1326 results about "Destructive testing" patented technology

The invention relates to the technical field of image processing, and discloses a dried persimmon quality detection method and system based on machine vision. The method comprises the following steps: performing multi-angle multi-spectral imaging through an industrial camera array to generate a standard image set; extracting quantitative dried persimmon appearance characteristics; the internal quality is subjected to nondestructive testing by using a spectrum; identifying defects and grading; analyzing a quality trend and establishing an early warning mechanism; and correlating process parameters to optimize the production process, and constructing a quality tracing system. According to the invention, comprehensive nondestructive testing of the appearance and internal quality of the dried persimmons is realized, a closed-loop system of quality early warning and process optimization is formed through data analysis and mining, and the quality stability and traceability of dried persimmon production are improved.

Provided is an in-situ high-precision non-destructive testing device for deep-sea parts, the device comprising a bottom plate (1), wherein a linkage base (5) is fixedly mounted on a side surface of the bottom plate (1), a first bearing (6) is provided on the linkage base (5), a rotary rotating shaft (7) is fixedly mounted on the inner side wall of the first bearing (6), the rotary rotating shaft (7) is connected to a driving bevel gear (8), the driving bevel gear (8) meshes with a driven bevel gear (9), a driving threaded shaft (10) is mounted on the driven bevel gear (9), and the end of the driving threaded shaft (10) close to the top surface of a side plate (2) is in transmission connection with the top surface of the bottom plate (1). The driving threaded shaft (10) is rotated to enable a lifting square block (11) in threaded connection with the driving threaded shaft (10) to move upwards, such that a non-destructive testing box is in an inclined position; in addition, by means of a stabilizing assembly, a laser scanner (66) can stably perform a scanning testing operation on parts, thereby avoiding errors in testing results, and improving the accuracy of the testing results. Moreover, the device can simulate a typical marine environment to perform standard testing on deep-sea parts, and can establish a surface damage database for comparison with deep-sea in-situ testing results, thus further improving the reliability of testing results in different marine environments.

The invention relates to the technical field of special equipment nondestructive testing image intelligent identification, and discloses a special equipment nondestructive testing image defect automatic identification method and system, and the method comprises the steps: carrying out the preprocessing based on an anisotropic diffusion mechanism; a local self-adaptive threshold method is adopted to complete preliminary defect area positioning; constructing a scale consistency constrained multi-scale image pyramid; constructing defect morphological parameters; and carrying out defect type identification by fusing fuzzy form constraint and an SVM classification mechanism. In the prior art, a global filtering or fixed threshold segmentation method is mostly dependent, and especially under the conditions of V-shaped grooves, multi-layer weld structures and corrosion perforation defects, accurate identification of irregular, multi-scale and weak-contrast defects cannot be realized. According to the method, the anisotropic diffusion mechanism is introduced, the multi-scale response extraction of scale consistency constraint is combined, and the classification strategy of fuzzy form modeling is fused, so that the accuracy of special equipment image defect identification is improved.

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention relates to the technical field of civil engineering, and discloses a foundation pit slope protection pile concrete strength evaluation and intelligent reinforcement integrated method which comprises the following steps: S1, data acquisition and transmission: acquiring a slope protection pile structure state and environmental parameters in real time through a multi-source sensor network, the sensor network comprises a mechanical sensor, an environment sensor and a hydrological sensor which are deployed in a spatial distribution manner; s2, data preprocessing and feature extraction: performing space-time fusion processing, including noise suppression, space-time reference unification and multi-dimensional feature extraction, on the acquired multi-source sensing data, and generating a structured feature matrix; according to the technical scheme, the multi-source sensor network and the deep learning model are combined, the strength of the foundation pit slope protection pile concrete is monitored and evaluated in real time, the accurate and lossless strength prediction effect is achieved, the problems of destructive testing and precision limitation are avoided, and the test efficiency is improved. The defects of low precision and poor real-time performance under complex geological conditions in the prior art are overcome.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention discloses a nondestructive testing method and system and a data processing method, and relates to the technical field of nondestructive testing. Comprising the steps of obtaining initial detection data of a detected component; establishing at least one reference model according to the initial detection data, and obtaining simulation data corresponding to each reference model; determining target data according to the initial detection data and simulation data; and obtaining subsequent detection data, and comparing the subsequent detection data with the target data to form an evaluation result. According to the invention, high and low frequency mixed excitation signals are adopted, so that the penetrating power and resolution of detection are improved, surface defects and buried defects can be detected at the same time, and the comprehensiveness and accuracy of detection are remarkably enhanced; a picked complex response signal is decoupled by using an orthogonal phase locking method, a magnetic field signal and an electric field signal are effectively separated, the signal-to-noise ratio and the detection precision are improved, and accurate discrimination of defect types, depths and orientations is realized by establishing a reference model and acquiring analog data.

