354 results about "Fatigue crack propagation" patented technology

The present disclosure relates to a multi-factor coupling cooperative early warning method and system for fatigue crack propagation of a steel structure. The multi-factor coupling cooperative early warning method includes: obtaining multi-physical field monitoring data of a dangerous source distribution point of a steel structure project, and obtaining a monitoring time series data set; establishing an intuitionistic fuzzy matrix of the monitoring time series data set; obtaining uncertainties of indexes by using a grey relation coefficient between the monitoring indexes of physical fields; taking obtained uncertainties as basic probability assignments of evidences; preprocessing the evidences through weighed averaging, and obtaining corrected basic probability assignments; obtaining basic probability assignments of fatigue crack propagation of the steel structure at different development stages; and determining a fatigue crack propagation grade of the dangerous source distribution point of the steel structure project through the basis probability assignment.

The invention provides a bridge structure fatigue crack propagation prediction method and system, and relates to the technical field of artificial intelligence. According to the method, structural parameter information of a bridge is collected, a three-dimensional model of the bridge structure is established through finite element analysis, and a fatigue crack propagation prediction model is established based on the fracture mechanics theory and a fatigue damage accumulation model; performing expansion simulation on preset cracks in the three-dimensional model of the bridge structure, calculating a crack expansion rate and predicting a crack expansion path, correcting a crack expansion simulation result in combination with an actual working environment and a load condition of the bridge structure, and calculating a crack expansion simulation result according to the corrected crack expansion simulation result. And predicting crack propagation information of the bridge structure in the preset time period. The method can accurately predict the expansion path, rate and future state of the crack under a complex working condition, thereby providing a scientific basis for maintenance and repair of the bridge, realizing real-time feedback and dynamic adjustment, ensuring high efficiency and accuracy of a prediction result, and providing a scientific basis for maintenance management of the bridge.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention relates to the technical field of rubber material data processing, in particular to a rubber material fatigue life prediction method based on data analysis. The method comprises the following steps: performing crack three-dimensional morphology reconstruction on a target rubber material to generate crack three-dimensional morphology data; performing material geometric model modeling on the target rubber material according to the crack three-dimensional shape data to generate an optimized rubber material geometric model; acquiring fatigue load condition data; carrying out cyclic load simulation by optimizing the rubber material geometric model based on the fatigue load condition data, and carrying out cyclic rubber fatigue characteristic analysis to obtain rubber crack fatigue characteristic data; and predicting the fatigue life of the rubber sample according to the rubber crack fatigue characteristic data to generate predicted fatigue life data. The fatigue life of the rubber material under the cyclic load is accurately predicted by analyzing the fatigue crack propagation of the rubber material.

The invention discloses a multi-scale fatigue crack propagation prediction method and system based on a physical information neural network, and aims to improve the prediction precision of a crack propagation rate. The method comprises the following steps: S1, acquiring crack lengths and cycle cycles of a metal material under different scales by using a microscope; s2, calculating an initial small crack propagation rate based on the acquired data, performing fitting by adopting a Newman-Wu model, constructing an initial crack propagation physical model, further combining experimental data to identify physical characteristics influencing propagation behaviors, introducing a PSC-LC turning point to divide small crack and long crack stages, and constructing a crack propagation rate model fusing multiple characteristics; s3, preprocessing the expansion rate data; and S4, constructing a physical loss function in combination with a crack propagation rule in an experiment, fusing the physical loss function with data loss to form a total loss function, and training the neural network model to realize accurate prediction of the multi-scale fatigue crack propagation behavior of the metal material.

The invention provides a rubber fatigue crack propagation test method, and relates to the technical field of rubber materials, the method comprises the following steps: obtaining a test piece of a to-be-tested rubber sample and a calendaring direction of the test piece; determining a test stretching direction based on the rolling direction; and performing crack tensile test on the test piece based on the test tensile direction to obtain a test result. The method comprises the following steps: determining a stretching direction which is the same as a calendering direction or a stretching direction which is different from the calendering direction according to test pieces made of different rubber materials, or cutting a plurality of test pieces with the same size on a rubber sample to be tested, and respectively stretching the test pieces with the same size in the calendering direction or stretching the test pieces with the same size in the calendering direction or stretching the test pieces with the same size in the calendering direction; according to the present invention, the multi-direction crack stretching test of the rubber material test piece is achieved, the stretching direction of the optimal stretching test can be selected, and compared with the prior art, the method can well illustrate the detected rubber material crack growth rate condition, and can improve the accuracy.

