306results about "Flaw detection using microwaves" patented technology

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention relates to the technical field of cable nondestructive testing, in particular to a cable defect detection system. The method mainly solves the problems that in the prior art, early-stage tiny defects in a cable are insufficient in recognition sensitivity, the omission ratio is high, and accurate classification cannot be achieved. According to the system, signals are synchronously collected through a multi-physics field composite sensing array, excitation parameters are optimized through a genetic algorithm, a three-dimensional defect probability graph is generated through fusion, a double-current Transform model is adopted to deeply analyze features and automatically recognize defect types, and finally, maintenance is guided through AR visualization, so that non-intrusive online accurate diagnosis and intelligent operation and maintenance of cable defects are achieved.

An RF-based material detection device includes an antenna connector, a first interchangeable antenna capable of being releasably coupled to the RF-based material detection device via the antenna connector, an RF transmitter unit operably connected to the antenna connector and configured to transmit an RF signal via the first interchangeable antenna into a first material at a specific resonance frequency for the first material, and an RF receiver unit operably connected to the antenna connector and configured to receive from the first material via the first interchangeable antenna a first modified signal in response to interaction of the RF signal with the first material, wherein the first interchangeable antenna includes one or more design characteristics optimized to detect the first modified signal from the first material.

An RF-based material detection device includes an antenna connector, a first interchangeable antenna capable of being releasably coupled to the RF-based material detection device via the antenna connector, an RF transmitter unit operably connected to the antenna connector and configured to transmit an RF signal via the first interchangeable antenna into a first material at a specific resonance frequency for the first material, and an RF receiver unit operably connected to the antenna connector and configured to receive from the first material via the first interchangeable antenna a first modified signal in response to interaction of the RF signal with the first material, wherein the first interchangeable antenna includes one or more design characteristics optimized to detect the first modified signal from the first material.

The invention belongs to the technical field of structural damage monitoring, and particularly relates to a structural dynamic tomography and damage monitoring method using self-excitation waves, which comprises the following steps: S1, carrying out spatial discretization processing on a monitored object, and establishing a three-dimensional discrete model; arranging a plurality of sensors on a monitoring object; recording the space coordinates of each sensor; s2, applying external excitation to a known position, and inverting an initial wave velocity field in the monitoring object; s3, extracting self-excitation waves of the monitored object received by each sensor; s4, identifying the arrival time of each self-excitation wave signal on each sensor; the space coordinates of the wave source of each excitation wave signal are calculated through inversion; s5, synchronously updating the wave velocity field based on the space coordinates of the wave sources of the respective excitation waves, and processing to obtain a damage result of the monitored object; and S6, performing corresponding processing based on a damage result. According to the method, dynamic tomography and real-time monitoring of the damage position, range and evolution process can be realized.

The invention provides a lossless identification and quantitative evaluation method and system for railway tunnel lining defects, and particularly relates to the technical field of railway engineering detection and intelligent identification. The method comprises the following steps: firstly, collecting a multi-source detection signal, and carrying out adaptive preprocessing on the multi-source detection signal; performing multi-level fusion on the preprocessed multi-source detection signals in a unified space coordinate system to generate a high-precision data set; and finally, inputting the high-precision data set into a multi-task lining defect evaluation model so as to realize refined identification and quantitative evaluation of the railway tunnel lining defects. According to the method, the automation and objectivity of lining defect detection are remarkably improved, and a reliable basis is provided for maintenance decision-making of a tunnel structure.

The invention relates to a tunnel lining quality detection method, which comprises the following steps of: acquiring tunnel lining ground penetrating radar data, and preprocessing the tunnel lining ground penetrating radar data; carrying out image segmentation on the preprocessed radar image, and dividing the preprocessed radar image into a lining thickness region, a steel bar distribution region and other background regions; counting the number of reinforcing steel bars in the reinforcing steel bar distribution area through a polymorphic detection method; on the basis of the segmented lining thickness area, the actual lining thickness is calculated in combination with the dielectric constant difference; inputting actually measured radar data into the cavity recognition model, and outputting a cavity recognition result; and generating a tunnel lining quality evaluation report by integrating the number of the reinforcing steel bars, the actual lining thickness and the cavity identification result. According to the prediction method provided by the invention, integrated operation of lining thickness measurement, steel bar statistics and defect detection is realized through multi-task collaborative optimization, the detection efficiency and precision are improved, and a reliable technical scheme is provided for tunnel lining quality evaluation.