The invention belongs to the technical field of nondestructive testing, and discloses a vector coherence factor-based thick-wall austenitic stainless steel welding seam full-focus imaging method, which comprises the following steps of: acquiring full-matrix data through a TRL area array probe, constructing an imaging area, calculating a spatial relationship, optimizing a propagation path in combination with a Fermat principle, and obtaining a full-focus imaging result; the method comprises the following steps of: firstly, extracting an instantaneous phase of an ultrasonic signal to construct a vector coherence factor, reconstructing an imaging data matrix according to the coherence factor, and obtaining a phase coherence weighted full-focusing imaging result, so as to ensure that each pixel point can obtain an optimal propagation path, thereby generating a full-focusing image data matrix by adopting a point-by-point delay superposition technology; the method can effectively improve the signal-to-noise ratio and positioning and quantification precision of the thick-wall austenitic stainless steel weld joint detection image, and has good engineering application value.

The invention discloses a nondestructive testing method for a welding seam of a steel structure. The method comprises the steps that firstly, morphology data of the surface of a building steel structure are obtained through a high-precision laser scanner and an electromagnetic induction technology; coating thickness distribution and weld surface roughness are analyzed based on the morphology data, and a surface information matrix is constructed; then automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the adaptive parameter regulation and control model and executing detection to obtain a preliminary ultrasonic echo signal; the time-frequency domain difference between the preliminary echo and the reference signal is calculated through an echo signal compensation model, and secondary compensation is completed to obtain an optimized ultrasonic echo signal; fusing the optimized echo signal, the morphology data and the surface information matrix to construct a detection data set, and adopting a pre-trained steel structure detection model to realize defect detection; and finally, evaluating the confidence of a detection result by adopting a three-dimensional confidence evaluation model, performing grading, and performing local re-detection and parameter adjustment on a low-confidence region to form closed-loop optimization. According to the method, the surface state of the steel structure can be adaptively matched, the accuracy of echo signals and the defect detection precision are improved, and the detection reliability is guaranteed through three-dimensional confidence evaluation.

The invention relates to the technical field of ultrasonic detection, in particular to a building steel structure strength ultrasonic phased array nondestructive testing method which comprises the following steps: acquiring morphology data of the surface of a building steel structure; analyzing coating thickness distribution and weld surface roughness of the surface of the building steel structure, and constructing a surface information matrix; automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the self-adaptive parameter regulation and control model, and performing ultrasonic phased array detection to obtain a preliminary ultrasonic echo signal; adopting an echo signal compensation model to perform secondary compensation on the preliminary ultrasonic echo signal through the difference between the reference signal and the preliminary ultrasonic echo signal to obtain an optimized ultrasonic echo signal; fusing the optimized ultrasonic echo signal, the morphology data and the surface information matrix, and performing defect detection on the building steel structure by using the building steel structure detection model; evaluating a detection result, and dividing a confidence level; and carrying out local re-detection and parameter adjustment on the low-confidence region to realize closed-loop optimization.

The invention discloses a high-sensitivity metal micro-strain sensor label and a detection method thereof, which are applied to the field of nondestructive testing and the field of structural health monitoring. The problems that an existing tag antenna is low in strain detection sensitivity and difficult in micro-strain detection are solved. The high-sensitivity metal micro-strain RFID sensing tag is attached to the surface of a component, when the tag deforms, the impedance change of the tag is Zsened, and the radar cross section changes accordingly; the reader can remotely monitor antenna parameters according to the change of the RCS0, the RCS1 and the intensity while communicating with the tag, so that the change of the intensity RSSI of an echo signal is deduced, and the deformation of the tag is indirectly known; when the impedance of the tag antenna changes, the resonant frequency of the tag changes accordingly, and the deformation information of the tag can be indirectly obtained by analyzing the deviation of the resonant frequency.

