640 results about "Life evaluation" patented technology

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

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 discloses a lithium iron phosphate battery pack SOE dynamic prediction and life evaluation method and system fused with a P2D model, and relates to the technical field of battery management, and the method comprises the steps: collecting operation data, building a multi-physical field coupled P2D model, carrying out the SOE dynamic prediction based on the P2D model, building a life evaluation model in combination with a performance attenuation mechanism, and optimizing an operation strategy. According to the method, on the basis of historical operation data and real-time state parameters, an SOE dynamic prediction result of the battery pack is generated by using an electrochemical-thermal-mechanical multi-physics field coupled P2D model; and analyzing the prediction result in combination with a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result. The SOE prediction precision is improved, the performance change rule is comprehensively reflected by using the life evaluation model, and the operation efficiency is remarkably improved and the service life is prolonged through real-time monitoring and optimization. According to the invention, efficient and reliable battery management requirements under complex working conditions can be met.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to a stamping die service life dynamic evaluation system and method based on multi-mode sensing. According to the system, a feature extraction module obtains stress distribution data and crack position information and inputs the data into a trained three-dimensional stress field model to obtain dynamic feature parameters of a stress concentration area under high-frequency cyclic loads; the crack prediction module judges the dynamic characteristic parameters based on a preset threshold value, and once the dynamic characteristic parameters exceed the preset threshold value, analysis is carried out according to the mapping relation between the stress level and the crack rate and the microstructure evolution rule at the same time, and a crack propagation prediction value is generated. And the service life evaluation module inputs the crack propagation prediction value into the trained health state evaluation model, and finally the service life evaluation value of the stamping die is obtained. The service life evaluation value of the stamping die under the existing working condition and the operation condition is accurately estimated, the production efficiency is effectively improved, the production cost is reduced, the potential crack risk of the die is perceived in advance, and accurate and dynamic die service life evaluation is achieved.

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention discloses a lithium ion battery health life assessment method and system based on a feature image, and the method comprises the steps: pre-training a battery health life assessment model through the sample data of a source domain, and building a mapping relation between the feature image and the health life of a lithium ion battery, and the feature image is generated based on an incremental capacity feature; introducing transfer learning and utilizing sample data of a target domain to optimize a battery health life evaluation model; and extracting incremental capacity features for the to-be-evaluated lithium ion battery in the target domain, generating a feature image of the lithium ion battery, and inputting the feature image into the optimized battery health life evaluation model to obtain the corresponding health life. According to the method, the lithium ion battery health life evaluation model obtained based on source domain data training can better adapt to the battery characteristics and working conditions of the target domain so as to solve the problems of battery aging and differences influenced by various external environment factors, and therefore the accuracy of lithium ion battery health life evaluation on the target domain is improved.

The invention discloses an electric vehicle vehicle-mounted battery health degree prediction method and system based on a neural network, and relates to the technical field of battery health degree prediction, and the method comprises the steps: collecting battery multi-source heterogeneous data, extracting key health factors through preprocessing, room temperature correction and staged feature engineering, and introducing a time window embedding strategy to construct a time sequence sample; a multi-module U-BiLSTM hybrid network is adopted as a core prediction model, and Bayesian optimization and an Adam optimizer are combined to complete hyper-parameter optimization and model training; and a prediction result is optimized through digital twinning correction, a physical compensation mechanism and residual service life label normalization processing. The method solves the problems that a traditional method is insufficient in deep feature extraction, low in time sequence information utilization rate and poor in prediction precision under complex working conditions, the aging state and the remaining service life of the battery can be accurately reflected, and reliable technical support is provided for safety management, operation and maintenance optimization and service life evaluation of the battery of the electric vehicle.

The invention relates to an energy accumulator durability test method and system, and the system comprises an extreme working condition simulation module which can achieve high-voltage, wide-temperature-range and high-frequency dynamic loading; the multi-sensor monitoring network integrates a fiber bragg grating strain sensor, a three-axis acceleration sensor and an acoustic emission sensor to realize collaborative monitoring of strain, vibration and microcracks; the intelligent control and energy recovery module is combined with a fuzzy PID and reinforcement learning algorithm, a magnetorheological fluid generator and a super capacitor bank, so that the control precision and the energy utilization rate are improved; and the data analysis and life evaluation platform accurately predicts the residual life by using an improved Bayesian network model and a real-time health index calculation module. According to the test method, a loading mode is dynamically adjusted through a real-time health index, an energy recovery strategy is optimized, and life early warning is triggered. The invention breaks through the limitation of the traditional test and provides an integrated solution.

