Thermal wave tomography method and device for polymer insulating material

Abstract

The invention provides a thermal wave tomography method and a thermal wave tomography device for a polymer insulating material, which are used for realizing rapid, lossless and high-resolution imaging of an internal structure of the material. The method mainly comprises the following steps: firstly, carrying out photo-thermal excitation on a material by adopting a logarithmic frequency modulation method, and concentrating low-frequency-band energy to enhance deep thermal wave energy; then, an infrared thermal imager is used for collecting an infrared thermogram sequence in the thermal diffusion process, and a thermal response signal is obtained; then, by adjusting the termination frequency of frequency scanning parameters, introducing a frequency difference, processing an infrared thermogram sequence and calculating phase information, selecting a phase diagram with a specific frequency as a chromatogram, and associating depth information; and finally, carrying out deblurring processing on the characteristic heat map by using a super-resolution algorithm to generate a three-dimensional tomogram and realize high-resolution imaging. The thermal wave tomography disclosed by the invention has the characteristics of rapidness, no damage, high resolution, non-contact, large area and visualization, and is suitable for detecting the polymer insulating material.

Description

Technical Field

[0001] The 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 insulating materials play an important role in many fields of modern industry with their excellent insulation performance, mechanical strength and stable physical and chemical properties. Many power equipment in the power grid use polymer insulating materials, such as insulators, capacitors, reactors, etc. However, polymer insulating materials are affected by many factors during the production and operation of polymer insulating materials, such as high temperature, high pressure, impurities, high humidity, etc., which may cause defects in the materials. The further development of defects will affect the overall performance of polymer insulating materials and even lead to severe accidents such as failure of power equipment. Actual engineering practice has put forward an important demand for effective identification of material defects in a short time. Compared with two-dimensional imaging, the results of three-dimensional tomography are more intuitive and can obtain more accurate defect information including depth and size. Therefore, it is the current research hotspot and future development trend in polymer insulating material detection to adopt corresponding non-destructive testing technology to perform rapid and intuitive tomography of polymer insulating materials and obtain effective structural information inside polymer insulating materials.

[0003] At present, the main methods for tomography 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 samples from multiple angles, which takes a long time to detect, the detection equipment is expensive, attention should be paid to radiation protection, and the samples detected 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, collect reflected echoes and use the reconstructed cross-sectional image 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 thermal loading process through an infrared thermal imager, and use a cross-correlation matching algorithm for the time-delayed excitation signal to obtain truncated images at different depths. However, due to the low thermal conductivity of polymer insulating materials and the large attenuation of thermal waves therein, the existing thermal wave tomography method is difficult to be effectively applied in the tomography of polymer insulating materials, and 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 for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the 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, to provide a thermal wave tomography method and device for polymer insulating materials, and to solve the problems of low imaging resolution and detection accuracy in the 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 the logarithmic frequency modulation method to perform photothermal excitation on polymer insulating materials, wherein during the scanning process from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to change with time in accordance with a logarithmic function relationship, so as to concentrate the energy in the low frequency band and enhance the thermal wave energy in the low frequency region;

[0010] A2. Collect infrared thermal image sequences of the heat diffusion process caused by photothermal excitation through an infrared thermal imager to obtain the thermal response signal of the internal structure of the material;

[0011] A3. Perform signal processing on the acquired infrared thermal image sequence, introduce frequency difference by adjusting the end frequency of the frequency scanning parameter in the data processing stage, so as to obtain transformation signals at different frequencies, wherein a series of transformation signals with different frequency differences are generated by the set starting frequency and the adjusted end frequency, calculate the phase information of the transformation signal, and select the phase map of a specific frequency corresponding to the average value of the starting and ending frequencies as the tomogram, and associate the characteristic thermal image with the corresponding depth information according to the relationship between the frequency and the material depth;

[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 a plurality of near-infrared semiconductor laser generators with a wavelength range of 700 to 2500 nm, 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.