The invention relates to the field of nondestructive testing, in particular to a flaw detection method for a large steel box girder and a related product, and the flaw detection method comprises the following steps: importing a three-dimensional model of the large steel box girder, and analyzing the three-dimensional model to obtain geometric feature information of the steel box girder; generating a scanning path of the ultrasonic probe based on the geometric feature information; an ultrasonic probe is controlled to scan the large steel box girder according to the scanning path, and ultrasonic echo signals are collected; the ultrasonic echo signals are processed, and the welding defect positions of the steel box girder are recognized and positioned; according to the method, the three-dimensional model of the steel box girder is imported and analyzed, automatic extraction of the detection area and the welding seam information is achieved, the optimized probe scanning path is generated by applying the self-adaptive path planning algorithm, efficient and full-coverage detection of the large complex structural part is achieved, and missing detection is avoided.

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 discloses a flow-guiding-free laminar flow water column nozzle, a nondestructive testing system and application of the flow-guiding-free laminar flow water column nozzle, and belongs to the field of industrial manufacturing. The flow-guiding-free laminar flow water column nozzle comprises a nozzle body, a nozzle head is detachably installed at the front end of the nozzle body through a connecting flange, and a filter element capable of greatly increasing the length of a laminar flow water column is arranged in the nozzle body through a clamping base; at least two through holes communicated with an internal channel of the nozzle body are formed in the nozzle body, a first flow channel, a second flow channel, a third flow channel and a fourth flow channel are sequentially formed in the nozzle body and the internal flow channel of the nozzle head in the water flow spraying direction, the filter element comprises a middle section, and a net face section is arranged at the end, away from the nozzle head, of the middle section. The end, close to the nozzle head, of the middle section is provided with a sunken section. According to the flow-guide-free laminar flow column nozzle, the nondestructive testing system and the application thereof, the filter element matched flow channel streamline combination capable of greatly improving the length of a laminar flow column can be achieved, the filter element can be manufactured through 3D printing, and the fund cost and the time cost are reduced.

The invention discloses a single crystal turbine blade crystal orientation defect detection system based on adaptive optics, particularly relates to the field of nondestructive testing, and is used for solving the problem that thermal damage and signal consistency are difficult to consider in acoustic imaging of complex structural parts. Through partition energy threshold setting, dynamic pulse planning, photoacoustic processing and unified coordinate cross-subarea data synthesis, local thermal damage caused by excessive concentration of energy is effectively avoided in the material detection process, and the requirement for collecting high-bandwidth signals can be met in a large range. The signal-to-noise ratio and the detection efficiency are improved by regulating and controlling pulse energy and a scanning path, the amplitude comparability and the image coherence among regions are ensured through signal normalization correction and subregion image set splicing, high-resolution and comprehensive-coverage acoustic mapping is output, the crystal orientation defects of the single crystal turbine blade are positioned and evaluated, the detection precision is improved, the measurement period is shortened, and the method is suitable for large-scale popularization and application. And the method has an applicable value for aero-engine parts requiring rapid and high-precision nondestructive detection.

The invention discloses a nondestructive testing device and a testing method for heterogeneous parts of engineering machinery. The method comprises the following steps: acquiring material attributes and historical failure data of a heterogeneous component, extracting defect form and position feature parameters, constructing a statistical distribution model and generating a typical feature set; according to the statistical distribution model and the material attributes, generating a virtual defect data set containing form and position features by adopting numerical simulation; performing finite element analysis on the virtual defect data set, calculating a stress concentration effect and an expansion trend, and outputting a defect harmfulness quantitative index; and if the harmfulness index exceeds a preset threshold value, marking the defect as a high-risk defect, and generating a preferential simulation target set and the like. According to the method, all links of nondestructive testing are covered from data acquisition to standard construction, a set of complete working process is formed, and the nondestructive testing problem of the heterogeneous parts of the engineering machinery can be systematically solved.

