source. Adjust the position and shape of the excitation source output array end to prepare for acquisition. The excitation of the thermal excitation source should meet the logarithmic frequency modulation method as follows:

[0074]

[0075] In step S4, one of the laser sources in the semiconductor laser array excitation source is triggered, and the infrared thermal imager is simultaneously triggered to obtain the thermal diffusion process after the point laser source heats the material surface. The corresponding thermal diffusion fuzzy equation of the material is fitted using the built-in algorithm and the material's thermophysical parameters. The form of the thermal diffusion fuzzy equation is as follows:

[0076]

[0077] In step S5, after the sample surface temperature returns to normal, an infrared thermal imager is used to capture an initial thermal image of the sample. Subsequently, the semiconductor laser array excitation source and the infrared thermal imager are synchronously triggered to capture the changes in sample surface temperature during the laser loading process, acquiring a sequence of infrared thermal images of the sample surface until the preset detection time is reached.

[0078] Step S6: Process the infrared thermal image sequence to obtain thermal wave tomography at different depths. Apply the Chirp-Z transform algorithm based on the dislocation frequency to each pixel in the infrared thermal image sequence with a frame number of L to obtain the transformed signal X k ,as follows:

[0079]

[0080] z k =AW -k k=0,1,…,L-1 (15)

[0081] A=exp(j2πf0) (16)

[0082]

[0083] The phase image with a frequency of 0.5×(f0+f1) is selected as the tomogram, and the corresponding depth is:

[0084]

[0085] Thermal wave tomograms at different depths were thus obtained.

[0086] In step S7, a super-resolution algorithm is used to deblur these characteristic heat maps in combination with the heat diffusion fuzzy equation to obtain a super-resolution characteristic heat map. A tomographic image of the material is generated using a tomographic imaging algorithm, thereby achieving three-dimensional tomographic imaging of the polymer insulating material.

[0087] The innovation of the present invention is:

[0088] (1) In the above step S3, the present invention proposes a semiconductor laser array excitation source. Compared with the traditional single laser excitation source, the array output end of the laser source increases the area of ​​photothermal excitation loading and can control the size of the thermal excitation loading area. The photothermal excitation loading efficiency is higher and the photothermal excitation loading is more uniform.

[0089] (2) In the above step S3, a logarithmic frequency modulation method is proposed. Compared with the existing linear frequency modulation method, the logarithmic frequency modulation method can concentrate more energy in the low frequency band, thereby achieving high-resolution depth analysis of the deep structure of polymer insulating materials.

[0090] (3) In steps S4 and S7 above, a super-resolution algorithm combining hardware and software is proposed. In terms of hardware, a microscope lens is customized based on the relevant configurations of the infrared thermal imager, material size, and detection distance. In terms of software, a thermal diffusion fuzzy function is obtained based on point laser pre-experiments according to the thermophysical properties of the material. This fuzzy function is then used for deblurring to obtain a high-resolution thermal wave image.

[0091] (4) In the above step S6, a Chirp-Z transform algorithm based on misaligned frequency is proposed. The algorithm changes the frequency corresponding to the basis function so that the basis function frequency and the excitation function frequency are misaligned. The best matching feature heat map is obtained through Z transform, and the obtained feature heat map is associated with the depth through frequency, thereby improving the depth resolution of defect detection.

[0092] The embodiments of the present invention are directed to a thermal wave tomography solution for polymer insulating materials, and the specific innovative designs are embodied in the following aspects:

[0093] (1) Hardware layout of thermal excitation source.

[0094] Compared with the existing single-fiber photothermal laser excitation source, the present invention uses an array of laser thermal excitation sources to load the specimen, which improves the efficiency of photothermal excitation loading, increases the area of ​​photothermal excitation loading, and makes photothermal excitation loading more uniform.

[0095] (2) Modulation method of thermal excitation source.

[0096] Compared with the traditional linear frequency modulation method, the present invention adopts a nonlinear frequency modulation method with logarithmic frequency variation (hereinafter referred to as "logarithmic frequency modulation") for photothermal excitation according to the actual size of the detection material. The excitation signal of the logarithmic frequency modulation conforms to the following equation:

[0097]

[0098] Where Q LogFM represents the logarithmic frequency modulated photothermal excitation function, Q0 is the energy density of the heat source, f s and f e are the FM start and end frequencies, T is the FM sweep time, and t is the signal loading time.

