105 results about "Thermal wave" patented technology

The invention provides a thermal wave imaging detection method based on nonlinear frequency modulation microwave excitation induction, and the method comprises the steps: constructing a composite excitation module of Gaussian beam and dynamic phase compensation, decomposing high-frequency / low-frequency sub-pulses for alternate emission, synchronously collecting infrared non-uniform sampling and microwave polarization diversity data, and carrying out the detection of the thermal wave imaging. And through feature fusion of a time domain CNN and a frequency domain graph neural network, propagation parameters are iteratively corrected in combination with a thermal diffusion equation model, and defect and deep structure detection is realized. According to the method, the composite excitation structure is combined with high-frequency / low-frequency sub-pulse alternate emission, the micro-defect detection resolution and the deep structure penetrating capacity are remarkably improved, the feature robustness is enhanced through multi-modal data acquisition and cross-modal neural network fusion, detection depth self-adaptive optimization is achieved through a depth compensation model and Kalman filtering, and the detection precision is improved. The parameter precision is improved with the assistance of a terahertz spectrum coupling matrix and Landweber iterative inversion, and finally an advanced detection system integrating efficient excitation, multi-dimensional perception and intelligent analysis is formed.

The invention discloses a container coating thickness online detection system and method, and the system comprises a non-contact thickness measurement unit which is used for transmitting modulation light to the surface of a container in real time according to the infrared light thermal modulation principle to excite a coating to generate a thermal wave signal, and determining the coating thickness through detecting the thermal wave reflection delay; the multi-mode executing mechanism is used for selecting a robot or a truss to carry the thickness measuring unit according to the specification of the container, and receiving a control instruction to drive the thickness measuring unit to move to a target detection point of the container according to a preset path; the three-dimensional positioning system is used for detecting the position deviation of the container entering the measuring station and feeding back the space coordinates of the container to the control system; and the control system is used for generating a motion path instruction according to the size of the container and a preset sampling rule, and receiving the coating thickness data of the thickness gauge in real time to generate a detection report, so that the precision, intelligence and high efficiency of container coating detection are realized.

The invention provides a body armor composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and a storage medium, and relates to the technical field of nondestructive testing. Ultrasonic guided wave, pulsed eddy current and infrared thermal wave excitation signals are synchronously applied to a body armor composite interlayer, and multi-modal response signals of all positions are collected; and generating an original multi-modal response signal set. And inputting the signal into a pre-trained deep convolutional neural network, determining a target frequency band parameter and a target noise suppression parameter of each modal response signal, and outputting an adaptive modulation parameter group. Performing frequency domain filtering and noise suppression processing on an original signal set to generate an optimized signal set, inputting spatial frequency features into a cross-modal feature fusion model, adaptively associating defect sensitive features of different modals through an attention mechanism in the model, and generating a three-dimensional defect distribution diagram of the composite interlayer, thereby realizing millimeter-level precision defect positioning. The precision and efficiency of body armor composite interlayer defect positioning can be improved.

The invention relates to a thermal diffusivity high-precision inversion method based on a multi-terminal deep neural network, belongs to the technical field of infrared thermal wave nondestructive testing and material thermophysical property measurement, and particularly relates to the thermal diffusivity high-precision inversion method based on the multi-terminal deep neural network. The invention aims to solve the problems of low calculation efficiency, strong noise sensitivity and insufficient robustness in a traditional thermal diffusivity measurement method, and provides a thermal diffusivity high-precision inversion method based on a multi-end deep neural network for materials with damage defects. Specifically, the amplitude and phase of thermal waves are extracted by using a phase-locked thermal imaging technology, and stable characteristics describing the thermal diffusion process are obtained. A deep learning network taking space coordinates, excitation frequency, surface temperature information, amplitude and phase as input is constructed, and efficient and accurate measurement of the thermal diffusivity of a complex material is realized through multi-modal data fusion, an advanced direct current component removal technology and a multi-end deep neural network architecture.

