2760results about "Material analysis using acoustic emission techniques" patented technology

The invention relates to the technical field of anti-cracking performance testing, in particular to an ultra-high performance concrete anti-cracking performance testing system and a dynamic monitoring method thereof.The system comprises a dynamic stress field simulation feedback module used for sensing three-dimensional distribution of a micro stress field in concrete in real time by constructing a flexible loading array to obtain dynamic stress field data; the multi-scale damage evolution tracking module is used for deploying a high-resolution acoustic emission network and a distributed optical fiber sensing layer based on dynamic stress field data of the flexible loading array to form a full-scale evolution graph from microdefects to macroscopic cracks; and the intelligent healing efficiency evaluation module obtains stress redistribution data and a full-scale evolution graph, and a repairing medium containing a tracer agent is injected into a preset crack path. According to the method, the whole process of crack resistance of the material from defect initiation to repair and regeneration can be quantitatively evaluated; and finally, outputting a dynamic evolution rule of the crack resistance and enhancing potential evaluation.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention provides a new energy battery internal microcrack detection method, which comprises the following steps: acquiring a battery internal three-dimensional structure image by adopting a high-resolution X-ray tomography technology, extracting a pole piece area through an image segmentation algorithm, and carrying out bending deformation simulation on a pole piece to obtain a pole piece internal stress distribution cloud picture; and combining the retrained health state evaluation and residual service life prediction model with battery electrochemical performance characterization data, coupling microcrack evolution and battery performance degradation, predicting a future crack propagation trend and a corresponding performance degradation condition of the battery, and guiding quality control in a battery design and production process.

The invention provides an engine internal damage identification method and system based on target detection, and the method comprises the steps: obtaining the distribution of thermal stress and mechanical stress under different working conditions through finite element analysis for typical working conditions of high temperature, high pressure and heat engine coupling load according to the structural characteristics and fault modes of an engine cylinder; processing an ultrasonic guided wave signal through a synthetic aperture focusing algorithm, reconstructing a three-dimensional shape of a crack by combining a three-dimensional geometric model of the cylinder body and guided wave propagation characteristics, and determining a crack propagation rate so as to analyze crack propagation of the cylinder body; the residual service life of the cylinder body under the given working condition is predicted according to the crack size, the crack growth rate and the high-risk working condition parameters in combination with the material mechanical property parameters of the fracture toughness; and according to the crack instability critical length, the cylinder body crack propagation analysis result and the high-risk working condition judgment result, the engine operation working condition is optimized, the occurrence frequency of the high-risk working condition is reduced, the service life of the cylinder body is prolonged, and the crack detection and recognition precision is improved.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The present invention discloses a defect detection method, device and equipment for a high-altitude fall prevention system, wherein the method includes: regionalizing the high-voltage electric control components and multi-layer composite insulation structures in the high-altitude fall prevention equipment; determining the high-voltage pulse parameters of each functional area and applying pulses according to environmental parameters and electromagnetic interference data; applying progressively increasing mechanical tension to the suspected stratified area; applying multiple thermal stress excitations to the target area; performing high-resolution acoustic imaging scanning to generate defect location information. The technical solution of the present invention can effectively cope with complex electromagnetic interference and extreme climatic conditions in high-altitude environments, and through a multi-dimensional and multi-stage detection process, accurately identify and locate microscopic stratified defects in high-altitude fall prevention systems, providing important guarantees for the safety of high-altitude operations.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

Methods and apparatus are disclosed for assessment of microstructural defects in a large structure. Third order elastic constants engender nonlinear excitation of acoustic modes particularly at points of local stiffness changes or stress concentration such as ends of microcracks. A broadband multimode acoustic signal is transmitted through an interrogation region from a first transducer, whereby a second transducer receives an acoustic signal containing linear and nonlinear components. Linear terms are canceled, and a magnitude of the nonlinear acoustic response is measured using time-resolved spectral analysis, to determine a coefficient for acoustic nonlinearity of the interrogation region that serves as a qualitative or quantitative representation of microcrack damage. Broadband signals avoid mode-specific effects such as shadowing or phase mismatch. Damage location can be identified by region or time-of-flight. Portable or dedicated embodiments can be deployed on a wide range of structures of arbitrary shape.

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 present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.

