2562 results about "Ultrasonic detection" patented technology

The invention relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention relates to a settlement data analysis and early warning method based on a cloud platform, and the method comprises the steps: collecting settlement data of a basic part of a main transformer in real time through a high-precision electronic settlement observation device array, obtaining deformation stress data through data preprocessing and coordinate calculation, and uploading the deformation stress data to the cloud platform; transmitting an ultrasonic pulse signal to the stress concentration position of the local area through ultrasonic detection equipment, receiving reflected wave data, and identifying the depth and range of the crack initiation position according to the reflected wave data; a sensor array is arranged on the basic surface of a crack initiation position, sound wave signals released in the crack propagation process are captured, and a time sequence of the crack propagation direction and speed is determined; and detecting crack depth data of the repair priority region through ultrasonic waves, calculating an adjusted basic health state value, comparing the adjusted basic health state value with a preset safety threshold, and generating a corresponding safety early warning signal.

The invention relates to a lightweight component surface defect quantitative evaluation method, system and device, and the method comprises the steps: obtaining a multispectral surface image, laser three-dimensional data and ultrasonic detection data of a lightweight component, and carrying out the fusion registration based on a preset multi-sensor synchronization protocol, and obtaining a component joint data set; performing dynamic pruning analysis on the component joint data set to obtain geometric contour features, and performing internal feature extraction on the component joint data set to obtain internal abnormal response features; stress gradient distribution data and volume ratio parameters are carried out on the geometric contour features and the internal anomaly features; and obtaining an original working condition data set of the lightweight component, carrying out service working condition coupling analysis on the multi-dimensional prediction matrix, and carrying out multi-dimensional quality l parameter identification to obtain component comprehensive quality information. According to the method, the morphology mechanical coupling feature extraction precision of composite defects such as cracks and holes can be improved.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses an ultrasonic transducer characteristic parameter test system, which belongs to the technical field of ultrasonic detection and comprises an excitation signal generation module, an impedance matching adjustment module, a resonance characteristic extraction module, a signal acquisition and processing module, a temperature control loading module, a physical vibration isolation module and a frequency spectrum calibration module. The excitation signal generation module is combined with an off-chip frequency locking feedback loop through an on-chip oscillator to generate a multi-frequency-point sweep frequency pulse array, the multi-frequency-point sweep frequency pulse array is connected to the resonance characteristic extraction module after passing through the impedance matching adjustment module, the multi-frequency-point sweep frequency pulse array and the frequency spectrum calibration module construct a bidirectional feedback path and share harmonic node synchronous calibration logic, and coupling offset compensation is achieved; the frequency spectrum calibration module performs bidirectional real-time error correction on the excitation output frequency and the resonance input phase; and the transducer is arranged in the thermal convection temperature equalizing cabin of the temperature control loading module and is overlapped with the physical vibration isolation module. The method has the advantages that high-precision, low-interference and full-frequency-domain dynamic testing of characteristic parameters of the ultrasonic transducer is achieved, and testing stability is improved.

The invention relates to the technical field of metal ultrasonic detection, and discloses a method for performing nondestructive flaw detection on a casting by using ultrasonic waves and a flaw detection system.The method comprises the following steps that rapid modeling and acoustic reference calibration are performed on equipment and a workpiece, and a unified coordinate system, a sound velocity delay reference and an optimal sound beam path are obtained; acquiring and caching multi-view ultrasonic echoes in real time to obtain a high-integrity original data stream; implementing adaptive noise diffusion based on the data stream, and constructing a diffusion data stack matched with scattering characteristics; performing scattering compensation inverse diffusion and super-resolution fusion on the diffusion stack to obtain a high-resolution purified echo image; performing closed-loop regulation and control on image quality indexes, and optimizing scanning parameters in real time; and combining the optimized image and scanning parameters, and adopting a segmentation network and online increment fine tuning to realize defect identification and automatic grading. The system has high resolution, real-time performance and self-adaptive closed-loop control, and the reliability and the accuracy of casting defect detection are remarkably improved.

