1729 results about "Destructive testing" patented technology

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

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 discloses a bridge pile foundation stress nondestructive testing method and system, the system comprises a sensor arrangement module, a data acquisition module, a data transmission module, a data processing module, a data analysis module, a result display and early warning module, a calibration module, a verification module and the like, sensors are various in type and high in precision, data acquisition multichannel synchronization is realized, and transmission is stable and reliable. Various technologies are used for data processing to construct a model, and a result is visually displayed and pre-warned. The system is further provided with an environment monitoring and structure health assessment module, comprehensive and accurate detection is guaranteed through an innovative formula and method, and efficient operation of the system is guaranteed through cooperative work of all modules. Through multi-module cooperation and innovative design, bridge pile foundation stress high-precision detection, environmental factor analysis and structure health assessment are achieved. Early warning can be carried out in time, bridge safety is guaranteed, a scientific basis is provided for maintenance management, and the reliability and safety of bridge operation are improved.

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 present invention discloses a pipeline defect detecting and positioning method based on multi-sensor information fusion, which belongs to the technical field of pipeline non-destructive testing. The method comprises the following steps of performing state segmentation processing on a pipeline according to a pipeline defect detection signal and / or a three-axis attitude signal of a pipeline detection equipment in the pipeline, positioning a pipeline defect signal in the pipeline defect detection signal to a pipeline section in a corresponding state, and calculating a position of the pipeline defect signal in pipeline section in each state according to a running speed, the three-axis attitude signal, and mileage information of the pipeline detection equipment in the pipeline section in the corresponding state. According to the invention, the pipeline is segmented so that the pipeline defect can be preliminarily positioned in the pipeline section in a certain state.

The invention relates to the technical field of intelligent detection and quality grading, in particular to an automatic fish maw detection and grading system which integrates a spectral imaging module, a trace component analysis module, a physical property detection module and the like to realize comprehensive detection of external characteristics and internal components of fish maw. Through a multi-band light source and a high-resolution camera, an image from visible light to near-infrared light is obtained, and key indexes such as collagen, mineral substances and nutritional ingredients are accurately analyzed by utilizing a nondestructive testing technology; the feature extraction and fusion module adopts weighted mutual information and a self-adaptive penalty factor model to optimize features, the intelligent grading decision module provides accurate grading based on a feature intelligent discrimination method, and the self-adaptive control module dynamically adjusts equipment parameters to ensure the process to be stable. The system has the advantages of high efficiency, accuracy and intellectualization, and provides technical support for standardized grading and industrial production of the fish maw.

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 discloses a backfill soil foundation compactness nondestructive testing method based on an intelligent sensor. The backfill soil foundation compactness nondestructive testing method comprises the following steps of S1, the intelligent sensor, S2, a GPS positioning system, S3, a wireless transmission network and S4, an information monitoring and collecting system. An on-site road roller is modified, an RTK-GPS receiver, a wireless communication antenna and an acceleration sensor are installed, accurate measurement of the position and the running speed of the rolling machine can be achieved through an automatic rolling system based on RTK-GPS and closed-loop feedback control, the acceleration sensor is fixed to a vibrating wheel, vertical acceleration signals of the vibrating wheel are collected in real time, and the vibration speed of the vibrating wheel is measured. A CMV value and a field compaction degree evaluation result are analyzed in real time through a background development system, then a correlation relation is established between a compaction degree value and the CMV value which are measured by using a standard compaction degree inspection method in a test section, and real-time compaction degree monitoring control can be carried out.

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 is suitable for the technical field of nondestructive testing of non-coal mine internal investigation and safety detection, and provides a mine working face roof deformation monitoring method based on a laser radar, which is specially used for a complex environment of a mine working face. The method comprises the following steps: S1, collecting laser radar data; s2, preprocessing the point cloud data; s3, deformation monitoring of a working face top plate; and S4, deformation analysis of the working face roof. The method comprises the following steps: after specific data preprocessing, constructing a triangular mesh model for scanned two-stage point clouds by utilizing triangular mesh division, performing central point extraction on each triangular surface of a second-stage triangular mesh model to form a second-stage central point cloud, performing distance calculation on the second-stage central point cloud and the first-stage triangular mesh model to form a second-stage central point cloud, and calculating the distance between the second-stage central point cloud and the first-stage triangular mesh model; the method is used as a basis for deformation quantity and deformation risk assessment. According to the method, the nondestructive testing technology is utilized, the rock wall in the mine does not need to be damaged, and the detection safety is improved under the condition that the labor cost and the workload are reduced.

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 belongs to the technical field of nondestructive testing, and discloses an acoustic emission, DIC and infrared technology combined integrated nondestructive testing method which comprises the following steps: acquiring damage related data of a material or a component; the method comprises the following steps of: acquiring data, preprocessing the acquired data, preprocessing acoustic emission data through a band-pass filter and feature extraction, carrying out DIC emission data preprocessing by using Brown distortion model correction image and distortion coefficient feature extraction, and carrying out infrared data preprocessing module processing through infrared image, radiance and black body calibration and feature extraction; and carrying out data fusion on the preprocessed data through a Kalman filter and a corresponding sensor. According to the method, combined application of the acoustic emission technology, the DIC technology and the infrared technology is integrated, mutual complementation can be achieved through combined application of the acoustic emission technology, the DIC technology and the infrared technology, limitation of a single technology is overcome, the damage state of the material can be recognized and evaluated more accurately, and data processing efficiency and accuracy are improved.

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 invention discloses a destructive test method, device, equipment and medium for a flexible direct-current converter valve, and the method comprises the steps: firstly constructing an IGBT test submodule according to the long-term through-current capability of an IGBT device in a flexible direct-current converter valve submodule; the device comprises a test object sub-module of which the IGBT has long-term through-current capability and a test object sub-module of which the IGBT does not have long-term through-current capability. Secondly, respectively carrying out upper and lower IGBT direct connection destructive tests on the two test object sub-modules, carrying out bypass switch false connection destructive tests on the test object sub-modules with IGBT long-term through-current capability, and respectively carrying out bypass switch refusal destructive tests on the two test object sub-modules; and finally, judging the explosion-proof performance of the flexible direct-current converter valve according to the test phenomenon of the test to obtain a test result. According to the invention, the requirement of the destructive test of the flexible direct-current converter valve can be better met, and the reliability of the test result is improved.

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.