The invention relates to the technical field of radar signal processing, in particular to a ground penetrating radar reinforcing steel bar clutter suppression method based on physical constraints, and mainly solves the problem that a data set is difficult to obtain in an existing ground penetrating radar reinforcing steel bar clutter removal method based on deep learning. The method is an improved method based on the CUT network, the CUT network structure and a comparative learning mechanism determine that the requirement of the network for the data size of a data set is low, a waveform smoothness constraint is added on this basis, the clutter removal effect and generalization ability are improved by introducing physical prior, the physical constraint serves as a regularization item, and the regularization efficiency is improved. The problem that a CUT network is prone to model collapse under a small data set is solved. Finally, the improved model is compared with other models through different evaluation indexes, and the result shows that the improved model has more advantages.

The invention discloses a cable trench fireproof cover plate performance detection device and method based on multi-band electromagnetic waves. The device comprises a microwave detection module, a terahertz imaging module, a high-precision two-dimensional mechanical scanning platform, a synchronous control and data fusion unit and a comprehensive data analysis platform. The microwave module is used for transmitting and receiving 1-10GHz electromagnetic waves to realize deep structure detection; the terahertz module works at a frequency band of 0.1-3 THz and is used for acquiring near-surface time-domain spectral information. And the scanning platform drives the modules to perform two-dimensional rasterization scanning on the surface of the cover plate. And the synchronous control and data fusion unit coordinates the time sequence action of each module to ensure the time-space consistency of data acquisition. And the comprehensive data analysis platform performs fusion processing on the multi-source data through an inversion and imaging algorithm to generate a comprehensive evaluation report covering internal macroscopic defects, near-surface microdefects and coating thickness. According to the invention, through collaborative integration of microwave and terahertz technologies, multi-scale and integrated nondestructive testing and performance evaluation of the fireproof cover plate from a deep layer to a surface layer are realized.

The invention relates to the technical field of road detection, in particular to a multi-dimensional road disease detection method and system based on a ground penetrating radar. The method comprises the following steps: acquiring original ground penetrating radar data, performing DC component removal, gain adjustment and background denoising on the original ground penetrating radar data, positioning a suspected disease area and defining the suspected disease area as a disease entity; extracting a time domain feature, a frequency domain feature and a spatial context feature of each disease entity in parallel to form a multi-dimensional feature vector; constructing a road disease knowledge graph according to the multi-dimensional feature vectors, and distributing an initial weight for each graph relation; and performing node matching operation according to the multi-dimensional feature vector and a knowledge graph, performing logical reasoning according to a graph relation path, fusing similarity and reasoning confidence, and outputting a diagnosis tag signal and a confidence signal. According to the method, the positioning accuracy of the suspected disease area is improved, comprehensive utilization of multi-dimensional information is realized, and a visual causal link is provided for diagnosis.

The invention discloses a radar and deep learning-based road surface internal disease three-dimensional morphology identification method, which integrates a novel technical path of qualitative identification and quantitative reconstruction, provides a high-precision and quantifiable reference for subsequent non-damage assessment by constructing a disease three-dimensional geometric model and reversely calibrating radar map characteristic parameters, and improves the accuracy and the accuracy of the three-dimensional morphology identification of a road surface internal disease. The problem that the three-dimensional morphology characteristics of the internal diseases of the asphalt pavement are difficult to accurately obtain at present is solved, and a calibration method is provided for obtaining the three-dimensional morphology characteristics of the internal diseases of the pavement based on a ground penetrating radar technology. And meanwhile, geometric data support is provided for mechanical response analysis and performance attenuation law research of the existing asphalt pavement structure containing internal diseases.