The invention discloses a hydropower station diversion tunnel longitudinal crack depth detection method, which comprises the following steps: arranging ultrasonic measuring points above and below a diversion tunnel longitudinal crack to obtain a reinforced concrete sound velocity representative value; arranging ultrasonic measuring points within the length range of the crack; performing ultrasonic detection, and judging the depth of the crack through ultrasonic waves; adjusting the position of the ultrasonic measuring point according to the depth of the fracture until the ultrasonic shape is in a suitable state; and calculating the depth of each detection point of the longitudinal crack. According to the technical scheme, by means of the method for detecting the longitudinal crack depth of the reinforced concrete in the diversion tunnel of the hydropower station through the ultrasonic waves, crack depth detection and calculation of the circular tunnel are completed, and in order to judge the development condition and the stability state of the crack, the crack depth can be regularly detected in the overhaul period of a hydropower station generator set; therefore, monitoring of longitudinal crack expansion and extension is realized.

The invention discloses an unmanned aerial vehicle inspection device and method, the unmanned aerial vehicle inspection device comprises an unmanned aerial vehicle, a laser ranging camera, a holder, a telescopic device and an ultrasonic detection device, the telescopic device is installed on the unmanned aerial vehicle, and the ultrasonic detection device is connected with the telescopic device through the holder. The laser ranging camera is connected with the first end of the unmanned aerial vehicle through the holder, and the laser ranging camera and the ultrasonic detection device are both located below the unmanned aerial vehicle. The invention belongs to the technical field of unmanned aerial vehicles, and aims to solve the problem that in the prior art, iron tower inspection cannot inspect the nut loosening condition. The technical effects are that the anti-loosening condition of the nut can be reliably and accurately detected, the loosening condition of the nut can be timely detected, and the stability of an iron tower can be guaranteed.

The invention relates to the technical field of nondestructive testing, in particular to a weld joint ultrasonic testing parameter optimization method based on particle swarm optimization, which comprises the following steps: acquiring a weld joint path three-dimensional coordinate sequence, combining structure prior and boundary conditions, removing abnormal points by self-matching cubic splines to regulate and control node density, generating a curvature data set, and optimizing weld joint ultrasonic testing parameters according to the curvature data set. Smooth processing is carried out to extract curvature derivative variation; a key section is screened; a parameter set is constructed and dense sampling is carried out; feature points are detected to generate a focusing area, a matching degree function is established to execute parameter optimization, and an optimal combination set is output. According to the method, on the basis of welding seam path coordinates and structure prior, self-matching cubic spline interpolation is improved to eliminate abnormal regulation density, curvature data is generated, filtering and derivative screening focusing sections, Latin sampling difference distribution parameters and derivative feature extension paths are performed, a matching degree function is constructed to drive particle swarm optimization, and parameter reinitialization matching energy is performed; the signal-to-noise ratio and the detection rate are improved, and the resource allocation efficiency is optimized.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention discloses a defect detection system for a metal-composite material mixed structure device. The defect detection system comprises an industrial control computer, a mechanical scanning arm, a polarization detection probe, an ultrasonic detector assembly and a power supply, the invention further provides a defect detection method for the metal-composite material mixed structure device. The defect detection method specifically comprises the following steps: acquiring polarization image information of the metal surface; solving polarization image parameter information; carrying out surface defect detection; metal internal ultrasonic detection information is obtained; performing internal defect detection; and performing multi-mode data fusion detection. According to the invention, two information sources with complementary advantages, namely polarization and ultrasound, are selected as detection information sources of the multi-modal detection method, so that all-directional defect detection and visualization on the surface-interior of the metal-based composite material mixed structure device are effectively realized; and a feasible technical method is provided for engineering application of polarized light / phased array ultrasonic multichannel integrated detection.

The invention discloses a real-time monitoring, regulating and controlling method for concrete pouring cracks of a high-rise building, and the method comprises the steps: arranging a distributed optical fiber sensor array and an ultrasonic detector in a concrete pouring region of the high-rise building, and collecting the data of a temperature field and a strain field in concrete in real time based on the distributed optical fiber sensor array; the collected data parameters are transmitted to a data processing terminal, and a crack early warning model is established in combination with concrete material characteristic parameters; analyzing the received data by using a crack early warning model, and judging whether the concrete has cracks and the development trend of the cracks; and when the crack is monitored or the crack development risk exists, the regulation and control system is started. The system has the beneficial effects that all-directional real-time monitoring of the internal state of concrete is realized through combination of distributed optical fibers and ultrasonic detection, the coverage range is wider, and data is more comprehensive. A crack early warning model constructed based on multiple parameters is combined with a deep learning architecture, so that the precision of crack identification and trend prediction is greatly improved.