The invention discloses a karst region water and soil loss monitoring method and system. According to the monitoring method, a water and soil loss risk prediction result of a target area is obtained based on a deployed acoustic sensor array, the acoustic sensor array is deployed at a key monitoring point of the target area to be monitored, and acoustic wave signals of the earth surface and underground environment are collected at the same time; acoustic wave signals detected by the acoustic sensor array are collected, and the acoustic wave signals are converted into frequency domain characteristic data; inputting the frequency domain feature data into a pre-trained acoustic mode recognition model, and recognizing acoustic features related to water and soil loss; based on an output result of the acoustic mode recognition model, judging the water and soil loss type, degree and development trend of the monitored area, and generating a water and soil loss risk prediction result; according to the invention, real-time and continuous monitoring of the water and soil loss process in the karst region is realized through an acoustic monitoring means, the monitoring precision and early warning timeliness are improved, and a scientific basis is provided for water and soil conservation management.

The invention relates to the field of bridge monitoring, and particularly discloses a crack monitoring system for a fixed end of a steel-concrete composite beam. The method is used for solving the problems that multi-source information fusion, hot spot stress on-line accurate calibration, fatigue life real-time accurate evaluation, S-N curve dynamic correction and remote visual early warning are difficult to realize in crack monitoring of a fixed end of an existing steel-concrete composite beam under complex load and environmental interference. Comprising a multi-sensor data acquisition module, an edge preprocessing module, a data fusion and damage identification module, a hot spot stress calibration module, an early warning decision module, a visual remote communication module and a digital twinborn calibration module. According to the invention, through integration of multi-sensor synchronous acquisition, edge intelligent preprocessing, multi-source data fusion, online hot spot stress calibration and digital twinborn calibration, Monte life prediction and early warning and hybrid energy management, high sensitivity, low power consumption and accurate early warning of steel-concrete composite beam fixed end crack monitoring are realized.

The invention discloses an aerial optical cable defect detection method based on multi-mode sensing fusion and related equipment, and relates to the field of data processing. The method comprises the following steps: acquiring multi-modal sensing data along an aerial optical cable through sensing equipment of different modals; performing time and space alignment processing on the distributed fiber bragg grating strain data, the acoustic emission signal data and the visual image data to obtain multi-mode time and space alignment data; extracting feature data of each mode based on the multi-mode space-time alignment data; performing fusion processing on the feature data of each modal through an attention mechanism to obtain fused feature data; determining a defect identification result of the aerial optical cable according to the fusion feature data, wherein the defect identification result comprises a defect type and a position of the defect on the optical cable; obtaining the result confidence of the defect recognition result; and generating a final optical cable defect detection result according to the result confidence. According to the invention, the problem that the current aerial optical cable defect detection has relatively large missed detection and false detection risks can be relieved.

The invention provides a pressure pipeline corrosion on-line monitoring method and system, and relates to the technical field of pipeline monitoring, and the method comprises the steps: obtaining three-dimensional point cloud data, obtaining reduced sampling point cloud data through an octree filtering algorithm, constructing a geometric feature scattering matrix, inputting a multi-scale fusion twin network, and learning feature differences through a contrast loss function. Generating a corrosion probability distribution diagram; on the basis of an iterative nearest point algorithm, registering the corrosion probability distribution diagram, extracting a corrosion overrun area, performing overlay analysis on the corrosion overrun area and the pipeline stress concentration area, and determining a high-risk corrosion area; the method comprises the following steps: arranging a sensor array around a high-risk corrosion area, acquiring sensor corrosion signals, performing variational mode decomposition, extracting intrinsic function components related to corrosion characteristics, performing sparse representation on the intrinsic function components, constructing a dispersion compensation matrix, and determining a corrected sparse characteristic matrix; and inputting into a multi-core extreme learning machine discriminator to judge whether leakage occurs in the high-risk corrosion area.

The invention relates to a hydraulic concrete structure state monitoring method and system based on acoustic emission and deep learning. The method comprises the following steps: collecting acoustic emission signal data generated by a hydraulic concrete structure; performing signal preprocessing on the acoustic emission signal data, and extracting key feature vectors; clustering operation is carried out on the key feature vectors, labels of various types of acoustic emission signal data are marked according to clustering results, the labels comprise data types and corresponding relations between feature parameters and structure states, and the number of clusters is determined through a voting and scoring mechanism; constructing a classification model, and training the classification model by using the data marked with the labels; and monitoring the state grade of the hydraulic concrete structure at the current moment or period in real time by using the trained classification model. According to the method, the physical process of the acoustic emission signal is deeply excavated by combining multiple features and matching with the clustering algorithm, and the hydraulic concrete structure state is accurately captured from early microcrack damage to real-time dynamic tracking evaluation by using the deep learning model.