The invention discloses a polar equipment fatigue crack propagation reliability analysis method based on an improved SVM (Support Vector Machine), which comprises the following steps: acquiring uncertain parameters influencing the fatigue crack propagation of a polar equipment structure, and generating a candidate sample population by adopting a random sampling method according to the uncertain parameters of fatigue performance; generating an initial sample by adopting an orthogonal test method based on the fatigue performance uncertain parameters; evaluating the real cycle index response of the test sample; performing normalization processing on the obtained candidate sample, the test sample, the initial training sample and the cycle index response set; constructing an IBOA-LS-SVM model based on the initial sample, and obtaining a cycle index prediction value of the test sample and the candidate sample set; carrying out reverse normalization processing on the predicted value of the cycle index of the test sample point and the predicted value of the cycle index of the candidate sample point, carrying out precision test on the IBOA-LS-SVM model by adopting the true value and the predicted value of the cycle index of the test sample, and if the precision meets the requirement, calculating the failure probability of fatigue crack propagation at different polar region service environment temperatures by utilizing an MCS method; and if the precision does not meet the condition, carrying out orthogonal test design again.

The invention discloses a fatigue crack propagation analysis method of a rocket engine turbine blade. The method comprises the following steps: establishing a fatigue crack propagation model of the rocket engine turbine blade; determining high-low cycle fatigue load and input through the working condition load; the fatigue crack propagation calculation of the turbine blade is carried out in combination with ABAQUS and FRANC3D; fatigue fracture parameters such as stress intensity factors and J integral are output, and the critical crack size of the turbine blade is determined; and according to a fatigue crack propagation model, performing high-low cycle fatigue life prediction on the rocket engine turbine blade. Compared with existing fatigue crack propagation analysis, the method has the advantages that working condition loads in the whole cycle process of starting, stopping, stable operation and overload of the rocket engine turbine blade are considered, and calculation of the high-low cycle composite fatigue problem of the rocket engine turbine blade is achieved; and the fatigue crack propagation life and the critical crack length of the rocket engine turbine blade under the real working condition can be more accurately predicted.

The invention belongs to the technical field of computer-aided manufacturing, and relates to a method and system for predicting the propagation rate of fatigue cracks across welding zones in electric arc additive remanufacturing, and the method comprises the following steps: S1, manufacturing a sample; s2, performing in-situ SEM fatigue test on the sample; s3, obtaining a corresponding relation between the crack growth rate and the stress intensity factor range of the sample; s4, fitting to obtain a fatigue crack propagation rate model of the sample; s5, establishing a CZH sample fatigue crack growth rate prediction model; and S6, predicting the fatigue crack propagation rate of the sample across the welding zone. The system further comprises an electric hydraulic fatigue test system, a data acquisition module, a database, a fitting module and a prediction module. According to the method, by using the correction coefficient, the fatigue crack growth rate is predicted by using the fatigue crack growth rate parameter of the matrix material area.

The invention relates to the technical field of crack detection, and discloses an aluminum alloy component crack detection method and device.The method comprises the steps that an aluminum alloy component is subjected to CT scanning and preprocessing, and a preprocessed CT image is obtained; performing crack judgment through an integrated classification model of a bilinear convolutional neural network and a support vector machine classifier to obtain an image which is judged to have cracks; performing crack width transformation to generate a crack width graph; carrying out region growing algorithm search to obtain a complete crack region and a corresponding crack quantitative parameter set; performing vibration analysis on the aluminum alloy component to obtain vibration characteristic data; a stress intensity factor is calculated by using a continuous damage mechanical model, and fatigue crack propagation analysis is performed to obtain residual life prediction data, so that the accuracy of crack identification is improved, accurate extraction of crack features is realized, and reliable residual life prediction is performed on the basis.

The invention belongs to the technical field of metal part surface modification, and relates to a composite strengthening method for a carburized surface of a high-bearing gear of an aero-engine. According to the method, high-hardness and high-density ceramic shots are adopted as media for high-energy mechanical shot peening strengthening, the advantage that the subsurface strain hardening effect of the contact strengthening technology is good and the barb-shaped distribution characteristic of mechanical shot peening residual compressive stress are utilized, extremely high residual compressive stress is introduced to the subsurface position, and the contact fatigue performance of the tooth surface of the gear is improved. High-frequency laser shot peening strengthening is used for secondary treatment, so that the surface layer of the material is subjected to plastic deformation, high-amplitude residual compressive stress is introduced into the surface layer of the material within the depth range of more than 500 microns by utilizing the advantage of large influence depth of laser-induced shock waves, and the residual compressive stress can inhibit fatigue crack initiation, has a certain depth and inhibits fatigue crack propagation; and the bending fatigue resistance of the root of the gear can be effectively improved. And an abrasive flow process is adopted, so that the roughness is reduced to be more than 0.4 mu m.