The invention relates to the technical field of life evaluation, in particular to a turbine blade life evaluation method which comprises the following steps: acquiring surface temperature gradient data of a turbine blade of a gas turbine, extracting the temperature change rate of each area of the blade, analyzing the influence of the temperature change rate on the local stress of the blade, and evaluating the life of the turbine blade. And calculating a thermal stress synthetic effect by combining the blade rotation inertia force and the aerodynamic load to obtain blade thermal stress distribution data. According to the method, the thermal stress synthesis effect is calculated by acquiring the temperature gradient data of the surface of the blade and combining the rotary inertia force and the aerodynamic load, the thermal stress distribution characteristics of the blade in different operation states can be accurately reflected, and the life attenuation rate is calculated by combining the current working condition, so that life prediction can be adjusted along with the operation states; the service life offset is calculated by analyzing the influence of the start-stop load of the gas turbine and comparing creep fatigue joint distribution, so that the influence of the running state of the gas turbine on the service life prediction can be dynamically updated in real time.

The invention provides a damp heat-oxygen-internal pressure-load coupled non-metal pipeline aging device and a method capable of evaluating the service life of an in-service non-metal pipeline. The non-metal pipeline aging device comprises six parts, namely an inner cavity, a box body temperature control system, a box body humidity control system, a pipeline internal pressure control system, a pipeline external load loading device and an air supply circulating system, and an accelerated aging test of a non-metal pipeline under different working condition combinations of four factors, namely damp heat, oxygen, internal pressure and load can be carried out by virtue of the aging device. Measuring oxidation induction periods before and after aging of the in-service non-metal pipeline and the non-metal pipeline with the same mark through a differential heat scanning method, substituting the oxidation induction periods into a non-metal pipeline aging life prediction formula coupled with damp heat-oxygen-internal pressure-load factors, and calculating to obtain the service aging life. According to the method, damp heat-oxygen-internal pressure-external load multi-factor coupling is realized, so that an actual service environment is accurately simulated, and comprehensive technical support can be provided for safe operation of the non-metal pipeline.

The invention discloses an escalator monitoring operation and maintenance method and system based on multi-source data fusion, and relates to the technical field of escalator operation and maintaining.The method comprises the steps that multi-source parameter collection is conducted on a target escalator, and a multi-source sensing data set is generated; transmitting the multi-source sensing data set to a data fusion processing center in real time; generating a fusion data set; running state monitoring of the target escalator is executed, and an early warning signal is sent out according to the monitoring risk value; and performing performance evaluation and risk grade division on the target escalator, executing a preventive maintenance decision, and sending the preventive maintenance decision to an operation and maintenance management user. The technical problems that in the prior art, monitoring of the running state of the escalator is not accurate, risks are difficult to perceive in time, and consequently the running safety and stability of the escalator are difficult to guarantee are solved, a perfect escalator intelligent operation and maintenance management system is constructed, and the safety and reliability of the escalator are improved. The technical effects of performance evaluation, risk grade division, preventive maintenance, environmental adaptability transformation, emergency response and service life evaluation are achieved.

The invention provides a packaging material intelligent selection method and system based on multi-dimensional data analysis, and relates to the field of material management. Vibration, deformation and temperature and humidity data are collected in real time through an environment sensing module to construct a feature matrix; the data analysis module fuses the historical case library and the logistics task duration to predict the material life, and associates the supply chain inventory to generate residual life evaluation; the decision engine module dynamically distributes cost, safety and environmental protection weights, and combines the supplier credit score to generate a multi-level decision scheme; the execution control module verifies the dimensional tolerance of the material and drives replacement operation, and model calibration is triggered through a 5% deviation threshold value; a humidity sensitive material grading standard and a deformation safety limit value are built in the system, and when the temperature and humidity sudden change exceeds the threshold value, deformation quantity prediction and emergency scheme reconstruction are started; a three-level self-optimization mechanism of environmental perception error, material life error and execution error is established, and closed-loop optimization is realized through sensor calibration, supplier credit recalculation and dynamic adjustment of control parameters.