The invention provides a multi-dimensional coupling water quality evaluation method and system for drainage of a sewage treatment plant, and the method comprises the steps: obtaining water response data of a drainage port of the sewage treatment plant in a flow velocity fluctuation environment, so as to generate an acoustic feature vector related to a treatment process of the sewage treatment plant; carrying out association processing on the acoustic feature vector, the chemical parameter variation and the temperature gradient variation to generate a multi-dimensional coupling data set so as to establish a mapping relation between sound wave spectrum features and water quality discharge standard grades, and identifying association between a multi-parameter variation mode and the processing process so as to generate an analysis result; according to the mapping relation and the analysis result, an evaluation control parameter set is generated, the multi-dimensional coupling data set is dynamically corrected, and a water quality evaluation result associated with the treatment process is generated. And the monitoring reliability and the evaluation accuracy under complex working conditions are improved.

The invention relates to a rock shear failure whole process prediction method based on acoustic emission monitoring, which comprises the following steps: firstly, collecting a rock sample, processing the rock sample into a plurality of rock test pieces, manufacturing an acoustic emission mold, secondly, installing an acoustic emission test platform, carrying out multiple acoustic emission tests of the rock shear failure whole process by using the acoustic emission test platform, and recording data of the multiple acoustic emission tests, the method comprises the following steps: selecting and calculating calculation parameters of a rock shear failure whole-process prediction model based on experimental data, finally constructing the rock shear failure whole-process prediction model based on an XGBoost algorithm, inputting the calculation parameters of the rock shear failure whole-process prediction method into the model, and outputting a rock shear failure prediction result. According to the method, the stability of the rock mass can be continuously monitored in real time, different stages in the direct shear process of the rock are predicted in a classified mode, and the safety and reliability of rock mass engineering such as tunnel engineering can be improved.

The invention discloses a slope stability evaluation method based on micro-seismic signal double-dominant-frequency mechanism characteristics, and relates to material analysis and application. The method is based on the objective principle that rock acoustic emission dominant frequency characteristics and micro-seismic signal dominant frequency characteristics have consistency and relevance, and the phenomenon of double dominant frequency characteristics existing in acoustic emission tests and micro-seismic monitoring. A rock sample dominant frequency characteristic rule obtained by an acoustic emission test is used for processing micro-seismic signal waveform data obtained by micro-seismic monitoring, so that a dominant frequency characteristic evolution rule of slope rock damage can be obtained through a micro-seismic signal, and the stress condition of the slope rock is more accurately judged based on the dominant frequency characteristic evolution rule; the stability of the slope is evaluated, so that the accuracy of an evaluation result is remarkably improved; the evaluation method combines an acoustic emission test and micro-seismic monitoring, has the advantages of high evaluation accuracy and good universality, and is suitable for large-scale application in monitoring and prevention of geological disasters such as landslide and the like.

The embodiment of the invention provides a real-time defect detection method and device based on carbon fiber winding and a medium, and relates to the technical field of carbon fiber laying layer detection.The method comprises the steps that real-time environment data of carbon fiber winding are obtained, and multi-mode cooperative detection is conducted on the real-time environment data of carbon fiber winding, obtaining a carbon fiber real-time defect data set; performing multi-mode space-time synchronous calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronous data; performing strategy-level fusion on the synchronous data of the carbon fiber defects to obtain winding state preliminary judgment data; based on the winding state preliminary judgment data, multi-modal weight update data are determined through carbon fiber winding process stage analysis; and according to the modal weight updating data, determining the real-time winding defect of the carbon fiber through carbon fiber defect type analysis. According to the method, the technical problem of poor anti-interference capability in a single parameter analysis process in carbon fiber winding detection is solved.

The invention relates to the technical field of microcosmic damage mechanism recognition of refractory materials, and discloses a refractory material damage mode recognition method based on an acoustic emission technology, which comprises the following steps of: acquiring acoustic emission signals of a refractory material under the action of static load through a three-point bending experiment, determining the number and characteristic parameters of data set categories of the acoustic emission signals, and determining the number of the data set categories of the acoustic emission signals; and performing clustering analysis on the acoustic emission signals by adopting a K-means clustering algorithm, dividing the acoustic emission signals into three classes, analyzing by combining an acoustic emission parameter association diagram, determining acoustic emission characteristics corresponding to different damage mechanisms, and determining a damage mode corresponding to each class of signals. According to the method for identifying the damage mode of the refractory material based on the acoustic emission technology, a method combining clustering analysis and acoustic emission parameter association diagram analysis is adopted, accurate judgment on the microscopic damage process of the refractory material can be effectively achieved, and theoretical support is provided for further research on mechanical behaviors and the microscopic damage mechanism of the refractory material.