The invention discloses a polyethylene pipe microwave defect detection system based on a multi-dimensional movement mechanism. The method comprises the steps that firstly, full-coverage scanning of a welding area is achieved through a clamp and a control device, and microwave reflection signals of a welding joint are collected and subjected to amplification, filtering and normalization processing; converting the frequency domain signal into a time domain B-scan image by using inverse fast Fourier transform (IFFT), and effectively inhibiting direct waves and high-frequency noise by using a wavelet transform threshold denoising method so as to enhance defect reflection characteristics; on the basis, the system can identify and position typical defects such as holes, slag inclusion, incomplete fusion and the like, and visual visualization of defect positions and forms is realized through the information display module. The system is compact in structure and high in detection precision, has the advantages of high automation degree, wide application range, simplicity and convenience in operation and the like, can realize rapid and accurate detection of weld defects of the polyethylene pipeline, and provides reliable technical guarantee for pipeline quality evaluation and safe operation.

The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on a microwave frequency crystal resonator, and the method comprises the steps: forming a microwave probe through a microwave frequency crystal resonator array, placing a to-be-detected material below the microwave probe, and setting a scanning area and scanning step lengths in two directions on the surface of the material; controlling the probe to perform point-by-point scanning according to step length, and collecting a plurality of reflection coefficients of each sampling point to form an echo signal matrix; performing singular value decomposition on the matrix, applying an adjustable coefficient to a first singular value and a second singular value, and adaptively selecting an optimal coefficient according to imaging quality to reconstruct an echo signal; performing two-dimensional Fourier transform on the reconstructed matrix, constructing a spatial filter in combination with probe spacing and defect depth to perform phase compensation, performing two-dimensional Fourier transform to obtain a defect image, and performing threshold segmentation on the defect image to obtain a binary image only containing defects; and analyzing the connected regions to obtain the gravity center position of each defect and the defect area determined by the pixel number and the single pixel area. The problems of serious clutter interference, insufficient defect imaging contrast ratio, difficulty in quantitative evaluation of defect size and the like in the existing microwave detection can be solved.

The invention discloses a double-probe film thickness and defect collaborative decoupling method. The method comprises the steps that microwave reflection signals of a thermal barrier coating are synchronously collected through a coaxial resonance probe and a CSRR resonance ring probe; the microwave reflection signal comprises reflection coefficient amplitude, reflection coefficient phase and resonant frequency; constructing a frequency deviation model based on the microwave reflection signal; training the dual-channel deep learning network based on the frequency deviation model to obtain a trained dual-channel deep learning network; and obtaining film thickness information and a defect positioning result based on the trained dual-channel deep learning network. According to the method, collaborative decoupling of the film thickness and the defect signal is achieved through a physical-data fusion method, effective decoupling and independent recognition of the coating film thickness change and the defect response signal are achieved, and the resolution ratio, the accuracy rate and the practicability of a detection system are improved.

The invention provides a microwave detection method and device for PE pipe hot-melting weld defects, and the method comprises the following steps: 1) taking a hot-melting weld of a PE pipe and a base material near the weld as detection objects, and carrying out axial and circumferential microwave scanning to obtain a reflection coefficient of each position point; 2) carrying out two-dimensional space Fourier transform on the reflection coefficient of each position point; 3) carrying out phase correction on the converted data in the depth direction; 4) carrying out two-dimensional space inverse Fourier transform on the corrected data to obtain a reflection coefficient of the target; 5, image reconstruction is carried out on the reflection coefficient of the target, and information of the defects in the PE pipe hot-melt welding seam is obtained.According to the scheme, the method has the advantages that the defects can be detected, the defect positions can be determined, and conclusion obtaining does not excessively depend on personnel skills.