The invention provides a pipeline steel fatigue crack growth rate prediction model construction method and system, and the method comprises the steps: constructing an initial crack growth rate prediction model considering the influence of a hydrogen pressure alternating load according to a Walker formula and a joint factor model; hydrogen pressure change is introduced, and the initial crack propagation rate prediction model is optimized into a hydrogen pressure adaptive crack propagation rate prediction model; hydrogen pressure is given, constant terms in the hydrogen pressure adaptive crack growth rate prediction model are solved, and a final fatigue crack growth rate prediction model is obtained. The cyclic load is designed according to the actual pressure fluctuation of the pipeline, the fatigue crack growth rate model of the pipeline steel under the combined action of the hydrogen pressure and the fatigue load is established, and guiding significance is provided for safe operation and service life prediction of the hydrogen-doped pipeline.

The invention discloses a wind driven generator structure high-cycle fatigue cross-platform simulation method based on multi-scale coupling modeling, and aims to accurately evaluate a fatigue damage state of a wind driven generator structure under the action of a high-cycle low-amplitude load. Firstly, a multi-scale model based on coupling of a fan overall structure finite element model unit and a local cracking area near-field dynamic model is established, and fan power time-history analysis is carried out based on a finite element analysis platform; then response data are transmitted to a processing platform, a rain flow counting method and a linear damage accumulation criterion are combined, the fatigue damage state of the connection key is determined, a fatigue life attenuation value is calculated, the fatigue cycle number needed by local crack propagation is determined, and the residual fatigue life of the unbroken key is updated; and finally, correcting the fan model, removing a newly added fracture key at the tip of the crack, and repeating dynamic time-history analysis and damage updating until the structure fails. According to the method, whole-process simulation and fatigue behavior analysis of fan fatigue crack propagation are realized, and important reference is provided for fan design optimization and maintenance.

The invention discloses a metal material fatigue life prediction method and device considering non-uniform grain size distribution, equipment, a medium and a product, and relates to the field of material fatigue life finite element numerical calculation. The method comprises the following steps: acquiring material parameters of a metal material; determining a crystal plastic constitutive model; determining a parameter group based on the crystal plasticity constitutive model; constructing a microstructure model to be tested; based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, applying a set loading condition and a set boundary condition, and determining material fatigue data; determining the fatigue crack initiation life according to the fatigue indication factor and the material fatigue data; according to the fatigue crack propagation curve, determining the fatigue crack propagation life by adopting a fitting mode; and determining the fatigue full life according to the fatigue crack initiation life and the fatigue crack propagation life. The method can improve the accuracy of determining the fatigue life of the metal material.

The invention discloses a method and device for predicting the fatigue life of a high-temperature alloy vacuum electron beam welding structure, and belongs to the technical field of aero-engines. The method comprises the following steps: acquiring fatigue experiment data of a target high-temperature alloy standard sample; extracting the maximum area of an initial defect, a defect shape factor and crack source position information; obtaining a stress intensity factor range considering defect morphological characteristics and a fatigue crack propagation threshold value of a material structure containing initial defects, and correcting a damage tolerance theoretical formula to obtain simulation data; fatigue test data and simulation data are fused to form an initial data set, a fatigue limit is calculated and expanded to the data set, the expanded data set serves as input, training is conducted through a support vector regression method, hyper-parameters are determined through Bayesian optimization, and a fatigue life prediction result of the high-temperature alloy vacuum electron beam welding structure is output. The method provided by the invention can well consider the influence of different morphology defects on the fatigue performance of the welded joint.

The invention relates to the technical field of structural engineering, and provides a surface crack fatigue propagation morphology and life prediction method based on machine learning, which comprises the following steps: establishing a finite element simulation model containing a surface crack, calculating a stress intensity factor of the front edge of the surface crack, and generating a finite element simulation data set; performing dimensionless processing on parameters in the simulation data set to generate a machine learning data set; optimizing the back propagation neural network through a hybrid optimization algorithm, and constructing a surface crack stress intensity factor prediction model; the surface crack stress intensity factor prediction model is combined with a fatigue crack growth rate formula to cyclically predict the surface crack growth morphology and the service life of the load bearing structure; establishing a visual interaction interface of parameter input and output and image output based on the surface crack stress intensity factor prediction model and the fatigue crack growth rate formula; a user can quickly predict the propagation morphology of the surface crack and calculate the service life without mathematical analysis and code writing.