The invention discloses a basin-type insulator internal defect detection system and method based on pulse infrared thermal waves, and relates to the technical field of nondestructive testing and computer vision, and the basin-type insulator internal defect detection system comprises a pulse infrared thermal wave excitation module, an infrared thermal imaging module and a data processing module; the pulse infrared thermal wave excitation module is used for enabling the surface of the insulator to generate thermal response in a pulse infrared thermal wave excitation mode; the infrared thermal imaging module captures the temperature change of the surface of the insulator in real time and performs early detection; and the data processing module is used for data preprocessing, feature extraction and defect detection. According to the method for detecting the internal defect of the basin-type insulator based on the pulse infrared thermal wave, the pulse infrared thermal wave excitation and the high-precision infrared thermal imaging technology are combined, so that the bubble defect in the insulator is efficiently detected. The pulse infrared thermal wave technology generates instantaneous temperature change on the surface of the insulator, so that the bubble defect is more obvious, and the detection sensitivity and speed are improved.

The invention relates to the technical field of defect detection, and discloses a defective product detection method and system for high-temperature insulating brick production, and the detection system comprises a detection module, a heating determination module, a heating adjustment module and an evaluation module. The detection module adopts a laser heat conduction instrument and an infrared camera to perform periodic thermal wave heating on the to-be-detected brick body, and records temperature data to calculate thermal diffusivity so as to judge whether a defect area exists in the brick body or not; the heating determination module obtains the thickness of the brick body and determines the heating pulse width, the period duration and the heating power according to the thickness of the brick body; the heating adjusting module is used for compensating and adjusting the heating power according to the real-time environment temperature; the evaluation module is used for calculating an evaluation score. Through automatic and intelligent means, accurate detection of the internal defects of the high-temperature insulating bricks is realized, and the detection efficiency and accuracy are improved.

The utility model discloses a high temperature resistant casting which comprises a casting body, the casting body comprises a polypropylene coating layer, the outer surface of the polypropylene coating layer is coated with a graphene coating layer, the outer surface of the graphene coating layer is coated with an alkyd coating layer, the outer surface of the alkyd coating layer is coated with a boron nitride coating layer, and the outer surface of the boron nitride coating layer is coated with a boron nitride coating layer. And a heat-conducting fin is arranged on the inner wall of the casting body. Through the arrangement of the cooling fins, the heat conducting fins, the polypropylene coating layer, the graphene coating layer, the alkyd coating layer and the boron nitride coating layer, the problems that an existing casting is poor in high-temperature-resistant effect, the temperature of a casting material is too high, oxides and sulfides are likely to be easily formed at high temperature, the quality of the casting becomes poor, defects are formed, meanwhile, the temperature is too high, and the service life of the casting is prolonged are solved. Casting materials and mold materials can cause thermal wave stretching due to thermal expansion and cold contraction and the like, so that sintering and hot crack defects are caused, and the casting quality and the use effect are seriously influenced.

The invention discloses a thermal wave detection method for hole defects in a plate in a complex environment, and relates to a profile detection method which is realized by fusing an edge detection algorithm, heat transfer theory and deep learning. The heat transfer model is interacted with Ansys simulation software through a Python programming language and a PyAnsys library, and material attribute data is established; the deep learning model is composed of a yov5 visual recognition algorithm, and the edge detection algorithm is a Canny edge detection algorithm; firstly, an initial model of heat transfer theory is established, then a simulation model is reversely corrected through detection results of deep learning and an edge detection algorithm, and finally, the defect edge of the hole in the plate is detected. According to the method, the plate defects are detected through fusion of an edge detection algorithm and finite element heat transfer model deep learning, and the situation that hole edge detection is inaccurate due to low contrast, unclear boundary and noise area interference of a thermal image in a complex environment is avoided.