[0099] Since thermal waves have frequency domain characteristics, thermal waves of different frequencies correspond to different detection depths, as shown in the following formula:

[0100]

[0101] In the formula, α is the thermal conductivity of the material, f is the frequency of the thermal wave, and μ is the depth of thermal diffusion. It can be seen that the depth of thermal diffusion is inversely proportional to the square root of the thermal wave. Low-frequency thermal waves can detect deeper depths and reflect deeper information. Compared with linear frequency modulation, logarithmic frequency modulation can concentrate energy in the low-frequency band. The power spectrum density distribution diagram at different frequencies is shown as follows: Figure 3 As shown in Figure 2, logarithmic FM has more concentrated energy in the low frequency band.

[0102] Therefore, the use of logarithmic frequency modulation method can make the energy of excitation modulation more concentrated in the low frequency band, thereby performing more refined detection of the deep structure of polymer insulating materials and improving the detection accuracy and depth of the materials.

[0103] (3) Optimization of post-processing algorithms.

[0104] The previous algorithm is to cross-correlate the collected thermal response signal with the time-delayed excitation signal, and obtain material tomography results at different depths through different time delay values. However, there is no clear correspondence between the thermal wave signal and the propagation depth in the time domain. Cross-correlation matching of the time-delayed excitation signal and the thermal response signal cannot obtain the best matching value that is strongly correlated with the depth; and the thermal wave signal is also severely attenuated in the time domain, so the corresponding material depth information will be blurred. According to the frequency characteristics of thermal waves, the present invention proposes a Chirp-Z transform algorithm using staggered frequency. By changing the frequency of the basis function, the best matching feature heat map after the Z transform is obtained, and the obtained feature heat map is associated with the depth through frequency, thereby improving the depth resolution of defect detection.

[0105] The staggered frequency is used to obtain the frequency difference by changing the termination frequency of the Chirp-Z transform basis function under the premise of keeping the key parameters of the FM signal such as the initial frequency and scanning time unchanged. The Chirp-Z transform is performed on the discrete signal x(n) of length L, and the obtained signal X k It can be expressed as follows:

[0106]

[0107] Where:

[0108] z k =AW -k k=0,1,…,L-1 (4)

[0109] A=exp(j2πf0) (5)

[0110] W=exp(-jθ) (6)

[0111]

[0112] Where z k is the basis function of Chirp-Z transform, f0 and f1 are the starting and ending frequencies respectively. k The Chirp-Z transform signal X at different frequencies can be obtained by using the stop frequency f1. k , calculate X k The phase information in the image is selected as the tomogram with a frequency of 0.5×(f0+f1), and the corresponding depth is obtained according to the relationship between frequency and depth. The relationship between frequency difference and depth is as follows:

[0113]

[0114] Therefore, by changing the frequency difference, a thermal tomogram at a specific depth can be obtained, and the resolution of the tomographic depth can be controlled by the magnitude of the frequency difference.

[0115] (4) Hardware and algorithms for super-resolution imaging.

[0116] In order to improve imaging accuracy, we made corresponding arrangements in both hardware and algorithms. In terms of hardware, we calculated the actual size represented by each pixel based on the camera parameters and experimental configuration. The calculation method is as follows:

[0117]

[0118] Where p is the pixel size, l T is the detection distance, f is the focal length, and a1 is the actual size represented by the pixels in the acquired image. The appropriate microscope lens can be configured based on the size of the test piece and the desired accuracy.

[0119] In terms of algorithms, by measuring relevant parameters of a specific material and fitting them through simulation calculations, the fuzzy function of the material's thermal diffusion is obtained. The fuzzy function satisfies the following form:

[0120]

[0121] Where ρ, C, and α are the density, specific heat capacity, and thermal conductivity of the material, respectively, and a0 is a variable parameter obtained by fitting the simulation data.

[0122] By obtaining relevant material parameters and fitting the simulation results, we can obtain the material's thermal diffusion fuzziness function. Based on this thermal diffusion fuzziness function, we use a deconvolution algorithm to deblur the infrared thermal image and improve the lateral resolution of the characteristic thermal image.

[0123] Leveraging these multiple innovations, the present invention effectively achieves high-resolution, high-precision tomographic imaging of polymer insulating materials. By building a corresponding hardware system and employing algorithms, the present invention can remotely, accurately, quickly, and intuitively visualize the internal structure of polymer insulating materials.

[0124] Example

[0125] Thermal wave tomography detection method for polymer insulation materials Figure 1 As shown, the corresponding detection device is as Figure 2 The detection process includes:

[0126] Step S1: Measure the sample size and configure the microscope lens according to the required measurement accuracy.