The invention discloses an active learning and physical loss-based fatigue crack growth rate prediction method, which comprises the following steps of: splicing and fusing a preprocessed stress intensity factor, a stress ratio, pre-strain and stress amplitude, and taking the fused characteristics as the input of a model; embedding the Jones model into a loss function of the neural network, constructing a physical information time sequence model based on the Jones model, setting a parameter set, selecting a most valuable training sample from a training set by using active learning, retraining the model by using the selected most valuable training sample, selecting an optimal model, and evaluating the optimal model by using a test set; fusing the actually measured stress intensity factor, stress ratio, pre-strain and stress amplitude of the to-be-predicted material, and inputting into the obtained prediction model to obtain a predicted value of the fatigue crack growth rate. By adopting the technical scheme of the invention, the fatigue crack growth rate of the material in the whole life period can be efficiently and accurately predicted at low cost.

The invention relates to a method for simulating fatigue small crack propagation of a polycrystalline metal material. The method comprises the following steps: determining a crystal plastic constitutive model and accurate values of all undetermined parameters of the crystal plastic constitutive model; establishing a crack propagation finite element model of the polycrystalline metal material, and embedding the crystal plasticity constitutive model of which each undetermined parameter has a respective accurate value and a pre-obtained crack propagation criterion into the crack propagation finite element model; applying load and boundary conditions to the crack propagation finite element model, and performing crack propagation finite element analysis to obtain a finite element analysis result, including stress-strain data calculated based on the crystal plasticity constitutive model and crack propagation data calculated based on a crack propagation criterion and the stress-strain data. According to the method for simulating the fatigue small crack growth of the polycrystalline metal material, the Hall epicy size effect is introduced into the crystal plasticity constitutive model to consider the influence of the grain size on the sliding resistance, so that the simulation accuracy is improved.

The invention discloses an aero-engine part fatigue crack propagation identification method based on machine vision. The method comprises the following steps: S1, obtaining an image block sequence; s2, inputting each image block into a pseudo stress estimation network to generate an image block pseudo stress diagram; s3, inputting the image block and the image block pseudo-stress diagram into an image feature decoupling coding module, and outputting a fusion feature vector; s4, inputting the fusion feature vector into an improved fox algorithm, and generating a crack path guide map and a direction vector field; s5, inputting the image block pseudo-stress diagram, the crack path guide diagram, the direction vector field and the fusion feature vector into a crack mask generation module, and outputting an image block crack mask; s6, splicing the image block crack masks according to the positions of the image blocks in the surface image to generate a complete crack structure diagram; and S7, outputting a crack propagation result based on the complete crack structure chart. According to the method, machine vision and an evolutionary path optimization method are fused, and accurate recognition of aero-engine crack propagation is achieved.

The invention discloses a steel bridge deck fatigue crack propagation evaluation method and device, a storage medium and a product. The method comprises the steps that equivalent stress range prediction models of different vehicle types are constructed according to a probability model of actually measured traffic loads; according to the actually measured traffic load probability model and the equivalent stress range prediction model, constructing a steel bridge deck weld fatigue stress spectrum probability model; constructing an equivalent stress strength factor range prediction model of the crack tip and the crack edge; according to the initial crack depth, the form ratio, the probability model of the material related parameters, the stress spectrum probability model and the equivalent stress strength factor range prediction model of the crack tip and the crack edge, performing crack random propagation analysis; and probability evolution analysis of different sensitivity parameters and reliability evaluation of the steel bridge deck are carried out according to the distribution characteristics of the sensitivity parameters in the crack propagation process. According to the invention, the evaluation accuracy is improved, the evaluation time consumption is reduced, and the evaluation efficiency is improved.

