383 results about "Ultrasonic guided wave" patented technology

The present application provides a method for pipeline defect detection using the magnetostrictive characteristics of ultrasonic guided waves, which relates to the technical field of pipeline defect detection, including: obtaining the geometric and material parameters of multi-layer heterogeneous pipelines, and establishing a mathematical model of the pipeline; according to the mathematical model, calculating the dispersion curve and modal characteristics of ultrasonic guided waves in the pipeline; selecting a suitable excitation frequency and guided wave mode, and optimizing the detection parameters of ultrasonic guided waves; presetting defects of different types and sizes in the pipeline, and analyzing the influence of defects on guided wave propagation characteristics through numerical simulation; building a multi-layer heterogeneous pipeline ultrasonic guided wave detection experimental system, collecting guided wave signals under different defect conditions, and extracting time-frequency domain characteristic parameters. The present invention can realize the effective detection and evaluation of internal defects of multi-layer heterogeneous pipelines at different temperatures, and provides an important technical means for ensuring the safe operation of pipelines.

The invention provides a high-temperature chemical pipeline defect detection system and method based on magnetostrictive ultrasonic guided waves, and relates to the technical field of high-temperature chemical pipeline defect detection.The method comprises the steps that magnetostrictive excitation signals are applied, the propagation process of the guided waves in a pipeline is calculated through numerical simulation, and the amplitude and phase information of guided wave signals is obtained; performing time-frequency domain analysis on the guided wave signal, extracting group velocities and phase velocities of guided waves in different modes, determining frequency dispersion characteristics of the guided waves, and evaluating the influence of a high-temperature environment on guided wave propagation; processing the received guided wave signal by adopting a modal decomposition and reconstruction algorithm to obtain a corrected guided wave signal; and carrying out feature extraction and parameter estimation on the corrected guided wave signal to obtain the position, size and type information of the pipeline defect. According to the invention, the problem of multilayer heterogeneous pipeline guided wave detection in a high-temperature environment is solved, the accuracy and reliability of defect detection are improved, and an effective method is provided for pipeline health monitoring and safety assessment.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel 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 metal sheet detection method, system and device, computer equipment and a storage medium. The method comprises the following steps: controlling an ultrasonic guided-wave probe to generate an ultrasonic guided-wave signal under the condition that a metal sheet is transmitted to a preset detection area, so that the ultrasonic guided-wave signal is propagated in the metal sheet; acquiring a reflection echo signal of the metal sheet; performing feature analysis on the reflection echo signal to obtain a feature analysis result; and detecting whether the metal sheet has defects or not according to a feature analysis result to obtain a defect detection result of the metal sheet. The method is beneficial to improving the detection efficiency of the metal sheet.

The invention relates to a multi-defect ultrasonic guided wave nondestructive testing, positioning and recognition method based on a mechanical arm, relates to the technical field of nondestructive testing and automatic testing, and solves the technical problems that in the prior art, time and labor are consumed in detection of large components, and positioning recognition and detection precision is difficult to guarantee in a manual detection mode. The method comprises the following steps of: post-processing signals acquired for N times, and setting a signal amplitude threshold value; performing global scanning on the tested piece; and whether the mechanical arm completes global scanning or not is judged. A mechanical arm is used for carrying an electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, and detection and positioning recognition of various different defects are achieved; using the collected data as an original data set to construct a deep learning model and an integrated CNN model, and realizing adaptive scanning detection of internal defects of a large component and a large curved surface structure; the use of a coupling agent is reduced, the detection time efficiency and the detection precision are improved, and the detection accuracy and the defect positioning accuracy are realized.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention relates to the technical field of wind power equipment detection, in particular to a wind turbine generator blade fracture damage detection method applying an inspection robot, which comprises the following steps of: S1, constructing a blade three-dimensional point cloud model and positioning the position of the robot in real time through a laser radar and a visual SLAM (Simultaneous Localization and Mapping) module carried on a robot body; s2, generating a spiral detection path covering the surface of the blade based on a path planning algorithm optimized by the genetic algorithm; s3, synchronously collecting blade surface data by adopting a multi-mode sensor array, wherein the blade surface data comprise pulse thermal imaging data, ultrasonic guided wave signals and high-resolution visible light images; s4, performing feature fusion on the acquired data through a deep convolutional neural network; through multi-modal sensor cooperative detection, deep learning feature fusion and digital twinborn evaluation technologies, full-process automatic detection from damage identification to life prediction is realized, the detection precision and efficiency are significantly improved, and reliable guarantee is provided for safe operation of the wind turbine generator.