The invention provides an ultrasonic sensitivity detection method based on a large workpiece, relates to the technical field of detection, and aims to solve the problems of inconsistent sensitivity in a full thickness range and insufficient deep defect detection precision of traditional ultrasonic detection. According to the method, multiple groups of depth-adaptive equivalent reflectors are arranged on a test block, and a gain value is dynamically adjusted in combination with a fuzzy neural network controller, so that sensitivity adaptive compensation in a full thickness range is realized, and a detection error is ensured to be stabilized within + / -1%; according to the dynamic reflector interval design based on the material attenuation coefficient and the acoustic parameter, the reflector distribution is optimized, and the manual calibration complexity is remarkably reduced. And fitting a TCG curve through a segmented weighted least square method and carrying out simulation verification to generate a high-precision DAC curve, so that high-confidence output of quantitative defect evaluation is realized. The method improves the sensitivity, accuracy and engineering applicability of ultrasonic detection of large workpieces, and is especially suitable for defect detection of workpieces with complex curvatures.

The invention discloses a method and a device for detecting internal defects of a special pipeline with a rifling. The device for detecting the internal defects of the special pipeline with the rifling comprises a detection equipment walking mechanism, the CCD industrial camera is used for collecting core images, and the CCD industrial camera is arranged at the foremost end of the special pipeline internal defect detection device with the rifling; the CCD industrial camera is connected to the holder, and the holder can rotate in all directions; the light supplementing light source can adjust the intensity of the light source and is used for adjusting the intensity of the light source and improving the imaging effect; the ultrasonic detection module comprises an ultrasonic probe and is used for identifying internal defects of the pipeline; the internal defect detection device for the special pipeline with the rifling further comprises a positioning device, a data processing terminal and a data transmission line. According to the invention, the problems of difficult carrier advancing stability, insufficient detection coverage and data fusion, and limited defect quantification and positioning precision in the prior art can be solved.

The invention discloses a magnetoacoustic coupling detection system and method for pipeline defect detection, and relates to the technical field of pipeline nondestructive detection. The system comprises a pipeline defect detection module, a magnetofluid driving module, an auxiliary moving module, a signal acquisition module, a data processing module and a data transmission module. The pipeline defect detection module comprises a magnetic flux leakage detection unit and an ultrasonic detection unit; the magnetofluid driving module comprises magnetofluid and an electromagnetic excitation device; the auxiliary moving module comprises a plurality of driving wheels tightly attached to the inner wall of the pipeline. The signal acquisition module comprises a data acquisition unit and a data storage unit; and the data processing module is used for processing the acquired magnetic excitation parameters, the magnetic leakage signals and the sound wave sensing data. According to the invention, the technical problems of low defect identification precision, complex use of a coupling agent, insufficient real-time data processing capability and the like existing in a single detection mode in the prior art can be solved.

The invention relates to the technical field of ultrasonic scanning imaging, and discloses an ultrasonic robot real-time trajectory planning method and system based on an ultrasonic image. The trajectory planning method is applied to the ultrasonic scanning robot and specifically comprises the following steps that S101, an area where a target tissue is located is obtained, continuous scanning is carried out, an image of a cross section is obtained in real time, meanwhile, preliminary positioning of an ultrasonic probe is completed, and each frame of collected image and posture data of a current mechanical arm are synchronously recorded; and S102, carrying out image segmentation processing on the obtained image of the cross section, identifying a target structure region represented in each frame of image, accurately separating the target structure region from a complex background, obtaining the target segmentation of each frame of image, and then entering a spatial modeling stage. According to the method, the quality and robustness of the ultrasonic diagnosis image are improved by using the advantages of high learning efficiency and good generalization of the deep learning model, and the problem of poor online real-time adjustment capability in the current ultrasonic detection technology is solved.

