5708results about "Material magnetic variables" patented technology

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention relates to the technical field of defect detection, and discloses a pipeline repairing method and system based on defect detection. The method comprises the following steps: performing acoustic detection on a pipeline based on a multi-frequency sound wave signal to obtain pipeline sound field data; carrying out electromagnetic wave scattering detection on the pipeline to obtain electromagnetic scattering data; constructing a sound field distribution diagram in the pipeline and an electromagnetic property distribution diagram on the surface of the pipeline; performing feature extraction on the sound field distribution diagram to obtain a first detection feature set, and performing feature extraction on the electromagnetic characteristic distribution diagram to obtain a second detection feature set; inputting the first detection feature set and the second detection feature set into a space-time diagram convolutional network model for pipeline defect diagnosis to obtain a defect position and a defect type; and performing causal reasoning and repair scheme matching on the pipeline according to the defect position and the defect type to obtain a target pipeline repair scheme, thereby improving the accuracy of pipeline defect detection, thereby improving the accuracy of pipeline repair.

The invention provides a color steel plate coating thickness dynamic monitoring method and system based on artificial intelligence. According to the method, the temperature distribution data of the surface of the color steel plate coating is obtained, the thermal response map is generated in a pulsed eddy current excitation mode, then the temperature distribution data and the thermal response map are subjected to time sequence correlation processing, the heat conduction characteristics are extracted, and then the heat conduction characteristics are synchronously analyzed; color steel plate coating defect types and thickness abnormal grades are distinguished through a dynamic weight distribution mechanism, a detection result containing defect positions, sizes and thickness deviation values is generated, and finally a coating thickness dynamic compensation instruction is generated according to the detection result; according to the technical scheme provided by the invention, the online control precision and the production efficiency of the coating quality are remarkably improved, and a closed-loop feedback system from detection to control is formed.

A prism for reflecting a laser includes: a single mounting cap at a first end of the prism, and first to seventh trihedral corner (TC) reflectors, each including a reflective surface including: three side edges, and three corners at respective intercept points between the side edges, wherein the seventh TC reflector, among the first to seventh TC reflectors, is on a second end of the prism opposite to the first end of the prism.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention relates to the technical field of surface defect detection, and discloses a metal surface defect detection method and device, equipment and a storage medium. The method comprises the following steps: performing multi-frequency eddy current scanning on the surface of a metal workpiece to obtain a four-dimensional original data set containing space coordinates, excitation frequency and impedance response; performing adaptive filtering and compensation on the four-dimensional original data set to obtain target signal data; performing adaptive imaging and frequency fusion on the target signal data to obtain a two-dimensional image; inputting the two-dimensional image into a lightweight adaptive convolutional neural network for multi-dimensional feature extraction to obtain a multi-dimensional feature map; performing texture analysis and anomaly detection on the multi-dimensional feature map to obtain a comprehensive anomaly score map; and carrying out multi-modal data fusion and decision making on the comprehensive abnormal score graph to obtain a metal surface defect detection result, and improving the detection accuracy of the metal surface defect.

The invention relates to the technical field of pipeline maintenance, and discloses a pipeline inner wall corrosion detection method and system suitable for hydraulic engineering, and the method comprises the steps: obtaining a plurality of sensor data from a sensor assembly arranged in a to-be-detected pipeline; performing feature extraction to obtain detection data features; performing time-space synchronization fusion to obtain fusion features; acquiring historical corrosion data and fusion features of the inner wall of the pipeline and inputting the historical corrosion data and the fusion features into the long-short-term memory network model to obtain a first corrosion prediction result; generating a second corrosion prediction result through a random forest algorithm; generating a third corrosion prediction result by a gradient boosting tree algorithm; and inputting the three prediction results into a preset detection model to obtain a detection report. According to the method, multiple sensor data are obtained and fused to obtain the fused features, multiple factors influencing corrosion are comprehensively referenced, prediction analysis is carried out through multiple algorithms according to the fused features to obtain three prediction results, the detection report is generated, and the accuracy of the detection result is effectively improved.

The invention provides an intelligent material damage diagnosis method and system based on magnetic characteristic data fusion, and relates to the technical field of nondestructive testing, and the method comprises the following steps: collecting magnetic characteristic data by using a magnetic stress detection robot; performing multi-scale decomposition on the data to construct a magnetic domain evolution characteristic spectrum; inputting the characteristic spectrum into a composite neural network to extract a material damage characteristic matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multilayer belief transfer network; and realizing damage calculation through temperature field modulation and self-adaptive weight distribution to obtain the damage grade and the residual life. According to the invention, accurate diagnosis of the material damage in the welding seam area of the pressure vessel is realized.