The invention relates to a mechanical arm ultrasonic guided wave multi-defect detection path planning method based on data driving, relates to the technical field of nondestructive detection and automatic detection, and solves the technical problem that automatic defect detection for a large plate structure and a large curved surface structure does not exist in the prior art. A system applicable to the method comprises a control and test system and a tested piece. According to the invention, the mechanical arm is used for carrying the electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, so that detection, positioning and identification of various different defects are realized; the use of a coupling agent can be reduced, the detection time efficiency is improved, the detection precision is improved, the detection accuracy is realized, and the accuracy of positioning and identifying defects is improved; the scanning path of the mechanical arm is optimally designed, so that the detection efficiency is improved; and the collected data serve as an original data set to construct a deep learning model and an integrated CNN model, and mechanical arm scanning detection path planning of multiple defects in a large component and a large curved surface structure is achieved.

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 discloses an ultrasonic guided wave signal and key feature extraction method, system and device based on deep learning and a medium, and belongs to the technical field of ultrasonic guided wave signal and key feature extraction, and the method comprises the steps: collecting a guided wave signal of a steel strand, and carrying out the preprocessing of the guided wave signal; converting the pre-processed guided wave signal into a time-frequency spectrum, extracting a frequency peak value, envelope energy and group velocity features, and performing dimension reduction processing on a feature set to generate a feature vector; inputting the feature vectors into a deep learning network model, extracting convolution features and time sequence features of the signals, and completing model parameter updating based on a training sample; the trained deep learning network model is used for reasoning the steel strand guided wave signals, classification labels of corrosion types and regression prediction values of corrosion depths are output, and the specific positions and corrosion degrees of defects are judged. According to the method, the positioning precision, the recognition accuracy and the cross-scene robustness of the ultrasonic guided waves on the corrosion defect of the steel strand under the complex working condition are remarkably improved.

A movable ultrasonic reflection clamping member for a pipe and an ultrasonic guided wave testing calibration method. The movable ultrasonic reflection clamping member (5) is openable and closable, can be mounted on the outer wall of a pipe and is used for reflecting ultrasonic guided waves. A main body of the movable ultrasonic reflection clamping member (5) consists of two semicircular components (1) of which inner walls are covered with ultrasonic coupling agent coatings (2), a constraint connecting member (3) and an opening and closing connecting member (4). The ultrasonic guided wave testing calibration method comprises: performing guided wave testing on a calibration pipe (6) provided with a movable ultrasonic reflection clamping member (5), so as to acquire a set of data comprising the echo amplitude and the mounting point (7) position of the reflection clamping member, and finally calculating data, such as a DAC, which represents the ultrasonic guided wave echo attenuation of the pipe.

The invention discloses an aviation composite material-oriented ultrasonic guided wave damage rapid imaging method, which relates to the field of nondestructive testing, and comprises the following steps: calculating a frequency dispersion curve according to material parameters of a carbon fiber composite material plate, and obtaining an ultrasonic optimal excitation angle and an ultrasonic guided wave propagation mode; scanning and collecting two groups of ultrasonic guided wave signals in the orthogonal direction in sequence; decomposing the guided wave signal, and obtaining a reconstructed ultrasonic guided wave signal after extracting a target component according to the size of an energy entropy value; calculating time-frequency information and energy of the signal, and estimating the damage probability in the scanning path by combining a damage index; embedding the expansion kernel convolution and the double probability distribution function into the damage index curve to respectively realize the reinforcement of the damage index and the sharpening of the edge; and expanding the two groups of damage index curves in the orthogonal direction into a two-dimensional damage matrix, and imaging according to a composite path cross imaging method, thereby finally realizing damage positioning imaging of the carbon fiber composite material plate.

The invention belongs to the field of ultrasonic guided wave detection, and relates to an ultrasonic guided wave damage positioning method of a gated differential attention unit network, which comprises the following steps: acquiring and preprocessing an ultrasonic guided wave scattering signal xS to obtain an energy density signal xE, and inputting the signals xS and xE into a trained damage positioning model to obtain a damage position; the training process of the damage positioning model comprises the following steps: inputting an ultrasonic guided wave scattering signal xS into a scattering wave feature extraction module to obtain a scattering wave feature fS; inputting the energy density signal xE into an energy density feature extraction module to obtain an energy density feature fE; inputting the features fS and fE into a feature fusion module to obtain a fusion feature; inputting the fused features into a prediction module to obtain a predicted damage position; updating parameters of the damage positioning model according to the predicted damage position until a trained damage positioning model is obtained; the gating differential vector attention unit excavates difference information between two adjacent moments so as to guide updating of a network state, and the model damage positioning capability is improved.