The invention discloses a pavement crack disease optimization identification method based on a grading mechanism and data enhancement, the grading mechanism of crack severity is introduced, the detection precision of light cracks is effectively improved, meanwhile, disease detection is divided into different grades, and a clearer technical basis is provided for subsequent coring verification and engineering decision. Diversified data enhancement strategies are further introduced on the basis of classification, a training data set is enriched through overturning, cutting, brightness adjustment, saturation adjustment and sharpening processing data expansion modes, and the robustness and detection ability of the model to complex crack diseases are remarkably improved. And the recognition efficiency and accuracy of the internal crack disease of the pavement structure in three-dimensional ground penetrating radar data processing are effectively improved.

The invention relates to a mobile device and measurement method developed to non-detructively measure the strength of concrete using electromagnetic (EM) waves in the microwave (MW) frequency band, which shows the resistance of the structure against earthquakes and external effects by measuring the strength of the concrete without damaging the reinforced concrete structure. The technical field of the invention is the non-destruuctive measurement of concrete strength using electromagnetic waves (EMW) in the microwave frequency band. This method is an alternative to destructive measurements by taking cores from structural elements such as columnsi beams, and slabs within the construction field, thus preventing damage to structures due to strength measurements and providing more rapid and non-destructive measurements from more regions. Results are evaluated using artificial intelligence techniques, providing a much more accurate strength measurement result instantly.

A concrete structure reinforcement corrosion detection method based on a random forest algorithm and microwave scattering parameters comprises the following steps: building a microwave detection platform, and carrying out fixed frequency sweep detection on a reinforced concrete structure to obtain S parameter data with reinforcement corrosion condition information; dividing the S parameter data into a training set and a test set; extracting frequency and S parameter data of the reinforcing steel bars under different corrosion degrees, and training the random forest algorithm model by taking the frequency and S parameter data as input features and the reinforcing steel bar corrosion depth as output features; and performing microwave detection on the reinforced concrete structure, and inputting the obtained frequency and S parameter data into the trained random forest algorithm model to obtain the corrosion depth of the reinforcing steel bar. According to the concrete structure reinforcement corrosion detection method based on the random forest algorithm and the microwave scattering parameters, prediction of the reinforcement corrosion depth is achieved, and the method has the advantage of being high in accuracy.

The invention relates to a radar scanning system (100) for non-invasive analysis of targets (10), comprising a plurality of radar transceivers (111) arranged in an irregular geometric configuration. The system (100) performs sequential acquisitions with a view to generating a high-resolution radar representation (20). A reconstruction processor coupled to an artificial-intelligence module (140) processes the acquired data in order to detect, classify and analyse objects within the target. The system (100) dynamically adapts to the characteristics of the target (10) and is able to compensate for relative movement between the system (100) and target. The invention is applicable to various fields requiring detailed and non-invasive analysis of internal structures.

An imaging system includes a visible-light image capturing device that images an object with light including visible light to acquire a visible-light image, a millimeter-wave image capturing device that images the object with an electromagnetic wave in a millimeter-wave band to acquire a millimeter-wave image, a distance measurement device that measures an imaging distance to the object, a memory in which information indicating end points and / or edges of the millimeter-wave image in the visible-light image is stored in correspondence with the imaging distance; and a processor. The processor acquires information in correspondence with the imaging distance by referring to the memory and generates a superimposed image in which the visible-light image and the millimeter-wave image are superimposed based on the acquired information.

This invention relates to the field of heat-sealing performance quality inspection technology for flexible packaging, and discloses an online detection system and method for the heat-sealing performance of large-scale flexible packaging. Specifically, it combines infrared thermal imaging detection technology, millimeter-wave imaging detection technology, and AI visual inspection technology. After the heat-sealing process, infrared thermal imaging detection technology is used to detect the temperature distribution during heat sealing, achieving visual quality inspection of the heat-sealing process. Then, millimeter-wave imaging detection technology is used to detect the physical structure inside the seal after heat sealing, achieving visual quality inspection of the heat-sealed product. This invention, through the sequential detection order of "infrared thermal imaging first, then millimeter-wave imaging," ensures that a product is only deemed qualified if it simultaneously passes two detection checkpoints based on different physical principles, and the results of both checkpoints are verified by the host computer. This greatly improves the overall reliability of the detection system.