The invention belongs to the technical field of grouting compactness detection, and particularly discloses and provides an intelligent detection system and method for the grouting compactness of a bridge prestressed pipeline, and the system comprises a signal acquisition and analysis module, a defect boundary calibration module, a defect recognition and analysis module and a compactness judgment and processing module. The method specifically comprises the steps that acoustic emission and fluctuation response signals are collected in real time through a sensor array arranged on the outer wall of a pipeline, and three-dimensional slurry density distribution is recognized; constructing an inner wall three-dimensional model in combination with a pipeline design drawing, dynamically setting a slurry density threshold value according to a grouting material formula and an environment temperature, and further calibrating a slurry defect boundary; extracting multi-dimensional features such as spatial positions, effective volumes, geometric regularity and types of the defects on the basis of boundaries, and calculating grouting compactness; and finally, according to whether the compactness reaches the standard or not, a grout supplementing early warning instruction or a detection report is automatically generated. According to the method, the reliability of defect identification and compactness calculation under complex working conditions is remarkably improved.

The invention relates to the technical field of new energy aluminum alloy precision casting multi-modal quality monitoring, in particular to a new energy aluminum alloy precision casting multi-modal quality monitoring device and method. According to the technical scheme, the new energy aluminum alloy precision casting multi-mode quality monitoring device comprises a quality monitoring device body, an infrared thermal imager probe, a germanium single crystal protection lens, an integrated spectrometer probe debugging port, an acoustic detection microphone array and a honeycomb acoustic shielding cover. An infrared thermal imager probe is arranged on one side of the quality monitoring device main body, and a germanium single crystal protection lens is arranged on the outer side of the infrared thermal imager probe; synchronous acquisition and fusion analysis of multi-physical field data are realized by integrating six modes of spectrum, infrared thermal imaging, vibration, machine vision, acoustics and pressure deformation, and meanwhile, when the shrinkage risk is predicted by the infrared thermal imaging, a die-casting process compensation mechanism is automatically triggered to kill defects in the germination stage, so that the defect rate is greatly reduced.

The invention discloses a wind power blade defect detection method based on acoustic emission, and relates to the technical field of wind power blade detection.The wind power blade defect detection method comprises the steps that multiple lasers, a multispectral camera, a thermal imaging sensor and a plurality of acoustic emission sensors are carried on an unmanned aerial vehicle, and multiple multispectral images and thermal imaging images within fixed time are obtained; a multispectral image total discrimination sequence and a thermal imaging image total discrimination sequence are obtained through processing, suspicious pixel points in the two sequences are mapped into actual three-dimensional coordinates of the wind power blade, a light reflection defect area and a thermal distribution defect area are obtained through fitting, the intersection of the two areas is obtained, and a specific area of the wind power blade defect is obtained. And positioning the region by using an acoustic emission sensor to obtain a to-be-detected acoustic signal, denoising by combining LMD with MFSE, extracting a feature vector, and substituting the feature vector into a GRU wind power blade defect judgment model to judge whether an internal fault exists or not. According to the invention, comprehensive perception and correlation judgment of internal and external defects of the wind power blade are realized.

The invention discloses a squeeze casting forming quality detection device and system, and belongs to the field of quality detection.According to the shape and size of a squeeze casting, ultrasonic emission signal parameters are adjusted, and self-adaptive preprocessing is conducted on received ultrasonic echo signals; according to the method, data fusion and feature extraction are carried out on ultrasonic echo signals received by multiple channels, abnormal grid units are screened, the abnormal types of the abnormal grid units are identified through a deep learning model, then abnormal areas are divided, and the molding quality score of the extrusion casting part is obtained through comprehensive evaluation on the abnormal areas. Carrying out qualification judgment on the extrusion casting part; detection data is stored, managed and maintained, the detection data is classified, filed and indexed by constructing a product database, a squeeze casting quality tracing path is generated, and data backup and data recovery are regularly performed on the product database; the objective of the invention is to solve the problems of accurate and efficient quality detection and effective data management of extrusion castings.

A fracturing effect determination method for phase transition fracturing deep rock, comprising: obtaining initial data of a true triaxial rock of a fracture hole; fracturing true triaxial rock and obtaining acoustic emission information in the fracturing process; obtaining test data of the true triaxial rock after fracturing; inputting initial data and test data into a preset formula to generate test data; determining quantitative data of fracturing according to acoustic emission information and test data and determining the fracturing effect of true triaxial rock according to the quantitative data of fracturing. The method is implemented using a device comprising a carbon dioxide fracturing device, a true triaxial loading device, a fracturing starter, a storage tank, and a liquid filling device. The device is configured to achieve the quantitative optimization effect of the technical parameters required for the prevention and control measures of energy release in deep engineering rock mass