[0014] Furthermore, in step A1, the excitation signal of the logarithmic frequency modulation satisfies the following equation:

[0015]

[0016] In the formula, 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 scanning 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 stop frequency of the Chirp-Z transform basis function to form a series of Chirp-Z transform signals with different frequency differences, and the phase information of the Chirp-Z transform signal is calculated, and the phase diagram of the average value of the start frequency and the stop frequency is selected as the tomogram.

[0018] Furthermore, step A3 specifically includes:

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

[0020] The acquired infrared thermal image sequence is processed by 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 diagram of a specific frequency corresponding to the average of the starting frequency and the ending frequency is selected as a tomogram;

[0023] Using 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] The 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 according to the camera parameters and the experimental configuration, the calculation method is 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] A 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 semiconductor laser array excitation source, and synchronously trigger the operation of 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 device for polymer insulating materials, which realizes rapid, non-destructive, high-resolution, non-contact, large-area, and visualized 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 that can present the internal structure of polymer insulating materials, and proposes an innovative photothermal excitation modulation method, signal processing method, and imaging enhancement algorithm, which can effectively realize high-resolution and high-precision tomography of the internal structure of polymer insulating materials. By constructing a corresponding hardware system and cooperating with algorithms, the present invention can present the internal structure information of polymer insulating materials remotely, with high precision, quickly, and intuitively.

[0046] Compared with the 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, in view of 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 structure information of the polymer insulating material in tomography imaging more complete, and improve the detection accuracy and depth. In terms of the post-processing algorithm, the Chirp-Z transform algorithm of the displaced frequency is adopted to obtain a tomography image with higher resolution. In terms of super-resolution imaging, the resolution of the detection is improved by adding a microscope lens and cooperating with a deconvolution algorithm that eliminates the thermal diffusion blur function. 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 the 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 It is a flow chart of a detection method of thermal wave tomography of polymer insulating materials 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 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 optical 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 supporting control computer for the thermal imager, 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 and application of the present invention.

[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 discovery, identification and location of defects are crucial for the safe and stable operation of industrial systems. At present, the 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 existing thermal wave tomography technology has a low detection resolution in polymer insulating materials, which is difficult to meet the actual engineering needs.

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

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

[0056] A1. Modulated thermal excitation source: Use logarithmic frequency modulation method to perform photothermal excitation on polymer insulating materials. In the process of scanning from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to change with time in accordance with the logarithmic function relationship, and the energy is concentrated in the low frequency band to enhance the thermal wave energy in the low frequency area, thereby improving the detection depth and resolution of the deep structure of the material;

[0057] A2. Collecting infrared thermal image sequence: using an infrared thermal imager to collect infrared thermal image sequence of the heat diffusion process caused by the photothermal excitation in step A1, so as to obtain the thermal response signal of the internal structure of the material;

[0058] A3. Post-processing: performing signal processing on the infrared thermal image sequence collected in step A2, introducing a frequency difference by adjusting the end frequency of the frequency scanning parameter in the data processing stage to obtain transformation signals at different frequencies, wherein a series of transformation signals with different frequency differences are generated by the set starting frequency and the adjusted end frequency, calculating the phase information of the transformation signal, and selecting a phase map of a specific frequency corresponding to the average value of the starting and ending frequencies as a tomogram, and according to the relationship between the frequency and the material depth, using the tomogram as a characteristic thermal map, and correlating it with the corresponding depth information, thereby improving the depth resolution of the detection of internal structural defects of the material;

[0059] A4. Super-resolution imaging and result output: Use a super-resolution algorithm to deblur the characteristic heat map processed in step A3 to obtain a super-resolution characteristic heat map, and perform three-dimensional tomography based on these heat maps to achieve high-resolution and high-precision internal structure imaging of polymer insulating materials. Output the generated three-dimensional tomography to show the internal structure and potential defects of the material.