The invention relates to the technical field of detection, particularly discloses an ultrasonic guided wave non-contact accurate liquid level measurement method for a high-temperature closed container, and aims at solving the problems that in the prior art, measurement precision is low and interference is prone to occurring in a high-temperature environment. Effective signals are extracted through a self-adaptive interference suppression algorithm, the liquid level is calculated through multi-source data fusion, and an intelligent calibration and self-diagnosis system and a machine learning prediction model are arranged. Experiments show that the measurement error of the method can be controlled within + / -3cm, and the method is remarkably superior to a traditional method and is suitable for accurate liquid level measurement of high-temperature closed containers in the industries of chemical engineering, electric power and the like.

The invention relates to the technical field of computer simulation testing, in particular to a dynamic analogue simulation testing method and system for plate type structure service deformation. The technical problem that the structural damage identification precision is insufficient when fixed signal decomposition parameters are used for processing all detection signals is solved. The method comprises the following steps: acquiring a mesoscopic parameter set and a bimodal ultrasonic guided wave original signal set of a to-be-detected plate type structure; based on the mesoscopic parameter set, determining optimization parameters for decomposing the bimodal ultrasonic guided wave original signal set; decomposing the bimodal ultrasonic guided wave original signal set based on the optimized parameters and a signal decomposition algorithm, and determining a target signal set related to structural damage; and performing simulation comparison on the mesoscopic parameter set and the target signal set, and determining the service damage state of the plate type structure. The method is used for plate type structure damage identification scenes.

The present invention relates to the field of nondestructive testing technology and provides a composite material damage imaging method based on the spatiotemporal cross-correlation of ultrasonic guided wave wavefields. The method comprises the following steps: S1, acquiring a wavefield signal of the plate being tested; S2, performing narrowband signal extraction on the wavefield signal; S3, filtering the wavefield signal after narrowband signal extraction; S4, shifting the wavefield signal to obtain wavefield signal cross-correlation features; and S5, using a convex hull algorithm to obtain damage imaging results. This method innovatively extracts the difference coefficient between wavefield signals as a basis for damage location, providing a new approach to feature extraction algorithms based on the time-frequency domain. This method enables automated identification of composite material delamination damage and is sensitive to both surface and internal damage.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention provides a high-sensitivity and anti-counterfeiting ultrasonic guided wave fingerprint imaging and authentication method, which comprises the following steps of: constructing an ultrasonic transducer for fingerprint acquisition, placing the ultrasonic transducer on an optical sensor, and acquiring a sound field signal and a fingerprint grayscale image of a fingerprint; preprocessing the fingerprint grayscale image, and establishing a fingerprint speed model; extracting a first-motion wave time domain signal segment in the sound field signal of the fingerprint, and establishing a frequency domain sound field matrix; establishing an offline training data set of fingerprint imaging, and obtaining a descent gradient matrix through offline training; a post-processing model for image inpainting and detail matching is built and trained; performing online inversion through the descent gradient matrix to obtain a fingerprint inversion image of the test case; inputting the fingerprint inversion image into the trained post-processing model to obtain a fingerprint matching result; and calculating personal authentication confidence by using a fingerprint matching result, and identifying the fingerprint to be authenticated. According to the invention, the form of the fingertip epithelium can be effectively discriminated from the sound field response even under the condition of environmental pollution, and the method has the characteristics of zero occupation of the space under the ultrasonic transducer screen, high-sensitivity imaging, authentication and anti-counterfeiting.

The invention discloses a method for measuring the thickness of an oil film on the surface of a bearing bush and pressure distribution based on ultrasonic guided waves. The method comprises the following specific steps: step 1, embedding a piezoelectric plate array in the back of a to-be-measured bearing bush; step 2, acquiring a reference signal, changing oil film pressure and film thickness, and acquiring a guided wave signal; step 3, acquiring a noise signal, and performing trend noise fitting on the noise signal; 4, after trend noise fitting is completed, a segmented RMS index is established, and pressure and film thickness features are extracted; and 5, importing the extracted guided wave data into a convolutional neural network in deep learning to decouple coupling features, and imaging the surface of the bearing bush by using an RAPID imaging algorithm based on a probability damage imaging principle to complete measurement. According to the bearing bush surface oil film thickness and pressure distribution measuring method based on the ultrasonic guided waves, the thickness of the oil film can be accurately calculated, and the pressure of the oil film can be calculated.