The invention discloses an automatic identification method for hot spot defects of a photovoltaic module based on thermal infrared imaging, relates to the technical field of defect identification, and aims to improve the environmental adaptation precision and the multi-frequency thermal wave penetration coverage dimension in the detection process of the hot spot defects of the photovoltaic module, realize efficient and accurate multi-frequency excitation parameter configuration and environmental thermal noise suppression and improve the detection accuracy of the hot spot defects of the photovoltaic module. The defect identification accuracy and the fault diagnosis reliability level in the operation and maintenance process of the photovoltaic module are remarkably improved, the situation that response of fixed excitation parameter setting to actual thermophysical property changes is insufficient is effectively prevented, depth deviation and defect misjudgment caused by experience judgment are avoided, the pertinence and defect identification effect of final heat source distribution field reconstruction are effectively improved, and the method is suitable for large-scale popularization and application. And determining the packaging layer to which the defect influencing the performance of the photovoltaic module belongs, and ensuring that the defect layering identification measure is highly matched with the actual heat source depth distribution risk.

The invention relates to the technical field of metal surface coating preparation, and discloses a cladding layer surface and deep defect detection method based on pulse laser, which comprises the following steps: clamping and fixing a workpiece; pulse laser excitation and bimodal signal synchronous excitation are carried out; synchronously acquiring and recording dual-channel signals; thermal wave signal processing and surface / near-surface flaw analysis; performing ultrasonic signal processing and deep defect analysis; and carrying out information fusion and three-dimensional flaw reconstruction. According to the invention, the pulse laser beam is utilized to excite the thermal wave and the ultrasonic wave at the same time, various flaws from the surface to the deep layer can be covered through one-time detection, the limitation of a single detection mode is solved, and meanwhile, the depth information of the flaws can be obtained by analyzing the phase diagram of the thermal wave signal; three-dimensional distribution of flaws can be reconstructed by combining ultrasonic time-of-flight (ToF) analysis and infrared sequence images, and accurate positioning and quantitative evaluation are realized.

The invention discloses a self-adaptive image surface texture elimination method based on phase-locked infrared thermal wave nondestructive testing imaging, and belongs to the technical field of self-adaptive image surface texture elimination methods.According to the self-adaptive image surface texture elimination method based on phase-locked infrared thermal wave nondestructive testing imaging, three algorithms including a relative total variation (RTV) algorithm, a discrete Fourier transform (DFT) algorithm and a bilateral filtering method are combined to remove textures on the surface of a material, and the texture of the surface of the material is eliminated through the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging based on the phase-locked infrared thermal wave nondestructive testing imaging. As the three algorithms have advantages on different texture removal effects, the result images obtained by the three algorithms are subjected to image fuzzy degree, texture structure similarity degree and image similarity degree scoring, and the result image with the best texture removal effect is finally output, so that the human eye impression of the infrared thermal wave nondestructive testing result image is improved.

The invention relates to the technical field of first-aid medical engineering and intelligent medical equipment, and discloses a digestive tract hemorrhage patient liquid resuscitation decision support system which comprises a thermal wave signal generation and infusion execution module, a distributed thermal dispersion perception and vascular stiffness resolving module, a multi-physics field coupling modeling module and a double-target game decision control module. By superposing a micro-amplitude thermal wave signal in resuscitation liquid, the system calculates a vascular stiffness parameter of a patient in real time based on a thermal dispersion principle; and predicting the shear stress of the wall surface of the blood vessel at different flow velocities by utilizing a temperature change viscosity and blood vessel elastic mechanics coupling model. In decision control, the system determines the target flow velocity according to the microcirculation perfusion requirement, and reversely solves the required fluid dynamic viscosity according to the shear stress safety threshold value, so as to dynamically adjust the basic temperature of the infusion fluid. The fluid viscosity is reduced through physical field intervention, the high-flow-rate resuscitation requirement is met, meanwhile, the shear stress of the wall face of the blood vessel is effectively restrained, and the rebleeding risk is reduced.

The invention discloses a welding quality detection method for a precise heat dissipation tooth-shaped plate, and the method comprises the steps: determining a heat flow feature point on the tooth-shaped plate, and applying a thermal pulse sequence to obtain dynamic thermal response data; performing space-time decomposition on the thermal response data to generate thermal disturbance response fingerprints; applying a preset phase thermal pulse source according to the thermal disturbance response fingerprint to obtain thermal wave interference field data and generate a thermal wave interference defect map; periodic thermal excitation is applied according to the thermal wave interference defect map, a thermal attenuation time constant is obtained, and a resonance thermal attenuation spectrum and a spectrum consistency spatial distribution map are generated; and calculating a thermal defect severity index according to the spectral consistency spatial distribution map and the thermal wave interference defect map, and generating a welding quality evaluation result. According to the technical scheme, direct correlation between the welding quality and the heat dissipation performance can be established through thermal wave interference and thermal resonance analysis, the welding defect which affects the heat conduction efficiency but does not cause obvious structural failure is effectively recognized, and accurate evaluation of the welding quality is achieved.