[0060] See also Figure 2 The embodiment of the present invention also provides a thermal wave tomography device for polymer insulating materials, including: a sample stage 12 for placing samples; a semiconductor laser array excitation source 5, which is composed of a plurality of near-infrared laser generators 3 and 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; an excitation source control system 1, which is used to set and control parameters of the semiconductor laser array excitation source 5, and synchronously trigger the operation of the excitation source and the thermal wave imaging system; a thermal wave imaging system 6, which uses a medium- and long-wave infrared thermal imager and is controlled by a matching control computer 8, and is used to collect a thermal image sequence of the sample surface and convert it into an electrical signal for storage and analysis; a microscopic imaging system 10, which magnifies the image collected by the infrared thermal imager through a microscope lens; and a post-processing computing system 9, which is used to execute super-resolution imaging, feature extraction and three-dimensional tomography generation algorithms, thereby realizing high-resolution and high-precision tomography of the internal structure of the polymer insulating material.

[0061] The 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 an adjustable semiconductor laser array. The semiconductor laser array can control the size and position of the heating spot, and is connected to the infrared thermal imager through a control system. The microscopic lens is used to magnify and identify the polymer material, and then combined with the post-processing algorithm system, effective tomography of polymer materials and equipment is achieved. The tomography device consists of six parts, including 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. The parts cooperate with each other to achieve high-resolution and high-precision tomography of the internal structure of the polymer insulating material.

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

[0064] Semiconductor laser array excitation source. This part is composed of multiple near-infrared (wavelength range is 700 to 2500nm, generally 808nm) semiconductor laser generators, each of which outputs laser light through optical fiber. The laser output optical fiber is arranged in a specific shape at the output array end to form a laser array of a specific shape. Through the arrangement of optical fibers, the laser output by the excitation source has high uniformity of heating, adjustable spot area, adjustable power, and adjustable waveform. In order 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 6-way motor adjustment system of up, down, left, right, front and back, so as to effectively detect polymer insulating materials and equipment of various shapes and sizes.

[0065] Excitation source control system. This part is mainly used to control the semiconductor laser array excitation source, set and control its parameters, and achieve synchronous triggering of the excitation source and the thermal wave imaging system by outputting a trigger signal. Specifically, the excitation source control system can control the power and waveform of the laser output by the semiconductor laser array source, and can also adjust the excitation source output array end in 6 directions. In addition, the control system can also output a trigger signal to the thermal wave imaging system to ensure that the thermal wave imaging system can collect data on the polymer insulating 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, videos 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 at least 0 to 200Hz), adjustable acquisition window, adjustable focal length, and fast data transmission. The system can control the acquisition time of the infrared thermal imager by itself 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. The thermal wave imaging system is usually used to collect the temperature change process and obtain the data of the infrared thermal image sequence.

[0067] Microscopic imaging system. This part is mainly composed of a microscope lens, which amplifies the resolution of the image collected by the infrared thermal imager to improve the accuracy of the detection imaging. The principle of configuring the microscope lens is to first calculate the actual size of the pixel through the parameters of the infrared thermal imager and the experimental configuration, and then configure a microscope lens that meets the requirements (for example, ×10 times) according to the accuracy required by the experiment to improve the resolution of the collected image.

[0068] Post-processing computing system. This part is composed of a high-performance computer with built-in software, which is mainly used for subsequent processing of 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 the characteristic thermal map of different depths, and combines the super-resolution algorithm to obtain the high-resolution characteristic thermal map. The three-dimensional tomography generation algorithm generates a three-dimensional tomography map from the characteristic thermal maps of different depths, thereby realizing the tomography 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, placing the polymer insulating material to be tested on the sample stage, adjusting the position of the sample stage, and observing the infrared thermal imager acquisition picture in real time to ensure that the sample is in the best testing position of the infrared thermal imager.