The invention discloses a non-destructive testing stress evaluation method based on a nonlinear ultrasonic Lamb wave static component, and belongs to the technical field of stress detection, and the method comprises the following steps: S1, constructing a Rayleigh-Lamb equation introducing an elastomer nonlinear theory, and further determining a Lamb wave frequency dispersion curve of a to-be-detected plate, the excitation frequency and the excitation mode of the excitation source are determined according to the Lamb wave frequency dispersion curve; s2, exciting lamb waves for determining excitation frequency and excitation mode at one end of the stress detection area of the plate to be detected, and receiving and extracting corresponding signals through the other end of the stress detection area; s3, inverting the phase of the extracted signal, and exciting and receiving the inverted signal again; and S4, processing the received signal, and calculating an ultrasonic nonlinear coefficient as an evaluation parameter of the stress state of the plate to be detected. According to the invention, the selection of the fundamental wave frequency and mode is more flexible, the related parameters of the high-attenuation material can be measured, and the early damage and stress state in the material can be effectively evaluated.

The present invention relates to a bearing ball inspection system configured for automated non-destructive testing of spherical components. In an exemplary embodiment, a bearing ball is delivered to an inspection point by a rail guide and supported by a positioning wheel configured to rotate the bearing ball during imaging. Cameras capture images of different surface regions under controlled illumination provided by ultraviolet or white-light sources. A control system comprising a microprocessor and memory executes a machine learning model to analyze the images and detect surface defects without requiring manual interpretation. Based on the analysis, a sorting mechanism directs the bearing ball to a pass or fail location. The system further includes communication interfaces configured to transmit inspection results to a remote computing device and receive control instructions. This integrated architecture enhances inspection reliability, improves throughput, reduces inspection variability, and supports centralized management of inspection systems across distributed production sites.

The invention provides an infrared pulse thermal radiation grinding burn nondestructive testing method and system, and belongs to the technical field of nondestructive testing. Comprising the following steps: inputting an excitation signal to an induction coil, and inducing a pulsed eddy current in a tested part for heating; a thermal infrared imager and a depth camera are used for obtaining a thermal response temperature matrix of a measured area of the part and a three-dimensional point cloud of the measured area; then, constructing a thermal response model of the part; extracting a time sequence temperature curve of the heating stage and the natural cooling stage of the detected area, and establishing a grinding burn discrimination function to judge whether a grinding burn area exists or not; and the thermal infrared imager and the depth camera are calibrated, the grinding burn area under the coordinate system of the thermal infrared imager is projected into the three-dimensional point cloud of the depth camera, the position of the grinding burn area under the world coordinate system is obtained, and detection and positioning of the grinding burn area are achieved.

The invention discloses an eddy current testing reliability evaluation method and system based on signal space distribution characteristics, and belongs to the technical field of nondestructive testing. According to the method, eddy current signal space distribution characteristics and the detection probability (POD) and the false detection rate (PFA) in nondestructive testing reliability evaluation indexes are fused, and the method is used for comprehensively evaluating the defect detection capacity and the reliability of a detection system. By introducing the Gini coefficient to quantitatively detect the imbalance of signal space distribution, the method effectively characterizes the distinguishability of signals in a defect state and a defect-free state, and replaces traditional POD and PFA analysis methods based on single-point amplitude or phase characteristics. Compared with a traditional method, the Gini coefficient can more comprehensively represent the overall distribution characteristics of the signals, the influence of noise is small, and the method is particularly suitable for the eddy current detection environment under the condition of the low signal-to-noise ratio. Besides, a POD and PFA relation curve is constructed based on Gini coefficients, ROC analysis is performed, the defect detection rate can be effectively increased, the false detection rate can be reduced, and the reliability of the nondestructive testing system can be evaluated more efficiently and accurately.

The invention discloses a gear defect detection system and method based on gear pump machining, and relates to the technical field of nondestructive testing. The gear defect detection platform is in communication connection with a magnetization preprocessing module, a magnetic signal acquisition module, a magnetic signal processing module, a gear defect analysis module and a defect visual labeling module, and all the modules are in electric signal connection; and the magnetization preprocessing module is used for customizing a magnetization scheme according to the material and structural characteristics of the gear. According to the method, the signal separation and feature extraction technology is adopted, independent features of different defect types can be effectively extracted from magnetic signals, the defect classification model is constructed, then surface damage, geometric deformation and fatigue cracks are accurately distinguished, quantitative analysis is conducted on the severity of the defects, the defect classification accuracy is improved, and the defect classification efficiency is improved. And classification errors caused by overlapping of magnetic signals in a traditional method are avoided.