The invention belongs to the technical field of infrared thermal wave detection, and provides a method for detecting internal defects of a nickel plate, which comprises the following steps: S100: dividing a nickel plate array into a plurality of areas to be detected, S200: heating the areas to be detected on one side of the nickel plate through a heating device, and shooting a temperature distribution change video of the areas on the other side of the nickel plate through an infrared camera, s300, repeating the step S200 to shoot temperature distribution change videos of all the to-be-detected areas, then splicing all the temperature distribution change videos according to the positions of the plurality of to-be-detected areas to obtain a temperature distribution video of the whole nickel plate, and S400, analyzing the temperature distribution video of the whole nickel plate through an image processing method to obtain the temperature distribution video of the whole nickel plate. Therefore, the position and the size of the defect in the nickel plate are judged. The whole nickel plate is divided into a plurality of small grid areas, and each area is independently heated, so that the whole nickel plate is uniformly heated, and the nickel plates which do not meet the production requirements can be found and screened in time.

The invention relates to the technical field of semiconductor chip manufacturing and detection, and discloses a method and system for realizing defect detection of an intelligent card chip based on deep learning, and the method comprises the steps: collecting a thermoluminescence signal of the surface of the intelligent card chip through a thermoluminescence detector, and generating a defect thermal topological graph of the surface of the intelligent card chip through the thermoluminescence signal; according to the defect thermal topological graph, carrying out phonon backstepping processing on the interior of the intelligent card chip to obtain phonon angular frequency of the interior of the intelligent card chip; based on the phonon angular frequency, constructing a phonon vibration frequency and virtual Joule heat distribution diagram of the smart card chip; according to the phonon vibration frequency and the virtual Joule heat distribution diagram, injecting modulation heat waves into the intelligent card chip so as to acquire in-phase heat wave data and anti-phase heat wave data of the intelligent card chip in response to the modulation heat waves; and identifying a defect area detection result of the intelligent card chip through the in-phase thermal wave data and the anti-phase thermal wave data. According to the invention, the defect detection accuracy of the existing chip can be improved.

The invention relates to a mixed porcelain insulator enhanced thermal wave nondestructive testing method, which comprises the following steps: applying photo-thermal excitation to a mixed porcelain insulator, so that the surface temperature of a silicone rubber layer in the mixed porcelain insulator is increased; after photo-thermal excitation of a preset time length is applied to the mixed porcelain insulator, whether the mixed porcelain insulator has defects or not is judged according to temperature distribution of a mixed porcelain insulator thermogram shot by a thermal imager, and if the temperature distribution of the mixed porcelain insulator thermogram is not uniform, the interface of the mixed porcelain insulator has defects; if the temperature distribution of the thermogram of the mixed porcelain insulator is uniform, determining that the interface of the mixed porcelain insulator has no defect; by means of the arrangement, the defect area of the mixed porcelain insulator is judged by additionally applying photo-thermal excitation to the mixed porcelain insulator and calculating the surface temperature difference between the defect area and the normal area, and the method has the advantages that environment changes are not seen, detection is convenient, the detection speed is high, the detection result is visual and visible, and the detection accuracy is high.