The present disclosure relates to a secondary battery inspection apparatus, a secondary battery inspection method, and a secondary battery manufacturing method, and the problem solved by the present disclosure is to identify an internal state of a secondary battery in a non-destructive manner. A secondary battery inspection apparatus according to one embodiment includes a magnetic field generator configured to apply a magnetic field to the secondary battery to be tested, a high frequency generator configured to generate a high frequency of a predetermined frequency and apply the high frequency to the secondary battery to be tested, a sensor configured to detect electromagnetic waves emitted from the secondary battery to be tested to which the high frequency is applied while the magnetic field is applied and a processor configured to determine an internal state of the secondary battery to be tested using the emitted electromagnetic waves detected by the sensor.

The invention provides a cold welding and over-welding microwave detection method and device for a PE pipe hot-melt weld, and the method comprises the steps: carrying out the axial microwave scanning of the PE pipe weld meeting the requirements across the weld to obtain a series of reference comparison data, scanning the base metal of the to-be-detected PE pipe weld to obtain the parameters of the base metal, carrying out the circumferential scanning, and carrying out the welding of the to-be-detected PE pipe weld; the method comprises the following steps of: finding out a corresponding suspected point, axially scanning the suspected point to obtain a target data matrix for judging cold welding and over welding, solving a measurement parameter by combining a corresponding algorithm, comparing the measurement parameter with an optimal parameter of a normal PE pipe welding seam, and making a final judgment according to a comparison result. The provided device is used for guaranteeing the detection precision, and the detection method and device can accurately identify cold welding and over-welding defects, can be suitable for PE pipes of different manufacturers and different specifications, and are higher in identification efficiency.

The invention discloses tunnel lining geological radar unmanned aerial vehicle detection equipment and a detection method thereof. The tunnel lining geological radar unmanned aerial vehicle detection equipment comprises an aircraft, a first annular piece located outside the aircraft and fixedly connected with the aircraft, and a second annular piece located outside the first annular piece and rotationally connected with the first annular piece. The axis of the first annular piece perpendicularly intersects with the axis of the second annular piece. A nozzle and a geological radar are arranged on the outer wall of the second annular piece; a plurality of distance measuring sensors are annularly and uniformly distributed around the spray head and are positioned on the outer wall of the second annular piece; image acquisition devices are arranged on the two sides of the geological radar and located on the front side end face of the second annular piece. The problems that in the prior art, an unmanned aerial vehicle is prone to being collided and damaged, it is difficult to meet the detection requirements for the vault area and the haunch area at the same time, and a measuring line needs to be drawn manually are solved, and the purposes that the damage rate of unmanned aerial vehicle operation is reduced, operation safety is improved, and the detection requirements for the vault area and the haunch area are met at the same time are achieved.

A modular robot (11) has an elongated body portion (14) including at least one interchangeable module (29) having an elongated profile containing circuitry configured to implement a predetermined functionality of the robot. At least one motherboard (37) is disposed at at least one end of the robot and is configured to connect to the module circuitry.

The invention relates to a method (100) for determining the composition of a component (1), comprising the following steps: - Providing (101) radar data, wherein the radar data results from a detection by a sensor (2), wherein the sensor (2) is arranged in an area of ​​the component (1) and emits a radar signal in the direction of the component (1) for detection, - Selecting (102) an analytical method for determining the composition of the component (1) depending on a thickness of the component (1) and / or a number of layers (3) and / or a thickness of the layers (3) of the component (1), - Determining (103) the composition of the component (1) using the selected analytical method, wherein the selected analytical method for determining (103) the composition of the component (1) comprises the following steps: - Detecting at least one reflection based on the provided radar data, - Determining a permittivity and a thickness of at least one layer (3) of the component (1) based on the at least one detected reflection, - Providing a description of at least one layer (3) of the component (1) based on the determined permittivity and thickness. Furthermore, the invention relates to a computer program, a device, a storage medium and a tracking device for this purpose.