The invention relates to the technical field of apple detection, in particular to apple quality nondestructive testing equipment which comprises a fruit tray, a rotating shaft, a driving shaft sleeve, a lantern ring, a first connecting plate, a supporting plate, a rotating cylinder, a linkage shaft, an inner ring, an outer ring and protrusions. According to the apple quality nondestructive testing equipment, a first driving source drives a rotating shaft to rotate, the rotating shaft drives a driving shaft sleeve to rotate through a first limiting block and a first limiting groove, the driving shaft sleeve drives a supporting shaft to rotate through a screw rod section and a linkage wheel, and the supporting shaft drives rotating cylinders to rotate, so that the two rotating cylinders rotate; and the bulges on the outer ring can enable the apples to be in a rolling state, so that the detection machine can conveniently detect the apples at multiple angles.

The invention provides a device and method for microwave nondestructive testing of a PE pipeline electric melting joint, the device comprises a walking module, a signal module and a control module, the walking module comprises a fixed part and a moving part, the fixed part comprises a plurality of pipeline clamps, and the pipeline clamps wrap the periphery of a PE pipeline; any two pipeline clamps are connected through a clamp connecting fitting, the moving part comprises a rotary moving mechanism, an X-axis moving mechanism and a Z-axis moving mechanism, and the signal module and the control module are connected and both installed on the walking module. According to the method, the defect detection precision is improved, the detection safety is enhanced, online real-time monitoring is realized, manual intervention is reduced, the adaptability and flexibility are improved, and comprehensive data support is provided.

The invention relates to a method and a system for designing a cable nondestructive testing probe based on tomography. The method comprises the following steps: firstly, establishing a finite element simulation model of a multi-layer cable structure, annularly configuring a coil sensor array at the periphery of a cable to form a tomography detection device, setting defects between layers of the finite element simulation model, and obtaining boundary voltage response data of the coil sensor array; constructing and solving a two-dimensional sensitive field mathematical model according to the boundary voltage response data, solving a sensitivity matrix of the electromagnetic parameter variation and the boundary voltage through a numerical calculation method, and constructing a nonlinear inverse problem model in combination with the boundary voltage response data; solving the inverse problem model to obtain a reconstructed image; and finally, determining an optimal probe design scheme based on the quality of the reconstructed image. Compared with the prior art, the method has the advantages that the sensitivity and the spatial resolution of defect detection are remarkably improved while non-contact measurement is guaranteed, and the method can meet the nondestructive detection requirements of cables of different specifications.

The invention provides an aero-engine blade damage assessment method based on an elastic metamaterial structure, and belongs to the technical field of nondestructive testing, and the method comprises the steps: S1, determining the size of a detection region according to an actual detection demand; s2, determining the size of the metamaterial array; s3, determining the structure of the metamaterial unit; s4, calculating a meta-material unit energy band diagram through a finite element method, and determining an excitation signal frequency; s5, arranging a metamaterial array and a sensor array; and S6, calculating pixel values of all the detection areas by adopting a DAS algorithm, carrying out positioning evaluation on the damage according to the pixel value distribution condition of the detection areas, and generating an image. According to the method, the wave field of the Lamb wave in the variable-thickness blade is controlled by using the elastic metamaterial structure with a special size, so that efficient and accurate positioning and damage identification of various types of damages are realized.

The invention discloses a method for detecting a black leather material through eddy current, and relates to the technical field of nondestructive testing. Shot blasting current and revolution are controlled through shot blasting pretreatment, so that the surface roughness Ra of the black leather material is reduced to be less than or equal to 6.3 microns, a qualified detection surface is provided for eddy current flaw detection, and the detection precision is prevented from being influenced by the surface state; a special sample rod containing longitudinal and transverse artificial defects is prepared, the sensitivity of a flaw detection system to defects in different directions can be synchronously verified, the detection comprehensiveness is ensured, eddy current flaw detection parameters are optimized, defect signals and noise signals are effectively distinguished by adjusting a current gate, optimizing filtering setting and maintaining phase difference stability, and the signal recognition accuracy is improved; according to the multi-frequency eddy current detection, three excitation signals with different frequencies are applied, so that the coverage detection of the defects of different depths of the black skin material is realized, the defect detection rate is improved, eddy current flaw detection core parameters are dynamically adjusted, the influence of surface non-uniformity on an eddy current field is compensated, and the detection stability is ensured.