The present invention discloses a method and a system for enhancing an ultrasonic guided wave signal based on multi-modal recognition and fusion. The method includes: solving an ultrasonic guided wave frequency dispersion curve of a detected structure, selecting target detection modes, and setting the strongest energy mode as the main mode; forming a frequency dispersion dictionary and obtaining sub-signals, then converting their abscissa axes from time to signal source distances; grouping sub-signals with similar source distances, classifying them as single modal or main mode conversion sub-signals, and removing interference signals; and resampling the classified sub-signals at a uniform frequency, extracting half-wave envelope signals, and superposing them within each source group to obtain an enhanced target signal

The invention provides an ultrasonic guided wave probability imaging method for structural damage in a temperature change environment, an improved U-Net neural network is constructed, input data of the improved U-Net neural network comprises an original guided wave signal and normalized temperature offset, and a model comprises a three-stage down-sampling module, a double-convolution layer and a three-stage up-sampling module. The attention mechanism module is connected behind the downsampling blocks of all levels and used for dynamically adjusting the feature weight, the representation capability of temperature features is enhanced, and the network can learn nonlinear mapping of temperature changes and signal changes. After model training data are obtained, filtering processing is carried out on original ultrasonic guided wave signals, normalized temperature deviation processing is carried out on temperature data, and the ultrasonic guided wave signals at the standard temperature are output through model prediction, so that the amplitude and phase interference of the temperature on ultrasonic guided waves is reduced, and the accuracy of ultrasonic guided wave detection is improved. And high-precision positioning of structural damage is realized in combination with a probability imaging technology. The method is small in data demand and few in execution steps, and the damage positioning efficiency is better than that of a traditional method.

The invention relates to the technical field of pipeline corrosion distribution detection, and discloses a drainage pipeline corrosion detection method and device based on ultrasonic guided waves, and the method comprises the steps: obtaining ultrasonic guided wave signals, sent by a plurality of ultrasonic guided wave transducers, in a target drainage pipeline; the plurality of ultrasonic guided wave transducers are uniformly arranged along the circumferential direction of the inner wall of the target drainage pipeline; performing signal conversion on the ultrasonic guided wave signal to obtain an effective guided wave signal of the corrosion area; performing signal characteristic analysis on the effective guided wave signal of the corrosion area to obtain corrosion area distribution data; performing trend prediction on the corrosion state of the drainage pipeline by using a convolutional neural network model based on the effective guided wave signal of the corrosion area to obtain corrosion development trend data; and obtaining a drainage pipeline corrosion detection result based on the corrosion area distribution data and the corrosion development trend data. According to the invention, the full-process automation of the corrosion detection of the drainage pipeline is realized, and the detection efficiency and the detection precision are improved.

The invention relates to a pipeline elbow ultrasonic guided wave corrosion thinning imaging method and system, and belongs to the technical field of ultrasonic guided wave nondestructive testing. The method comprises the following steps: establishing a two-dimensional planar non-uniform grid model of the pipeline elbow; determining the positions of the annular transduction arrays at the two ends of the to-be-detected area of the pipeline elbow; performing forward modeling based on the excitation signal; obtaining an objective function about sound velocity based on the simulation result and the observation result; judging whether the target function meets an iteration termination condition or not; according to a judgment result, whether iterative calculation of the target function is carried out is selected; obtaining sound velocity distribution information based on a judgment result and an iterative calculation result; and obtaining a pipeline elbow corrosion thinning image based on the relationship between the frequency thickness product and the sound velocity, the sound velocity distribution information and the two-dimensional plane non-uniform network model. According to the method, the two-dimensional planar non-uniform grid model conforming to the actual size of the pipeline elbow is constructed, and corrosion thinning imaging of the pipeline elbow is accurately achieved on the two-dimensional planar non-uniform grid model.

The invention discloses a pipeline crack detection method, device and equipment and a medium, and relates to the technical field of pipeline detection.The method comprises the steps that a time sequence signal for detecting a target pipeline is obtained; the time sequence signal comprises a plurality of signal samples, each signal sample corresponds to an ultrasonic guided wave signal at a time point, and the ultrasonic guided wave signal at each time point is an ultrasonic guided wave signal transmitted from one end of the target pipeline and received from the other end of the target pipeline; inputting the time sequence signal into a pipeline crack detection model, and outputting a crack detection result; the pipeline crack detection model comprises a dynamic feature enhancement module, and the dynamic feature enhancement module is used for outputting multi-scale features according to different convolution kernel sizes; and one signal sample corresponds to one sample feature vector. The method can improve the pipeline crack detection accuracy.