The invention belongs to the technical field of aircraft strength experiments, and particularly relates to a hybrid-driven metal multi-fatigue crack propagation fusion prediction method. The method comprises the following steps: establishing a stress intensity factor expression under multiple fatigue cracks; initial distribution of material parameters in the Paris physical model is obtained, and posterior distribution of the material parameters is adjusted; based on the stress intensity factor expression under the multi-fatigue crack and the posterior distribution of the material parameters, obtaining an improved Paris physical model for describing the multi-fatigue crack propagation process; a depth encoder network model is established, and a network bottleneck layer is utilized to extract guided wave deep features with strong correlation with the fatigue crack length; establishing a guided wave data driven multi-fatigue crack length online monitoring model; and building a particle filter fusion framework, and correcting and improving a distribution interval of uncertain material parameters in the Paris physical model according to guided wave monitoring data to realize dynamic tracking and prediction of metal multi-fatigue crack propagation.

The invention discloses a material fatigue crack propagation prediction method based on a cyclic cohesion model of a random vibration load. The method comprises the following steps: building a crack propagation analysis finite element model based on the cyclic cohesion model; based on the established crack propagation analysis finite element model, calculating the strain of the whole non-cohesion region unit and the separation displacement of the cohesion unit under the current time increment step; according to the separation displacement and the frequency and the amplitude of the current vibration load, the traction force increment in the current time increment step is calculated, and the cohesion strength, the critical separation displacement and the accumulated separation displacement in the traction separation criterion after the fatigue damage increment is loaded are updated; and unit failure judgment is carried out according to the fatigue damage, and if the fatigue damage is reduced to 0 and the cohesive unit rigidity value is reduced to 0, it is judged that macroscopic expansion of fatigue cracks occurs. According to the method, reasonable prediction of fatigue crack growth under the random vibration condition is realized, and the method has wide adaptability.

The invention discloses a method and device for testing the fatigue crack growth rate of a metal material based on temperature gradient change, and relates to the technical field of metal material detection. Mounting a constant low-temperature component and a constant high-temperature component close to two ends of the sample respectively; pasting a plurality of thermocouples on the middle surface of the sample; a magnetic powder dynamic moving assembly is installed on the surface of the sample, and a constant-temperature state is given to magnetic powder; loading a bidirectional tension-compression fatigue testing machine along the through hole of the sample; a thermal imager is arranged far away from the parallel sample, and a thermal imaging image is shot; according to the data, the fatigue crack growth rate of the sample at the corresponding temperature is obtained; according to the method, the fatigue crack growth rate of the metal material can be rapidly tested, meanwhile, the temperature gradient is formed in the sample, the influence of the temperature on the crack growth rate can be obtained in different temperature areas, the device required by the whole test is simple in structure, low in cost and convenient to load and disassemble, and the whole efficiency of the test is effectively improved.

The invention discloses a PSO-SVR-based FSW joint fatigue crack propagation life prediction method and electronic equipment, and the method comprises the steps: simulating a crack propagation process of an FSW joint through a finite element simulation technology, obtaining a crack tip stress intensity factor, a corresponding loading load size and a crack length of the FSW joint in the process as a group of data, and constructing a data set; performing standardization processing on the constructed data set, and then dividing the data set into a training set and a test set; and training a machine learning model by using the training set, evaluating the trained model by using the test set, and performing hyper-parameter optimization on the model until an optimal crack tip stress intensity factor prediction model is obtained. According to the PSO-SVR-based FSW joint fatigue crack propagation life prediction method and the electronic equipment provided by the embodiment of the invention, finite element simulation data and test data are fused, so that the time cost and expense for constructing the fatigue crack propagation life model are effectively reduced, and the method and the electronic equipment have the advantages of high calculation efficiency, high prediction precision and the like.

The invention relates to an engine stator part small target crack length measurement method based on a deep convolutional neural network, and the method comprises the steps: carrying out the labeling of an initial fatigue crack image obtained through carrying out a fatigue crack propagation experiment, converting the initial fatigue crack image into a PNG label, and carrying out the expansion of the original image and the label through a data intensifier, constructing a VOC (volatile organic compound) format small target crack data set; with the help of the strong feature extraction capability and fine distinguishing capability of a deep convolutional neural network, a semantic segmentation network for small target crack segmentation is constructed based on a full convolutional network, a U-shaped architecture and jump connection, and training is performed to obtain a trained model weight for high-quality crack segmentation; and based on the segmented image, extracting a crack skeleton with a pixel width by adopting preprocessing means such as expansion and a refinement algorithm, counting the number of pixel points in different arrangement modes based on the crack skeleton, calculating to obtain a crack pixel length, and performing scale conversion to obtain an actual crack length. According to the invention, high-precision measurement of the crack length of a large batch of small targets is realized.