[0073] Step S3, set the acquisition parameters of the infrared thermal imager such as acquisition frequency, window size, integration time, acquisition time, etc., 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, and make preparations before acquisition. The excitation of the thermal excitation source should meet the logarithmic frequency modulation method, as follows:

[0074]

[0075] Step S4, trigger one of the point laser sources in the semiconductor laser array excitation source, and synchronously trigger the infrared thermal imager to obtain the thermal diffusion process after the point laser source heats the material surface, and fit the corresponding thermal diffusion fuzzy equation of the material through the built-in algorithm and the material's thermal physical parameters. The form of the thermal diffusion fuzzy equation is as follows:

[0076]

[0077] Step S5, after the sample surface temperature returns to normal, an infrared thermal imager is used to collect the sample initial thermal image. Subsequently, the semiconductor laser array excitation source and the infrared thermal imager are synchronously triggered to collect the sample surface temperature change during the laser loading process, and obtain the infrared thermal image sequence 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. A Chirp-Z transform algorithm based on the dislocation frequency is used for each pixel point 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] Step S7, using a super-resolution algorithm and combining the heat diffusion fuzzy equation to perform defuzzification calculation on these characteristic heat maps, and obtaining a super-resolution characteristic heat map. According to the tomography algorithm, a tomography of the material is generated, thereby realizing three-dimensional tomography of the polymer insulating material.

[0087] The innovation of the present invention lies in:

[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 the polymer insulating material.

[0090] (3) In the above steps S4 and S7, a super-resolution algorithm combining hardware and software is proposed. In terms of hardware, a microscope lens is customized according to the relevant configurations of the infrared thermal imager, material size, and detection distance. In terms of software, based on the thermal physical properties of the material, a thermal diffusion fuzzy function is obtained based on a point laser pre-experiment, and the fuzzy function is 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 thermal wave tomography scheme of the embodiment of the present invention for polymer insulating materials has the following specific innovative designs:

[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-type laser thermal excitation source to load the specimen, which improves the efficiency of photothermal excitation loading, increases the area of ​​photothermal excitation loading, and makes the 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 change (hereinafter referred to as "logarithmic frequency modulation") for photothermal excitation according to the actual size of the detection material. The excitation signal of logarithmic frequency modulation conforms to the following equation:

[0097]

[0098] In the formula, 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 scanning 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. The depth of low-frequency thermal wave detection is deeper and can 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 in the figure below: Figure 3 As shown, 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 materials.

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

[0104] The previous algorithm is to cross-correlate the collected thermal response signal with the time-delay 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. The cross-correlation matching of the time-delay 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 a 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 the frequency, thereby improving the depth resolution of defect detection.

[0105] The offset frequency used is to obtain the frequency difference by changing the termination frequency of the Chirp-Z transform basis function under the premise of ensuring that the key parameters of the frequency modulation signal such as the initial frequency and scanning time remain 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] In the formula, 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 of a specific depth can be obtained, and the resolution of the tomographic depth can be controlled by the size of the frequency difference.

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

[0116] In order to improve the imaging accuracy, corresponding arrangements were made in both hardware and algorithm. In terms of hardware, the actual size represented by each pixel is calculated 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 of the acquired image. The corresponding microscope lens can be configured according to the size of the test piece and the desired accuracy.

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

[0120]

[0121] In the formula, 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 the relevant parameters of the material and fitting the simulation results, the thermal diffusion fuzzy function of the material can be obtained. Based on the thermal diffusion fuzzy function, the deconvolution algorithm is used to deblur the infrared thermal image and improve the lateral resolution of the characteristic thermal image.

[0123] By utilizing the above-mentioned innovations, the present invention can effectively realize high-resolution and high-precision tomography of polymer insulating materials. By constructing a corresponding hardware system and coordinating algorithms, the present invention can present the internal structure information of polymer insulating materials remotely, accurately, quickly and intuitively.

[0124] Example

[0125] Thermal wave tomography detection method for polymer insulation materials Figure 1 As shown, the corresponding detection device is as follows 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, placing the sample on the sample stage and adjusting 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, using a feature extraction algorithm to extract features from the acquired infrared thermal image sequence, and obtaining thermal wave tomography images at different depths.