The invention relates to the technical field of tunnel detection, in particular to a detection device applied to tunnel leakage cracks and a using method thereof.The detection device comprises a walking seat, and the walking seat is designed in a modular mode and assembled into a shape matched with a tunnel; the detection track is installed on the outer edge of the walking seat in a modular design mode, and a detection plate is movably arranged in the detection track. The detection assembly is installed on the detection plate, and at least one detection mode is adopted to detect tunnel leakage cracks. The movement of the detection plate and the walking seat drives the detection assembly to cover all areas of the to-be-detected tunnel; the driving assembly is arranged between the detection plate and the detection track and is used for driving and locking the detection plate. According to the tunnel detection system, modular design is adopted, adaptability and flexibility are high, the tunnel detection system is suitable for tunnel detection of different parameters, detection blind areas are reduced through multi-mode coupling detection, detection comprehensiveness and accuracy are improved, powerful support is provided for tunnel maintenance and management, and safe and stable operation of the tunnel is guaranteed.

The utility model relates to the technical field of metal detection accessory devices, in particular to novel metal surface anomaly detection equipment, which can enable a worker to quickly clamp and fix metal plate pieces with different specifications and sizes, and is convenient for the worker to detect, so that the convenience of the device is improved, and the practicability is enhanced. Comprising an equipment body and further comprises two sets of clamping plates, four sets of sliding rods, four sets of first springs, four sets of first sliding blocks, a hinge assembly, a sliding assembly and an auxiliary assembly, a containing groove is formed in the front end of the top of the equipment body, two sets of first sliding grooves are evenly formed in the two ends of the containing groove of the equipment body correspondingly, and the four sets of first sliding grooves are each provided with a set of sliding rods; the four first sliding blocks are slidably connected with the corresponding first sliding grooves through the sliding rods correspondingly, the first springs are arranged at one ends of the outer sides of the four sliding rods correspondingly, and the other ends of the four first sliding blocks are hinged to one ends of the clamping plates through hinge assemblies correspondingly.

A supporting foundation state monitoring device for a drainage pipeline comprises a hole rod (5) arranged on a water inlet hole of the drainage pipeline, a reflecting plate (6) arranged on the end face portion of the upper end of the middle of the drainage pipeline, a walking vehicle (3) arranged in the drainage pipeline, and a radar detection head (2) arranged on the walking vehicle (3). According to the invention, the radar wave detection position of the drainage pipeline is identified, the vehicle-mounted motion support of the radar detection head (2) is realized through the walking vehicle (3), and the radar wave detection of the supporting foundation state of the drainage pipeline is realized through the radar detection head (2). According to the invention, the on-line monitoring of the supporting foundation state disease defect of the drainage pipeline is realized through the detection wave band, and the technical problem that the basic condition in the pipeline is shot by using the monitoring lens is solved, so that the monitoring requirement of the supporting foundation state of the drainage pipeline is met.

The invention discloses a defect detection method and device for a cable accessory based on vector detection, and relates to the technical field of defect detection methods, and the complex reflection coefficient of the cable accessory is determined based on the recognition of each feature data combination. And determining a plurality of defect characteristics of the cable accessory according to the complex reflection coefficient, the performance parameters of the cable accessory and the corresponding time domain signals, thereby improving the accuracy of the plurality of defect characteristics of the cable accessory. Therefore, the corresponding area image is determined according to the tracing of the defect area, and the defect type of the defect area is determined according to the area image, the corresponding defect feature and the previous defect event of the cable accessory; the defect surface morphology is determined according to the area position of the defect area, the corresponding defect type and the surface morphology of the cable accessory, and the defect distribution diagram of the cable accessory is determined according to the defect surface morphology, the defect type of the corresponding defect area and the plurality of defect characteristics, so that the accuracy of the defect distribution diagram of the cable accessory is improved.