The invention relates to the technical field of crack monitoring, and discloses a metal material crack propagation monitoring device and method, a storage medium and a program product.The device comprises control equipment, acoustic emission equipment, ultrasonic equipment and infrared collection equipment, the acoustic emission equipment is arranged on a to-be-detected metal sample, and the ultrasonic equipment is arranged on the control equipment; the control equipment is used for receiving the acoustic emission signal, judging whether the to-be-detected metal sample is deformed or not according to the acoustic emission signal, and controlling the ultrasonic equipment and the infrared acquisition equipment after detecting that the to-be-detected metal sample is deformed; the ultrasonic equipment is arranged on the to-be-detected metal sample and is used for sending an ultrasonic excitation signal to the to-be-detected metal sample; the infrared acquisition equipment is used for acquiring a surface thermal wave signal of a to-be-detected metal sample and generating an infrared image; and the control equipment is also used for receiving and analyzing the infrared image to obtain crack information of the metal sample to be detected. The method can improve the crack propagation detection accuracy.

The invention provides an integrated thickness measurement feedback laser cleaning system, and relates to the technical field of laser cleaning, the integrated thickness measurement feedback laser cleaning system comprises a laser cleaning head, a light path transmission unit, a laser assembly, a non-contact photo-thermal thickness measurement head, a mechanical arm and a controller, the laser cleaning head and the non-contact photo-thermal thickness measuring head are arranged at the tail end of the mechanical arm at intervals, the laser cleaning head is connected with the laser assembly through the light path transmission unit, and the control unit is in communication connection with the mechanical arm, the laser assembly and the non-contact photo-thermal thickness measuring head. And the controller is used for controlling the mechanical arm to drive the laser cleaning head to clean the surface of the workpiece along a preset cleaning route according to a thermal wave signal collected by the non-contact photo-thermal thickness measuring head. The laser cleaning efficiency can be improved.

The invention relates to a thermal wave coating thickness detection method based on step excitation, and belongs to the technical field of infrared nondestructive testing. The method comprises the following steps: continuously heating the surface of a test piece by adopting a thermal excitation source with constant power, and recording the change data of the surface temperature along with time through a radiation detector to obtain a surface temperature rise curve delta T; the method comprises the following steps of: introducing a time offset tau, performing division operation on delta T and a time square root # imgabs0 # with the offset, constructing a new thermal response function curve # imgabs1 #, and realizing accurate and rapid measurement of the coating thickness by constructing a linear or monotonous relation model between a square root at a peak moment of an f (t) curve and the coating thickness. According to the method, the dependence of a traditional method on a reference curve and complex differential calculation is avoided, the calculation process is simplified, the anti-interference capability is enhanced, and the method is suitable for measuring the thickness of a single-layer or multi-layer coating. In addition, the method can be extensively applied to detection of thermal properties, mechanical parameters and bonding quality of a coating and a substrate and depth detection of internal defects of a material, and has a wide application prospect.

The application provides a bulletproof vest composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and storage medium, relating to the technical field of nondestructive testing. The application synchronously applies ultrasonic guided waves, pulse eddy current and infrared thermal wave excitation signals to the bulletproof vest composite interlayer, collects multi-modal response signals at each position, and generates an original multi-modal response signal set. The original multi-modal response signal set is input into a pre-trained deep convolutional neural network to determine the target frequency band parameters and target noise suppression parameters of each modal response signal, and output adaptive modulation parameter groups. The original signal set is subjected to frequency domain filtering and noise suppression processing to generate an optimized signal set. The spatial frequency characteristics are input into a cross-modal feature fusion model. The attention mechanism in the model adaptively associates defect sensitive features of different modalities to generate a three-dimensional defect distribution map of the composite interlayer. Accordingly, millimeter-level precision defect positioning is achieved, which can improve the precision and efficiency of bulletproof vest composite interlayer defect positioning.

Title: Carbon Dioxide Gas Cooling System Carbon dioxide gas cooling system comprising a first circuit configured to be traversed by the carbon dioxide gas and a second circuit configured to be traversed by a decarbonized gas, said system comprising at least one heat exchange zone between the first circuit and the second circuit, said zone comprising at least one heat exchanger configured to operate a heat exchange between the carbon dioxide gas and the decarbonized gas, the cooling system comprising a third circuit configured to be traversed by a heat transfer fluid and which includes at least one heat exchanger configured to vaporize said heat transfer fluid by heat exchange with the carbon dioxide gas, said heat exchanger being disposed in the heat exchange zone, the third circuit being connected to the second circuit.Figure from the summary: figure 1.