A prism for reflecting a laser includes: a single mounting cap at a first end of the prism, and first to seventh trihedral corner (TC) reflectors, each including a reflective surface including: three side edges, and three corners at respective intercept points between the side edges, wherein the seventh TC reflector, among the first to seventh TC reflectors, is on a second end of the prism opposite to the first end of the prism.

The invention discloses a magnetic imaging identification method based on deep learning and a magnetometer, and the method comprises the steps: carrying out the preprocessing of collected magnetic field data, and obtaining encrypted and reconstructed magnetic image data; calculating magnetic gradient tensor components based on the encrypted and reconstructed magnetic image data, and extracting magnetic field gradient features by using different combination methods of the magnetic gradient tensor components; magnetic field abnormal information is recognized by analyzing magnetic field gradient features, a magnetic source boundary and encrypted and reconstructed magnetic image data are combined, pseudo-color coding is carried out, and a magnetic image data set is established; the magnetic image data set is used for training an improved R-CNN pipeline defect detection model, the input of the pipeline defect detection model is a magnetic image, and the output of the pipeline defect detection model is the position and type of the pipeline defect; and performing pipeline defect identification on a newly input magnetic image by using the trained pipeline defect detection model. Through an advanced data processing technology and an artificial intelligence algorithm, the precision, efficiency and adaptability of pipeline defect detection are improved.

The invention provides a method for measuring the conductivity and the inner diameter of a pipeline based on sweep frequency eddy current. The method comprises the following steps: establishing an eddy current detection device, designing an absolute eddy current detection coil, placing the coil in a metal pipeline, arranging the coil and the pipeline coaxially, introducing alternating current to excite an alternating magnetic field, and inducing eddy current on the surface and the near surface of the pipeline; establishing a metal pipeline eddy current analysis theoretical model, setting coil geometric parameters, and calculating an analysis expression of coil impedance changing along with excitation frequency; calculating the peak frequency and the corresponding phase value of the impedance phase of the coil in a frequency sweep mode, respectively changing the conductivity and the inner diameter of the pipeline, extracting the corresponding peak frequency and phase value, and constructing an inner diameter relation model based on quadratic curve fitting and a conductivity relation model based on power function fitting; and inverting the inner diameter and the conductivity of the metal pipeline by combining a coil impedance sweep frequency phase measurement result. The method can be used for simultaneously measuring the conductivity and the inner diameter of the pipeline, and is suitable for nondestructive testing and performance evaluation of the metal pipeline.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention provides a welding defect identification method, system and equipment for an automobile part and a medium, and relates to the technical field of automobile welding.The method comprises the steps that a structured light vision sensor and a high-frequency ultrasonic sensor are used for obtaining three-dimensional structured light data of a to-be-welded area of the automobile part and sound wave signals generated in the welding process; obtaining characteristic frequency information used for identifying welding internal defects; the method comprises the following steps: capturing a heat distribution image of a to-be-welded area of an automobile part through a thermal imaging camera to obtain area feature information used for identifying welding defect thermal anomaly, and obtaining magneto-optical signal feature information used for identifying welding defects; performing defect identification and classification on the fused feature representation by training a deep learning model to obtain a defect identification result; and welding parameters are adjusted through a simulated annealing algorithm, so that the welding defects of the automobile parts are recognized. According to the invention, various welding defects can be identified, so that the accuracy and efficiency of welding quality detection are remarkably improved.

The invention relates to the technical field of nondestructive testing, and particularly discloses an automatic detection system and method based on a titanium plate welding part, and the method comprises the steps: obtaining an original physical field signal of a to-be-detected part under the excitation of a single energy field, and extracting a space energy attenuation gradient and time phase lag distribution through wavelet packet decomposition and Hilbert transform; constructing a dynamic propagation model for describing a signal propagation path evolution rule, and performing space-time registration and vector difference calculation with a preset ideal reference model to generate a difference evolution graph; high-dimensional topological feature mapping, density clustering and multi-scale persistence analysis are carried out on the atlas, and a stable abnormal mode caused by defects is identified and confirmed; and backtracking a dynamic evolution path of an abnormal mode, extracting defect core parameters, and completing three-dimensional positioning, type classification and security level evaluation in combination with process information.

The invention provides a method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially improved.