[0132] Step S7, using 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 of polymer insulating materials, defects may occur inside them due to 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 discovery, identification and positioning of defects are crucial for the safe and stable operation of industrial systems. At present, the 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, fast 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 is difficult to meet the actual engineering needs. The present invention proposes a thermal wave tomography device and method for polymer insulating materials. Compared with the existing methods, the present invention has made innovations in many aspects such as thermal excitation source arrangement, thermal excitation source modulation method, post-processing algorithm and super-resolution hardware and algorithm in view of the characteristics of polymer insulating materials. In terms of thermal excitation source arrangement, an array laser thermal excitation source is used to improve the efficiency, area and uniformity of thermal excitation loading. In terms of the modulation method of the thermal excitation source, the logarithmic frequency modulation method is used to make the deep structure information of the polymer insulating material of the tomography more complete, thereby improving the detection accuracy and depth. In terms of the post-processing algorithm, the Chirp-Z transform algorithm of the offset frequency is used to obtain a tomography image 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, wherein the processor executes a computer program and at least executes 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 disk memory or a 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 present 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 can understand that: all or part of the steps of implementing the above method embodiments 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 method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, 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 function 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 can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a 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 contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope 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 the logarithmic frequency modulation method to perform photothermal excitation on polymer insulating materials, wherein during the scanning process from the starting frequency to the ending frequency of the excitation signal, the frequency of the excitation signal is controlled to change with time in accordance with a logarithmic function relationship, so as to concentrate the energy in the low frequency band and enhance the thermal wave energy in the low frequency region; A2. Collect infrared thermal image sequences of the heat diffusion process caused by photothermal excitation through an infrared thermal imager to obtain the thermal response signal of the internal structure of the material; A3. Perform signal processing on the acquired infrared thermal image sequence, introduce frequency difference by adjusting the end frequency of the frequency scanning parameter in the data processing stage, so as to obtain transformation signals at different frequencies, wherein a series of transformation signals with different frequency differences are generated by the set starting frequency and the adjusted end frequency, calculate the phase information of the transformation signal, and select the phase map of a specific frequency corresponding to the average value of the starting and ending frequencies as the tomogram, and associate the characteristic thermal image with the corresponding depth information according to the relationship between the frequency and the material depth; 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 insulating material according to claim 1, characterized in that: 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 a plurality of near-infrared semiconductor laser generators with a wavelength range of 700 to 2500 nm, 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 insulating material according to claim 1 or 2, characterized in that: In step A1, the excitation signal of logarithmic frequency modulation conforms to the following equation: In the formula, 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 scanning time, and t is the signal loading time.

4. The thermal wave tomography method of polymer insulating material according to any one of claims 1 to 3, characterized in that: 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.

5. The thermal wave tomography method of polymer insulating material according to any one of claims 1 to 4, characterized in that: Step A3 specifically includes: Adjusting the frequency scanning parameters in the data processing algorithm to change the end frequency, and combining it with the preset start frequency to generate a series of transformation signals with different frequency differences; The acquired infrared thermal image sequence is processed by 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 diagram of a specific frequency corresponding to the average of the starting frequency and the ending frequency is selected as a tomogram; Using the relationship between frequency and material depth, the selected phase map is correlated with the depth information to generate a three-dimensional tomogram.

6. The thermal wave tomography method of polymer insulating material according to any one of claims 1 to 5, 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; The 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.

7. The thermal wave tomography method of polymer insulating material according to claim 6, 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, 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.

8. The thermal wave tomography method of polymer insulating material according to any one of claims 1 to 7, characterized in that: In step A4, a microscope lens is first used to magnify the image collected by the infrared thermal imager; The method also 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, and a1 is the actual size represented by the pixel 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.

9. 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 of polymer insulation material according to any one of claims 1 to 8 is implemented.

10. A thermal wave tomography device of a polymer insulating material, characterized in that: include: A 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; The excitation source control system is used to set and control the parameters of the semiconductor laser array excitation source and synchronously trigger the operation of the excitation source and the 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 insulating materials.

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