The invention relates to the field of test methods, in particular to a method for estimating the fatigue crack growth rate of a corrosion-resistant metal composite material, which comprises the following specific steps: S1, preparing a sample, and measuring and recording the thickness B, the width W and the machining notch length am of the sample; s2, prefabricating fatigue cracks; s3, selecting test parameters; s4, carrying out a fatigue crack growth rate test; s5, calculating the opening displacement variation (delta delta) of the crack tip; s6, forming a data group, analyzing a distribution rule and establishing a mathematical model; and S7, carrying out damage tolerance design and fatigue life check. According to the method, the crack tip opening displacement variation delta delta is used as a driving parameter to describe the fatigue crack propagation rule of the corrosion-resistant low-strength high-plasticity metal material and the composite material of the high-strength low-plasticity metal material; the method can solve the crack propagation problem that K is only suitable for elastic or small-range yield and r cannot be fully covered under the condition of plastic yield.

The invention belongs to the technical field of industrial detection, and provides a low-temperature fatigue crack propagation life prediction method and system based on a steel bridge deck, and the method comprises the steps: judging whether the crack direction deflection risk assessment needs to be started or not through the sharp reduction of steel plasticity and the non-uniform analysis of local stress gradient; if yes, the crack direction deflection risk of the steel bridge deck is evaluated by analyzing the grain orientation difference and the strengthening phase of the welding seam and the HAZ area; if the risk is high, a stress gradient gentle area is determined through a weld toe stress gradient cloud picture, a resistance-gradient coupling equation is established according to the minimum resistance principle and in combination with the stress gradient, and a crack deflection path is determined; dividing the crack crossing area according to the crack deflection path, calculating the actual propagation rate of the crack by combining the path proportion and stress gradient distribution of each stress gradient area after division, and correcting the life prediction output result, thereby improving the crack propagation path prediction precision and the fatigue life prediction evaluation precision.

The invention relates to the technical field of fatigue time-varying reliability analysis, in particular to a fatigue time-varying reliability calculation method and device, equipment, a storage medium and a product. The method comprises the following steps: performing crack propagation simulation on a three-dimensional plate shell structure based on a random propagation finite element method, and establishing a geometric model of a crack propagation path; based on the geometric model, calculating a stress intensity factor of the crack tip by adopting an interaction integral method, and determining change data of the stress intensity factor in the crack propagation process; based on the stress intensity factor of the crack tip and the change data of the stress intensity factor in the crack propagation process, performing numerical simulation on the crack propagation process by adopting an propagation finite element method; and based on a numerical simulation result, optimizing the crack propagation process by adopting an HLRF-BFGS optimization algorithm, and determining the reliability of fatigue crack propagation. According to the method, the fatigue reliability evaluation effect is improved.

The invention provides a crack-containing pipeline failure analysis software system, and belongs to the field of crack-containing pipeline failure evaluation. The system comprises a parameter import module, a random sampling module, a penetration crack length module, a leakage rate module, a failure analysis module and a display module. The parameter import module imports pipeline material parameters, operation environment parameters and the like into the computer; the random sampling module is used for carrying out random sampling according to a common distribution function on the basis of importing parameters; the fatigue crack propagation module is used for calculating a stress intensity factor and fatigue crack propagation; the leakage rate module is used for calculating a detectable leakage length; and the result analysis module is used for integrating all calculation results and input parameters into a failure probability analysis chart. According to the method, the probability fracture mechanics method is adopted, the detection accuracy is improved, and the failure probability of equipment can be evaluated more quickly and visually.

The invention relates to the technical field of material structure performance detection, and discloses a crack propagation state detection method, device and equipment, a medium and a product, and aims to improve the purity of a signal and eliminate the interference of other damage signals by carrying out noise reduction and blind separation treatment on the acoustic emission signal. Furthermore, through feature classification processing, a voiceprint signal feature parameter set which can most accurately reflect the crack propagation state can be found from numerous features, and the effectiveness and pertinence of the features are improved. Furthermore, the change relationship between the voiceprint signal characteristic parameters and the crack propagation rate is established by using a fatigue crack propagation voiceprint signal test method and a Paris's formula, and the relationship between the voiceprint signal characteristic parameters and the crack propagation state can be quantified from the perspective of combining theory and experiment. Finally, in combination with the voiceprint signal feature parameter set and the change relation, the crack propagation state can be accurately recognized in the high-noise environment, and the accuracy and reliability of the detection result are improved.