This invention discloses an adaptive thermally excited infrared thermal wave nondestructive testing method and device. The method includes: querying a pre-stored database based on the scanned position signal to obtain the theoretical standard energy distribution spectrum under the current pose; triggering pre-aiming sensing; acquiring and generating the actual pre-aiming energy distribution spectrum; comparing the two to calculate the actual energy non-uniformity, and comparing it with a preset threshold to decide whether to initiate compensation; driving the actuator to perform physical adjustments and confirming the adjustments; and finally synchronously triggering the main thermal excitation and infrared thermal image acquisition. The device includes a scanning motion and pose feedback module, a high-precision multispectral pre-aiming sensing module, an intelligent decision-making and synchronous control module, a finely adjustable main excitation execution module, and a data management and communication interface module. This invention can proactively correct the uneven excitation energy distribution caused by scanning geometric changes from the physical source, significantly improving the quality and reliability of the detected image.

To provide a thermoacoustic device with improved efficiency.SOLUTION: The thermoacoustic device 1a includes a fluid pipe 2 filled with a working fluid and including a pipe body part 21 and a pipe large diameter part 22 having an inside diameter larger than that of the pipe body part 21, a thermoacoustic converting part 3 disposed inside the pipe large diameter part 22, and an orifice part 4 disposed inside the fluid pipe 2. In the thermoacoustic device 4a, the orifice part 4 includes a through-hole 4a through which the working fluid flows, and a small orifice diameter part 4b in which an inside diametrical size of the through-hole 4b is smaller than other parts, and the small orifice diameter part 1a is arranged in a boundary part 23 between the pipe body part 21 and the large pipe diameter part 22.SELECTED DRAWING: Figure 1

The invention discloses an ion implantation thermal wave monitoring piece and a reuse method. The method comprises the following steps: providing a plurality of monitoring pieces used in the same batch; measuring a first thermal wavefront value of each monitoring sheet; when all the first thermal wave front values meet the consistency requirement, first ion implantation doped with a first gas element is conducted on the multiple monitoring pieces, and first thermal wave rear values of all the monitoring pieces are obtained; performing second ion implantation doped with a second gas element on the plurality of monitoring sheets after the first thermal wave back value is obtained, and annealing, so that the measured second thermal wave front value of each monitoring sheet meets the consistency requirement; the multiple monitoring pieces are used for carrying out first ion implantation again, and second thermal wave back values of all the monitoring pieces are obtained; wherein the second injection dose for performing the second ion injection is greater than the first injection dose for performing the first ion injection. According to the invention, the accuracy of V-shaped curve angle correction and the recycling frequency of the monitoring piece can be improved, and the recycling efficiency can be improved.

The invention relates to the technical field of infrared thermal wave nondestructive testing, and discloses a method, a system, equipment and a medium for improving an infrared thermal wave nondestructive testing effect. Inputting into a physical information neural network model which takes a physical constraint comprehensive model as a constraint and is trained by adopting a multi-task loss function based on a self-adaptive weight adjustment mechanism to carry out atmospheric error correction, and outputting an infrared correction heat map; inputting the correction result into a pre-trained target detection network model for defect identification, and outputting a defect identification result; by optimizing the excitation mode, the image correction algorithm and the defect recognition algorithm, the influence of atmospheric factors on the accuracy of an infrared thermal wave detection result under a long-distance detection condition is reduced, automatic defect recognition is realized, and technical support is provided for engineering application of an infrared thermal wave technology.

The invention relates to an infrared thermal wave nondestructive testing integrated test bench under electric pulse thermal excitation, which comprises a main frame, a console, a thermal excitation source, a liquid nitrogen gasification system and a thermal shielding case, the thermal shielding case is covered outside the main frame, the console is arranged outside the thermal shielding case, the thermal excitation source is fixed in the thermal shielding case, and the liquid nitrogen gasification system is fixed in the thermal excitation source. The thermal excitation source is electrically connected with the control console, the main frame is provided with a direction adjusting device, the direction adjusting device is movably connected with a thermal infrared imager, the main frame is provided with a plurality of supporting rods through fixing devices, the supporting rods are connected with telescopic fixing pieces through movable devices, and the telescopic fixing pieces are connected with the thermal infrared imager through movable devices. An electric contact is arranged at the other end of the telescopic fixing piece and electrically connected with the control table through a cable. According to the invention, the problems of rapid cooling of the test piece, shielding of an environment heat source and infrared light, and rapid fixation and position adjustment of the test piece, the thermal infrared imager and the thermal excitation loading electrode can be solved.