The invention relates to the technical field of ultrasonic detection, and discloses an ultrasonic-based weld defect detection method and system.The method comprises the steps that a historical database is established, ultrasonic echo signals are traversed in the historical database, a reuse defect strategy or an ultrasonic verification strategy is determined according to the traversal result, and when the reuse defect strategy or the ultrasonic verification strategy is determined, the ultrasonic verification strategy is determined; determining a first ultrasonic result based on the echo delay and the echo intensity, determining a second ultrasonic result based on the ultrasonic echo signal and the ultrasonic feature model, determining whether the weld joint to be detected has defects or not according to the first ultrasonic result and the second ultrasonic result, and performing Fourier transform on the ultrasonic echo signal to obtain the weld joint to be detected. The defect type of the weld joint to be detected is determined based on the ultrasonic characteristic spectrogram, the attenuation result is determined based on the three-dimensional model and the metal sample and written into the simulation database of the simulation environment, the defect type is verified based on the relation between the ultrasonic characteristic spectrogram and the simulation spectrum curve, and the reliability and stability of ultrasonic detection are ensured.

The invention discloses a duct grouting fullness detection method and device, and relates to the technical field of duct grouting fullness detection.The duct grouting fullness detection method comprises the following steps that an ultrasonic probe is arranged at an inlet of a duct and transmits an ultrasonic signal, and a receiver is arranged at an outlet of the duct and used for receiving reflection and transmission signals and collecting ultrasonic propagation data in the duct; and performing frequency decomposition on the acquired signals, and analyzing energy attenuation conditions of the signals in different frequency bands by using penetration characteristics of ultrasonic waves with different frequencies. According to the method, multi-band ultrasonic detection, self-adaptive signal compensation and machine learning technologies are combined, so that the accuracy and robustness of duct grouting fullness detection are remarkably improved. Multi-band detection covers different depths, and an adaptive compensation algorithm maintains signal stability and reduces misjudgment. The machine learning optimizes data fusion, ensures that the system adapts to different construction conditions and outputs stable results, and the generated grouting map provides clear visualization to help engineers to quickly identify and maintain defect areas.

The invention relates to the technical field of titanium alloy bar detection, and discloses a titanium alloy bar quality detection system and method. The system comprises a multi-modal data acquisition module, a detection reference dynamic optimization module, a data standardization processing module, a multi-modal feature fusion module, an intelligent defect identification and evaluation module, a quality grade evaluation module and a quality detection report generation module. When the quality of the titanium alloy bar is detected, through a multi-mode nondestructive detection mechanism integrating ultrasonic detection data, optical surface detection data and eddy current electromagnetic detection data, all-dimensional defect information of the interior, the surface and the near surface of the bar is synchronously obtained, and complementation and cross validation of the multi-mode detection data are achieved; the problem of limitation of a single sensor detection visual angle is solved, the missed detection and misjudgment risks are reduced, and the comprehensiveness and reliability of a defect detection result are ensured.

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention provides an ultrasonic detection method and system for inverting residual stress by using sound wave phase velocity, and belongs to the field of air coupling ultrasonic guided wave detection. The method is based on an air coupling ultrasonic guided wave sound field propagation finite element model, and guides optimization design of air coupling ultrasonic transducer parameters. The air coupling ultrasonic guided wave detection technology is adopted for conducting non-contact excitation and receiving of guided wave signals on the composite material component with the preset stress through the stretcher. And calculating the phase velocity of the air-coupled ultrasonic guided wave in the composite material component based on a geometric positioning and time difference cooperative gradual difference method. And calibrating the phase velocity in the target composite material and preset stress to obtain a corresponding relational expression, performing phase velocity detection on a composite material component with unknown stress based on a calibration result of the target composite material, and performing inversion to obtain composite material construction residual stress. By adopting the method and the system, the residual stress detection of the composite material component is realized.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention relates to the technical field of ultrasonic detection, in particular to an ultrasonic imaging method and system for ultrasonic detection of a titanium alloy bar, and the method comprises the steps: obtaining an ultrasonic detection signal of each detection point; the ultrasonic detection signals of the detection points form an initial imaging area according to the combined probing path, and a state label about amplitude deviation in the initial imaging area is recognized; performing conditional mapping on the frequency spectrum difference of each detection point and the interval time during detection by using the state label of the initial imaging area to obtain the difference classification of each detection point; according to the difference classification of each detection point, identifying the difference guidance of the current detection path, converting the corresponding detection point into a displacement component of the surface of the titanium alloy bar according to the combination position of each detection point in the difference guidance, analyzing partial data of a minimum value, a minimum value inflection point and an offset standard value of the displacement component distribution, and calculating the displacement of the titanium alloy bar. Identifying the surface defect form of the titanium alloy bar; and the titanium alloy bar defect identification accuracy and efficiency are realized.