The invention discloses a rotor surface magnetic field characteristic real-time monitoring and damage assessment system and method, and relates to the technical field of damage assessment. According to the method, a rotor surface magnetic field is monitored in real time under different rotating speed conditions, detailed magnetic field change data information is collected, the monitoring means can dynamically capture the magnetic field fluctuation condition, and the damage assessment accuracy is improved. According to the method, the damage prediction model is constructed by using the deep learning technology, the relevance between the damage prediction model and the rotating speed of the rotor is analyzed, early detection and classified evaluation of the unbalance condition are ensured, accurate data support is provided for further damage analysis, and accurate evaluation of the surface damage of the rotor is realized by using the deep learning technology to construct the damage prediction model and combining the unbalance type and the magnetic field change data. Meanwhile, the damage evaluation index is subjected to linear normalization processing, the influence between different data dimensions can be eliminated, and the evaluation result is more stable and reliable. And whether the damage state of the rotor surface is abnormal or not is judged in time by comparing and analyzing the damage evaluation index and a threshold value.

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 present invention relates to the field of defect detection technology, and in particular to an OLED screen defect detection method based on optical-magnetic multimodal fusion, comprising the following steps: S1: synchronous excitation and data acquisition based on pulsed current drive; S2: cross-modal spatial calibration and noise suppression; S3: polarization-magnetic field coupling feature extraction; S4: dynamic correlation decision-making and closed-loop feedback, including realizing defect classification determination through correlation analysis of multimodal data, and verifying the authenticity of defects through current overload testing. This method effectively solves the problems of ambient light interference, missed detection, high cost, low resolution, etc. existing in the prior art, has significant beneficial effects, can improve detection accuracy, efficiency and reliability, meet the needs of large-scale production lines, and can efficiently locate and reduce errors and redundant steps in the detection process.

The invention relates to the technical field of copper pipe defect detection, in particular to a copper pipe internal thread secondary machining defect detection method and system, and the method comprises the steps: obtaining thread surface feature data based on multi-mode optical parameter measurement, and the multi-mode optical parameters at least comprise a polarized light inhibition reflection parameter and a multi-band spectral reflection parameter; temperature gradient parameters in the machining process are synchronously obtained, and deformation error correction is conducted on thread surface feature data in combination with dynamic compensation; according to the corrected thread surface feature data, a multi-dimensional defect judgment model is pre-trained to execute hierarchical defect detection; and integrating the coupling relationship between the multi-mode optical parameters and the electromagnetic parameters to output a defect measurement result, wherein the coupling relationship realizes multi-physical quantity fusion judgment through a preset weight distribution matrix. According to the invention, the problems of optical detection signal distortion and high microcrack omission ratio caused by surface high specular reflection in copper pipe internal thread secondary processing defect detection are effectively solved.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention relates to the technical field of marine pipelines, in particular to a marine pipeline inner wall defect detection and evaluation method and system, an electronic device and a storage medium, and the marine pipeline inner wall defect detection and evaluation method comprises the following steps: collecting marine pipeline operation data and marine pipeline inner wall magnetic field data in real time; carrying out data preprocessing on the collected marine pipeline operation data to obtain accurate data; constructing and integrating a pipeline entity three-dimensional geometric model, a pipeline physical behavior model and a pipeline performance evaluation model to obtain a digital twin model, and outputting a pipeline performance evaluation result; constructing a defect identification model by adopting a convolutional neural network architecture, and outputting a pipeline inner wall defect evaluation result; and constructing a visual interface, and generating a pipeline inner wall defect coping scheme in combination with the pipeline entity three-dimensional geometric model, the pipeline performance evaluation result and the pipeline inner wall defect evaluation result. The marine pipeline inner wall defect detection and evaluation system can perform real-time monitoring and predictive maintenance on the state of the marine pipeline, and can simulate behaviors and performance of the pipeline under different working conditions, so that potential structural damage, corrosion risks and maintenance requirements are predicted, the maintenance basis is optimized, and the operation cost is reduced.

The invention provides an intelligent eddy current nondestructive testing method and system based on a flexible GMR sensor array, and relates to the technical field of nondestructive testing. According to the invention, a GMR sensor array and a micro excitation coil array are integrated on a flexible substrate, and synchronous acquisition under multi-frequency and multi-phase excitation is realized by establishing a probe-workpiece unified space coordinate system and a time synchronization reference; performing temperature drift compensation, attitude correction and gap normalization processing on the original response tensor to generate a corrected response tensor; the intelligent inversion model based on the prior constraint of the electromagnetic field jointly identifies the conductivity, the thickness and the crack parameters, and outputs a defect parameter map and a confidence map; and a detection report containing the position, the size, the depth and the confidence coefficient is generated through spatial clustering and connectivity analysis, so that high-sensitivity and high-reliability nondestructive detection under a complex curved surface structure is realized.

