244 results about "Ultrasonic guided wave" patented technology

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 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.

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 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 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 belongs to the technical field of ground wire steel core detection, and particularly relates to a ground wire steel core detection system based on digital enabling, which comprises a detection device module, a data acquisition and processing module, a data analysis and intelligent identification module and a visualization and early warning platform module, the detection device module is integrated with a multi-mode sensing unit (a magnetizing device, a Hall array sensor, an ultrasonic guided wave transducer and an optical fiber sensing array) and a displacement positioning assembly, so that comprehensive acquisition and accurate positioning of damage signals are realized; the data analysis module is fused with a CNN-LSTM deep learning model to complete defect automatic identification and residual life prediction; the visual platform realizes hierarchical display and intelligent early warning based on digital twinning, and is also provided with a block chain module to guarantee data credibility; the system is compatible with portable inspection and on-line monitoring, is adaptive to the existing AEMR-I system, can improve the damage detection precision, the intelligent level and the operation and maintenance efficiency, and provides support for the safe operation of a power grid.

The invention discloses an overhead ground wire corrosion assessment method, system and device based on ultrasonic guided waves and a medium, and belongs to the technical field of overhead ground wire corrosion assessment, and the method comprises the steps: building a guided wave propagation model of a steel strand, and completing the design of guided wave excitation parameters and sensor arrangement based on the guided wave propagation model; exciting ultrasonic guided waves through an excitation device and performing signal acquisition to obtain a steel strand guided wave propagation signal; carrying out signal preprocessing and feature extraction on the collected guided wave signal; inputting the extracted feature information into a deep learning model to complete identification and evaluation of the corrosion state; and outputting a corrosion position, a corrosion type and a corrosion degree based on an identification result. Compared with the prior art, the method has the advantages that the detection precision, the recognition capability, the adaptability, the anti-interference capability, the intelligent level and the like are obviously superior, and reliable technical guarantee is provided for health monitoring of the overhead ground wire and power grid safety by introducing deep learning, an attention mechanism, transfer learning and a data enhancement strategy.

The invention provides a flange bolt group damage identification method, electronic equipment and a storage medium. The method is used for carrying out damage identification on a bolt on a target flange plate. A piezoelectric driving piece or a piezoelectric sensing piece is arranged between every two adjacent bolts on the target flange plate, and the piezoelectric driving piece and the piezoelectric sensing piece are arranged on the two sides of each bolt on the target flange plate respectively. The method comprises the following steps: controlling each piezoelectric driving sheet to sequentially excite an ultrasonic guided wave signal; receiving the ultrasonic guided wave signal by using each piezoelectric sensing sheet to obtain a real-time ultrasonic guided wave signal; for each real-time ultrasonic guided wave signal, extracting a real-time dominant frequency modal signal in the signal; based on the difference between the real-time dominant frequency modal signal and the reference dominant frequency modal signal, determining the damage condition of the bolt on the target flange plate; wherein the dominant frequency modal signal comprises a preset number of modal components with the maximum energy in the modal components corresponding to the ultrasonic guided wave signal. According to the method, the damage condition of each bolt can be accurately judged.

The invention discloses an ultrasonic guided wave damage ellipse imaging positioning method and equipment for a layer decreasing composite material laminated plate. The method comprises the following steps: S1, acquiring thickness change direction information of the laminated plate and position coordinates of an excitation transducer and a receiving transducer; s2, according to the material characteristic parameters, simulating and calculating the propagation time of ultrasonic guided waves transmitted from an excitation transducer to any point to generate a first theoretical propagation time map, and simulating and calculating the propagation time of the ultrasonic guided waves transmitted from any point to a receiving transducer to generate a second theoretical propagation time map to generate a second theoretical propagation time map; coupling influence of thickness change and material anisotropy on guided wave propagation is considered in the simulation process; s3, transmitting an ultrasonic guided wave signal, and extracting the actual flight time of a damaged wave packet; s4, calculating theoretical total propagation time, and determining a candidate area where damage exists; and S5, on the basis of repeated operation of the multiple groups of transducer pairs, taking a plurality of candidate region intersections to determine a final damage position. The objective of the invention is to solve the positioning error problem caused by the structural characteristics of a laminated plate in a traditional algorithm, and to realize more accurate damage detection and positioning.

The invention discloses a method for quantitatively evaluating the three-dimensional shape of a thinning defect in a plate under ultrasonic guided wave field undersampling scanning, belongs to the field of industrial nondestructive testing, and particularly relates to high-precision three-dimensional shape reconstruction and thickness loss quantification of a corrosion defect in a metal plate shell structure (such as aerospace skin, a pressure vessel wall, an oil pipeline and the like). According to the method, sparse sampling guided wave field reconstruction, full waveform inversion (FWI) and priori knowledge constraint are fused, Shannon sampling theorem limitation is broken through, efficient measurement of corrosion depth, contour and distribution under the conditions of a single excitation source and a limited view angle is achieved, and the method is suitable for rapid online detection in a complex industrial environment.

The invention discloses an overhead ground wire monitoring ultrasonic guided wave signal denoising method, system equipment and a medium. The method comprises the following steps: acquiring and preprocessing an original signal of an overhead ground wire; adding noise into the preprocessed signal to obtain a mixed signal; constructing a de-noising auto-encoder model fused through a one-dimensional convolutional neural network and a full-connection layer; inputting the mixed signal into a trained denoising auto-encoder model; an input signal is mapped to a hidden layer of the model through a model encoder to obtain a signal feature; and performing data reconstruction on the signal features in the hidden layer through a model decoder to obtain a de-noised signal. According to the method, the interference noise is added into the input signal for training, so that the robustness of the implicit layer on the interference noise existing in the input signal is improved; through a one-dimensional convolutional neural network and full-connection fusion mode, feature information in the ultrasonic guided wave signals is captured more accurately, the problem of signal distortion is avoided, and therefore the recognition capacity of tiny defects in the overhead ground wire is improved.

The invention discloses a pipeline defect detection method and system, and belongs to the technical field of pipeline defect detection. The pipeline defect detection method comprises the following steps: acquiring a preprocessed magnetic flux leakage signal and an ultrasonic guided wave signal; performing feature extraction on the signal to obtain a first feature subjected to multi-scale enhancement processing and a second feature subjected to convolution and attention mechanism processing; fusing by adopting a strategy including mutual information calculation and adaptive weight to obtain cross-modal fusion features; and performing multi-task detection and correction based on the fusion features to obtain a detection result indicating the type, the position and the size of the defect. According to the method, the limitation of single-mode detection can be overcome, and the accuracy of defect type identification, the positioning precision and the size quantification reliability are remarkably improved through dynamic weight distribution and physical constraint verification.