The invention relates to the technical field of ultrasonic non-destructive testing, and discloses an intelligent ultrasonic non-destructive testing device which comprises a body mechanism, the body mechanism comprises an operation table, a displayer, an ultrasonic testing instrument and a mounting frame are fixedly mounted on the upper surface of the operation table, and an electric sliding rail is fixedly mounted on the inner top wall of the mounting frame; a mechanical arm is fixedly installed on the bottom face of the electric sliding rail, a probe is fixedly installed on the bottom face of the mechanical arm, and a processing mechanism is arranged above the operation table. The processing mechanism comprises a smearing unit, and the smearing unit is located below the mechanical arm and used for evenly smearing the coupling agent on the to-be-detected object according to the detection speed. According to the intelligent ultrasonic nondestructive testing device, the output efficiency of a coupling agent is automatically adjusted according to the detection rate, so that the output coupling agent is uniformly smeared on the surface of an object to be detected, the phenomenon of poor coupling between a probe and the object to be detected is avoided, and the supply efficiency of the coupling agent is improved.

The invention relates to the technical field of nondestructive testing and performance analysis of engineering materials, in particular to a nondestructive testing and performance degradation analysis method for bridge structure materials. The method comprises the following steps: acquiring material nondestructive testing monitoring data, and executing unified alignment, noise portraying and quality labeling; carrying out combined noise reduction and component evaluation, and generating a noise reduction parameter snapshot; extracting a marginal spectrum and characteristic energy ratio, and establishing a material characterization characteristic index dictionary; calculating a variation index, judging material performance degradation, and constructing a supervision signal; and training the prediction network, generating a trend and a residual error, and completing early warning fusion by adopting a time neighborhood consistency strategy to obtain a material performance degradation early warning level sequence. According to the method, continuous nondestructive monitoring and quantitative characterization of material levels are realized, characteristics and parameters are traceable, cross-channel and cross-time evaluation is consistent, prediction and early warning are sensitive and stable, and the method is suitable for long-term service safety evaluation.

The invention relates to a near-infrared coal quality quantitative analysis model construction method, and belongs to the technical field of coal quality analysis. In order to solve the problems that a traditional coal quality analysis method is tedious in operation, time-consuming, destructive and difficult to meet rapid nondestructive detection requirements, the invention aims to realize rapid, accurate and quantitative detection and analysis of moisture, ash and volatile components in coal by combining a near infrared spectrum analysis technology with a deep learning algorithm. The method comprises the following specific steps: collecting and preparing a coal sample according to the national standard, and measuring moisture, ash and volatile components; the method comprises the following steps: acquiring spectral data by using a near infrared spectrometer, marking the spectral data, performing smoothing, multiplicative scatter correction and normalization processing, and dividing a training set and a test set according to a Kennard-Stone algorithm; and constructing a cascaded integrated deep learning prediction model of CNN-LSTM, and performing training, evaluation and optimization to predict coal quality indexes. According to the method, data are obtained based on a strict national standard experiment method, the accuracy of quantitative analysis of coal quality and the robustness of the model are improved by combining the near infrared spectrum technology with the deep learning model, and powerful support is provided for quality control and reasonable utilization of resources in the coal industry.

The invention discloses an electric spark nondestructive testing evaluation method for the integrity of a waterproof layer of a cable trench, and belongs to the technical field of nondestructive testing, and the method comprises the following steps: applying a multi-frequency electric signal through a preset moving electrode; the initial contact pressure of the moving electrode and the surface of the waterproof layer is obtained in real time, the pressure of the contact position of the moving electrode is adjusted, an electric spark signal generated at the defect position of the cable trench waterproof layer by a multi-frequency electric signal applied by the moving electrode is captured, and characteristic parameters of the electric spark signal are extracted to generate current electric spark characteristic data; utilizing the current electric spark characteristic data to distinguish defect types so as to generate a defect evaluation result; and outputting defect distribution information and triggering an acousto-optic alarm according to a defect evaluation result. According to the method, a detection mode of combining multi-frequency electric signal excitation and electric spark signal feature extraction and analysis is adopted, waterproof layer defect types can be distinguished, the severity of the waterproof layer defects can be evaluated, and the detection accuracy is improved.