The invention relates to the technical field of signal processing, in particular to a pipeline guided wave signal processing method and system. The method comprises the following steps: acquiring a multi-channel ultrasonic guided wave signal through a sensor array circumferentially arranged around a pipeline, and converting the multi-channel ultrasonic guided wave signal into a frequency domain response matrix; calculating a circumferential amplitude distortion degree based on the power spectrum amplitude distribution of each channel; constructing a modal coupling matrix by combining the circumferential amplitude distortion degree and the theoretical wave number of the ideal pipeline; correcting the standard orthogonal modal matrix to generate a modal projection matrix; and decomposing the frequency domain response matrix by using the projection matrix, and separating a structure echo component and a defect signal component. According to the method, the influence of geometric defects such as ovality deviation is evaluated more accurately, energy exchange between modeling modes is accurately established, and orthogonality hypothesis is corrected; modal energy leakage is inhibited, and the decomposition accuracy is improved; signals are effectively separated, coherent noise interference is solved, and the accuracy and reliability of pipeline defect detection are improved.

The invention discloses a bone injury detection method based on ultrasonic guided waves and a multi-branch convolutional neural network, and the method comprises the steps: firstly collecting UGW signals of bone tissues, and carrying out the standardization preprocessing of the UGW signals, so as to eliminate the amplitude difference between different samples; then, data enhancement strategies such as Gaussian noise disturbance and amplitude scaling are adopted, a finite sample set is effectively expanded, and the robustness and generalization ability of the model are improved; a multi-branch convolutional neural network (TB-CNN) structure is constructed, the network designs independent branches for time domain, frequency domain and time-frequency domain input, deep features are automatically extracted from the respective branches, and feature splicing and multi-level information integration are realized in a fusion module; finally, regression prediction of the bone injury depth is realized through a full connection layer, so that continuous value output is obtained, and the severity of the bone injury can be reflected more finely.

The invention relates to the technical field of ultrasonic detection, in particular to a cable aluminum sheath defect grading detection method based on multi-frequency guided waves, which comprises the following steps: determining propagation characteristics of each guided wave mode according to a frequency dispersion curve, and determining excitation frequency for detecting a main mode based on the propagation characteristics; arranging and installing a transducer array on the cable aluminum sheath; according to the coarse screening main modal frequency, low-frequency guided waves are emitted, guided wave echo signals are collected, and low-frequency guided wave coarse screening is carried out to determine a suspected defect interval; and transmitting ultrasonic guided waves to the suspected defect interval according to the fine screening main modal frequency, accurately positioning the suspected defect interval, determining a comprehensive score through multi-feature weighting calculation, and determining a defect grade according to the comprehensive score. Low-frequency guided wave coarse screening is combined with wavelet transform and entropy verification, a suspected defect interval can be quickly locked, high-frequency fine screening extracts multi-dimensional defect features through variational mode decomposition, and accurate positioning of defect positions and scientific judgment of grades are achieved.

The invention is suitable for the technical field of power system maintenance, and provides a magnetostriction longitudinal ultrasonic guided wave excitation device and a ground wire damage detection method, the device comprises a box body and a connecting box slidably arranged on the box body; the mounting seat is fixedly mounted at the bottom of the inner wall of the box body; the group of threaded cylinders are rotationally mounted in the connecting box and on the mounting seat respectively, and threaded rods are mounted on the group of threaded cylinders in a threaded manner; the exciting coil and the receiving coil are respectively arranged on the group of threaded rods and are used for conducting ground wire damage detection; the bias magnetic field device is arranged at the bottom of the box body and is used for providing a bias magnetic field for the excitation coil; and the mounting mechanism is arranged on the box body and the group of threaded cylinders and is used for fixing the exciting coil and the receiving coil on the ground wire. The magnetostriction longitudinal ultrasonic guided wave excitation device and the ground wire damage detection method provided by the scheme have the advantages that the distance between the excitation coil and the receiving coil can be quickly adjusted, the installation efficiency is further improved, the installation process is simplified, and the device is convenient to carry and use.

The invention provides a method for controlling an ultrasonic guided wave propagation mode in a steel rail based on an excitation mode, which belongs to the field of nondestructive testing, and comprises the following steps: constructing a semi-analytical finite element model through geometric parameters and material parameters of the steel rail, and then determining an incident angle, an excitation position and a signal phase difference; and establishing a three-dimensional finite element model according to the determined incident angle, excitation position and signal phase difference, and calculating and verifying an excitation control effect. According to the method for controlling the propagation mode of the ultrasonic guided wave in the steel rail based on the excitation mode, the mode of controlling the incident angle, the excitation position and the signal phase difference is adopted, the excitation efficiency of a target mode is improved, generation of a non-target mode is restrained, noise signals can be reduced in a field test, the detection precision can be improved, and the method is suitable for popularization and application. Good application prospects are realized.