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 discloses a pulsed eddy current detection method and system for a thermal power pipeline with a coating layer, and relates to the technical field of pulsed eddy current nondestructive detection.The pulsed eddy current detection method comprises the steps that pulsed eddy current detection simulation and experiment are conducted under the condition of a three-dimensional simulation model and a test piece, and excitation current waveform, amplitude and frequency parameters are determined; an induced voltage signal is collected, peak values, time points and attenuation characteristics are extracted, the corresponding relation between the wall thickness, the coating layer thickness and the welding seam structure and detection signals is constructed, a parameterized database is constructed, and temperature correction is completed; and optimizing a sensor structure and a signal acquisition scheme according to a parameterized database, obtaining detection data in a pipeline operation state, completing two-dimensional imaging of wall thickness distribution and welding seam positioning, and forming detection information covering a straight pipe, an elbow and a welding seam area. According to the method, wall thickness detection and welding seam positioning of the thermal power pipeline under the conditions of a thick wall and a thick coating layer are achieved through the full-process design of test piece modeling, simulation experiment and imaging collection.

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

The invention relates to the technical field of defect detection, and discloses a Babbitt metal coating surfacing thin-wall layer defect detection method and device. The method comprises the following steps: carrying out multi-source detection data acquisition, multi-domain transformation and filtering enhancement on a babbitt metal coating, and obtaining preprocessed multi-source initial coating detection data to carry out interface-tissue-defect mapping, so as to obtain unified representation of an internal tissue structure of the coating; performing progressive fusion of a coating depth structure and extraction of coating defect morphology and structural features on the unified feature representation to obtain preliminary defect semantic features, and performing feature weighting calculation of defect morphology on the preliminary defect semantic features to obtain initial defect semantic features; and carrying out bearing strength and shear strength calculation and fatigue life prediction on the initial defect semantic features to obtain a final detection result. According to the method, the defects of the thin-wall layer coated and surfaced by the babbitt metal can be efficiently and accurately detected.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

The invention relates to the technical field of pipeline detection, and discloses a pipeline weld defect analysis method and device based on magnetic flux leakage detection.The method comprises the steps that a composite excitation signal is generated and optimized through a multi-frequency excitation source, a magnetic flux leakage attenuation coefficient and scattering characteristics are calculated in combination with pipeline parameters, and if the attenuation coefficient exceeds a preset attenuation threshold value, the pipeline weld defect is detected; if yes, adjusting the excitation signal for optimization, extracting high-frequency transient and low-frequency stable features of the optimized magnetic flux leakage signal, and generating a preliminary feature vector of the weld defect; mapping the preliminary feature vector to a three-dimensional defect space position, generating a space distribution diagram containing the defect position, optimizing the preliminary feature vector to make a fuzzy region clear, and obtaining the depth and direction of the defect; and according to the depth and direction of the defect, analyzing the spatial distribution characteristics of the preliminary feature vectors, determining the type of the defect, dynamically adjusting excitation signal parameters for optimization, recalculating the attenuation coefficient of the signal, extracting the optimized scattering features, and obtaining a high-precision weld defect positioning result.

The invention relates to the technical field of nondestructive testing, in particular to a gear defect intelligent detection method and system based on a magnetic memory technology. If the service gear is an abnormal service gear, a plurality of abrupt change magnetic flux signals are calculated by moving a sliding window so as to draw a magnetic flux line trend distribution diagram, the magnetic flux line trend distribution diagram is identified and analyzed, and an actual magnetic domain structure of each quasi magnetic flux leakage characteristic point in the magnetization process is obtained; and analyzing the actual magnetic domain structure to obtain a plurality of magnetic moment vector variations of each quasi-magnetic leakage flux feature point, analyzing defect features of the service gear based on the change amplitude of the magnetization intensity of the strong magnetic field for the magnetic moment vector of the normal service gear and the plurality of magnetic moment vector variations, and finally outputting an accurate defect type. According to the invention, the stress defect of the gear can be detected, positioned and identified based on the magnetic memory technology, so that a reliable fault detection basis is provided for the maintenance of the gear, and the accuracy and sustainability of the use and maintenance of the gear are improved.

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.