[0127] Step S2: Place the sample on the sample stage and adjust the sample to the optimal detection position.

[0128] Step S3: Setting relevant parameters of the infrared thermal imager and the thermal excitation source.

[0129] Step S4: using a point laser source in combination with a built-in algorithm to obtain the thermal diffusion fuzzy function of the material.

[0130] Step S5, triggering the semiconductor laser array excitation source and the infrared thermal imager to collect data on the temperature change process of the sample surface.

[0131] Step S6: extract features from the acquired infrared thermal image sequence using a feature extraction algorithm to obtain thermal wave tomography images at different depths.

[0132] Step S7: Use a super-resolution algorithm to obtain a super-resolution feature heat map and perform three-dimensional tomography.

[0133] Compared with the prior art, the present invention has the following advantages:

[0134] During the production, transportation, storage, and service life of polymer insulation materials, defects may develop due to numerous factors, including process, environment, and human intervention. If these defects are allowed to develop, they can lead to safety incidents such as material failure and equipment malfunction, severely impacting the safety and stability of industrial systems. Therefore, timely detection, identification, and location of defects are crucial for the safe and stable operation of industrial systems. Currently, existing nondestructive testing technologies for polymer insulation materials have limitations, making them difficult to meet the practical needs of long-range, rapid, and intuitive visual inspection in engineering projects. Thermal wave tomography offers a potential solution. However, existing thermal wave tomography technologies for polymer insulation materials suffer from low resolution, making them difficult to meet practical engineering requirements. This invention proposes a thermal wave tomography device and method for polymer insulation materials. Compared to existing methods, this invention incorporates innovations in multiple aspects, including thermal excitation source layout, thermal excitation source modulation methods, post-processing algorithms, and super-resolution hardware and algorithms, targeting the unique characteristics of polymer insulation materials. In terms of thermal excitation source layout, an array of laser thermal excitation sources is used to improve the efficiency, area, and uniformity of thermal excitation loading. In terms of the modulation method of the thermal excitation source, a logarithmic frequency modulation method is used to make the deep structural information of the polymer insulating material in the tomography imaging more complete, thereby improving the detection accuracy and depth. In terms of the post-processing algorithm, a Chirp-Z transform algorithm with offset frequency is used to obtain tomography images with higher resolution. In terms of super-resolution imaging, the resolution of the detection is improved by adding a microscope lens and combining it with a deconvolution algorithm that eliminates the thermal diffusion blur function. Through the above innovations, the present invention overcomes the limitations of thermal wave tomography, improves the detection capability of thermal wave tomography, and ensures the reliability, objectivity and effectiveness of the detection.

[0135] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0136] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0137] An embodiment of the present invention further provides a processor, which executes a computer program and at least performs the method described above.

[0138] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a magnetic disk memory or a magnetic tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0139] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0140] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0142] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0143] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0144] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0145] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0146] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0147] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that, without departing from the scope of the present invention, several equivalent substitutions or obvious variations can be made, and the performance or use of the same should be considered to fall within the scope of protection of the present invention.

Claims

1. A thermal wave tomography method for polymer insulating materials, characterized in that: The following steps are involved: A1. Use a logarithmic frequency modulation method to perform photothermal excitation on a polymer insulating material. During the sweep from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to follow a logarithmic function over time, thereby concentrating the energy in the low-frequency range and enhancing the thermal wave energy in the low-frequency region. In step A1, the logarithmic frequency modulation excitation signal conforms to the following equation: ； Where, Q LogFM represents the logarithmic frequency modulated photothermal excitation function, Q 0 is the energy density of the heat source, f s and f e are the start and end frequencies of FM, T is the FM sweep time, t is the signal loading time; A2. Use an infrared thermal imager to capture a sequence of infrared thermal images of the thermal diffusion process caused by photothermal excitation to obtain the thermal response signal of the material's internal structure; A3. Perform signal processing on the acquired infrared thermal image sequence. Frequency differences are introduced by adjusting the end frequency of the frequency scanning parameters during the data processing phase to obtain transformed signals at different frequencies. The set start frequency and the adjusted end frequency generate a series of transformed signals with different frequency differences. Phase information of the transformed signals is calculated, and the phase image at a specific frequency corresponding to the average of the start and end frequencies is selected as a tomogram. Based on the relationship between frequency and material depth, the characteristic thermal image is associated with the corresponding depth information. A4. Use a super-resolution algorithm to perform deblurring calculation on the feature heat map to obtain a super-resolution feature heat map, and perform three-dimensional tomography based on the super-resolution feature heat map.