The present invention discloses a multilayer material measurement method, device and storage medium, which are applied to the field of thermal property measurement. The multilayer material measurement method includes: obtaining a thermal signal, and controlling the thermal signal to pass through the multilayer material along a first direction, wherein the multilayer material is provided with a plurality of sublayers arranged in a stacked manner; obtaining the temperature-propagation depth change relationship and the temperature-time change relationship of the multilayer material in the first direction; calculating the thermal resistance-propagation depth change relationship according to the thermal signal, the temperature-propagation depth change relationship and the temperature-time change relationship; calculating the thermal wave transfer characteristic-propagation depth change relationship according to the temperature-propagation depth change relationship and the temperature-time change relationship; obtaining the interface thermal resistance of the multilayer material and the transfer characteristics of the thermal wave at the interface of the multilayer material according to the thermal resistance-propagation depth change relationship and the thermal wave transfer characteristic-propagation depth change relationship. The method enables users to better understand the relevant parameters of the multilayer material and facilitates the application of the multilayer material.

The application is a kind of thermal wave detection method for internal hole defects of plate in complex environment, relates to a kind of section detection method, the method is edge detection algorithm and heat transfer, depth learning is fused to reach detection method;Heat transfer model is interacted by Python programming language through PyAnsys library and Ansys simulation software, material attribute data is established;Deep learning model is composed of yolov5 visual recognition algorithm, and the edge detection algorithm is Canny edge detection algorithm;First, the initial model of heat transfer is established, then the detection results of deep learning and edge detection algorithm are used to correct the simulation model reversely, finally the edge of internal hole defect of the plate is detected;The application detects the plate defect by the fusion of edge detection algorithm and finite element heat transfer model deep learning, avoids the inaccuracy of hole edge detection caused by low contrast, unclear boundary and noise area interference of thermal image in complex environment.

The present invention discloses an infrared thermal wave nondestructive testing system, comprising a testing table, a thermal excitation module, a thermal imaging device, a component to be tested, a computer, a time relay and a synchronous trigger; the thermal excitation module, the thermal imaging device and the component to be tested are respectively arranged on the testing table; the component to be tested is located on one side of the testing table, and the thermal excitation module and the thermal imaging device are located on the other side of the testing table; the thermal imaging device is facing the component to be tested; the excitation end of the thermal excitation module is aligned with the component to be tested; the computer is connected to the thermal excitation module via a time relay. The present invention also discloses an infrared thermal wave nondestructive testing method. The beneficial effects of the present invention are: through continuous excitation of the thermal excitation module, deeper defects can be effectively detected; and through the background subtraction-principal component analysis algorithm, the noise signal caused by the uneven surface of the component to be tested can be effectively eliminated, thereby improving the accuracy of quantitative detection of defect area.

This invention discloses a method and system for detecting shallow defects in insulating tie rods based on infrared thermal wave images, belonging to the field of non-destructive testing technology for high-voltage electrical equipment. The method includes: applying thermal wave excitation to the surface of the insulating tie rod and acquiring a sequence of infrared thermal images; preprocessing the acquired infrared thermal image sequence; segmenting the defect region using a multi-scale feature fusion network; classifying and locating the defects using a deep learning model with an integrated attention mechanism, and optionally performing quantitative evaluation of the defect area and depth. The system includes a thermal wave excitation module, an image acquisition module, an image preprocessing module, a feature extraction module, and a recognition and classification module. This invention combines infrared thermal wave technology with deep learning algorithms, effectively improving the detection accuracy and automation level of shallow defects. It has advantages such as high recognition accuracy, comprehensive functions, and strong practicality, and is suitable for condition monitoring and intelligent operation and maintenance of power equipment.