2. The thermal wave tomography method of polymer insulation material according to claim 1, characterized in that: In step A1, an array of laser thermal excitation sources is used to apply photothermal excitation to the specimen; The array-type laser thermal excitation source includes multiple near-infrared semiconductor laser generators with a wavelength range of 700 to 2500nm. Each generator outputs laser light through an optical fiber, and a multi-directional motor adjustment system is provided at the output array end to adjust the output of laser arrays of various shapes.

3. The thermal wave tomography method of polymer insulation material according to any one of claims 1 to 2, characterized in that: In step A3, the infrared thermal image sequence collected in step A2 is processed using Chirp-Z transform. The frequency difference is introduced by changing the end frequency of the Chirp-Z transform basis function to form a series of Chirp-Z transform signals with different frequency differences. The phase information of the Chirp-Z transform signal is calculated, and the phase diagram of the average value of the starting frequency and the end frequency is selected as the tomogram.

4. The thermal wave tomography method of polymer insulation material according to any one of claims 1 to 2, characterized in that: Step A3 specifically includes: Adjust the frequency scanning parameters in the data processing algorithm to change the end frequency, and combine it with the preset start frequency to generate a series of transformed signals with different frequency differences; The acquired infrared thermal image sequence is processed using the conversion signals with different frequency differences to obtain a series of conversion signals reflecting the temperature distribution of the internal structure of the material at different depths; Calculate the phase information of the transformed signal reflecting the temperature distribution to extract the phase change of the signal at different frequencies; From the calculated phase information, a phase map of a specific frequency corresponding to the average of the starting frequency and the ending frequency is selected as a tomogram; Exploiting the relationship between frequency and material depth, the selected phase map is correlated with the depth information to generate a three-dimensional tomogram.

5. The thermal wave tomography method for polymer insulation materials according to any one of claims 1 to 2, characterized in that: Step A4 specifically includes: By measuring the relevant thermophysical parameters of the material and performing simulation calculations, the thermal diffusion fuzzy function of the material is obtained; Deconvolution algorithm is used to deblur the infrared thermal image to improve the lateral resolution of the characteristic thermal image; The characteristic heat map processed by the heat diffusion blur function and the deconvolution algorithm is combined to generate a high-resolution three-dimensional tomogram.

6. The thermal wave tomography method of polymer insulation material according to claim 5, characterized in that: The fuzzy function of material thermal diffusion satisfies the following form: ； Where, ρ 、 C and α are the density, specific heat capacity and thermal conductivity of the material, a 0 is a variable parameter obtained by fitting the simulation data. r Indicates the internal position of the material, t Indicates the thermal diffusion time.

7. The thermal wave tomography method for polymer insulation materials according to any one of claims 1 to 2, characterized in that: In step A4, a microscope lens is first used to magnify the image captured by the infrared thermal imager; The method further includes a process of pre-configuring the microscope lens, specifically comprising: Measure the size of the sample and calculate the actual size represented by each pixel based on the infrared thermal imager camera parameters and experimental configuration. The calculation method is as follows: ； Where, p is the pixel size, l T is the detection distance, f is the focal length, a 1 It is the actual size represented by the pixels of the acquired image; Configure the corresponding microscope lens according to the size of the sample, the actual size represented by each pixel and the required measurement accuracy.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the thermal wave tomography method for polymer insulation materials according to any one of claims 1 to 7 is implemented.

9. A thermal wave tomography device for polymer insulating materials based on the thermal wave tomography method according to any one of claims 1 to 7, characterized in that: include: Sample stage, used to place samples; The semiconductor laser array excitation source consists of multiple near-infrared laser generators, which can control the size, position, power and waveform of the heating spot, and adapt to samples of different shapes and sizes through a multi-directional motor adjustment system; Excitation source control system, used to set and control parameters of the semiconductor laser array excitation source, and synchronously trigger the operation of the excitation source and thermal wave imaging system; Thermal wave imaging system, using medium and long wave infrared thermal imagers, is used to collect thermal image sequences of the sample surface and convert them into electrical signals for storage and analysis; Microscopic imaging system, which magnifies the image collected by the infrared thermal imager through a microscope lens; A post-processing computing system is used to perform super-resolution imaging, feature extraction, and 3D tomography generation algorithms, thereby achieving high-resolution, high-precision tomography of the internal structure of polymer insulation materials.

Images