The invention discloses a fatigue test method for a cabin structural member of an offshore wind power generator. A test piece is subjected to surface treatment, a multi-sensor array is arranged, the test piece is placed in a combined system of a multi-axis dynamic loading unit and a collaborative environment simulation cabin, an intelligent control and analysis platform synchronously applies a multi-axis alternating load, salt spray corrosion and a circulating temperature and humidity environment, and real marine service conditions are simulated; the state of a test piece is evaluated in real time based on criteria such as a strain concentration coefficient and acoustic emission energy, a strengthening test is started for potential damage risks, a load strategy is adjusted to track crack propagation when microcracks are monitored, and after the test is ended, a fatigue life prediction model is established or calibrated by combining failure test piece analysis data. And outputting a life evaluation result and a structure improvement suggestion. Through the multi-factor collaborative simulation and self-adaptive control technology, the authenticity and efficiency of the test are improved, and technical support is provided for design optimization and reliability guarantee of offshore wind power cabin structural parts.

The invention discloses a super capacitor energy storage life evaluation method under a fire storage frequency modulation working condition, relates to the technical field of power system energy storage, and can at least partially solve the problems that an existing super capacitor life evaluation method is poor in adaptability to the frequency modulation working condition, insufficient in multi-factor coupling quantization and lack of engineering criteria. The method comprises the steps of simulating a fire storage frequency modulation working condition and collecting multi-dimensional operation data of a super capacitor; preprocessing the collected data and extracting life characteristic parameters such as capacity attenuation rate, equivalent C rate, average temperature rise, frequency modulation load factor and the like; key influence factors are screened through correlation analysis, weight coefficients are trained through a machine learning algorithm, and a coupling influence coefficient model is constructed; establishing a capacity attenuation equation, an equivalent series resistance growth equation and a response delay growth equation, and fusing three criteria to determine a life end point; and dynamically correcting model parameters based on the real-time frequency modulation data and calculating the residual life. Accurate evaluation and dynamic prediction of the service life of the super capacitor under the fire storage frequency modulation working condition are achieved.

The invention provides a digital twin unmanned aerial vehicle circuit state monitoring method and system, and the method comprises the steps: firstly collecting a partial discharge pulse sequence of an unmanned aerial vehicle switching power supply circuit, separating environment electromagnetic noise through polarization filtering operation, and extracting real discharge pulses to generate partial discharge fingerprint data; inputting the fingerprint data into a pre-established fault search tree model, judging the type of a capacitor or inductor component through a first-stage branch node, and matching a degradation mode such as capacitance attenuation or magnetic saturation through a second-stage branch node to determine the fault type; then, corresponding component parameters of the equivalent circuit model are dynamically updated in the digital twin body, and the circuit operation state change trend is simulated; and finally, according to the state change trend, predicting the residual effective working time of the fault component and outputting a monitoring result. According to the invention, the accuracy of service life evaluation of key components in a complex electromagnetic environment is improved.

The invention relates to the technical field of capacitor life detection. The capacitor service life detection method comprises the steps of obtaining electrical performance response information of a capacitor under a multi-excitation condition, constructing a dynamic response behavior path of the capacitor in a state space based on the electrical performance response information, obtaining an evolution behavior sequence, and calculating the service life of the capacitor according to the evolution behavior sequence. And matching the evolution behavior sequence with the aging behavior path in the aging trajectory library, identifying a corresponding degradation trend type, obtaining current detection condition information, inputting the degradation trend type into a life evaluation model, and performing analysis in combination with the current detection condition information to obtain a life evaluation result. The method has the effect of improving the accuracy of service life evaluation of the capacitor.

The invention relates to the technical field of actuation mechanism fatigue life evaluation, and discloses a data coupling actuation mechanism fatigue life evaluation method and system, and the system comprises a plurality of strain sensors and an evaluation server. According to the method, a sensor collects strain and load data to generate a fatigue state data set, a server constructs a regional stress distribution matrix, fatigue damage distribution characteristics are determined, and a maintenance strategy is dynamically adjusted. And when the distribution characteristics are determined, the matrix is processed to extract the characteristics, modeling is performed to divide regions, stress intensity gradient is calculated to predict damage distribution, spatial-temporal distribution characteristics are output through the coupling evaluation model, and a data set is updated. Maintenance strategy adjustment comprises mapping area identification, control load adjustment and other actions, and maintenance resources are dynamically distributed. According to the method, multi-dimensional data coupling analysis is realized, the fatigue life is accurately evaluated, the maintenance strategy is dynamically optimized, and the evaluation accuracy and the maintenance efficiency are improved.

The invention relates to the technical field of spring measurement, and discloses a spring fatigue life test system and a dynamic balance evaluation method, and the system comprises a reference length acquisition module, a dynamic balance state determination module, a working condition spectrum loading module, a stress-strain response data acquisition module, a damage feature extraction module and a life evaluation module. Acquiring a reference length in a free state; step increasing preload is applied, and when it is detected that the deformation rate of the to-be-detected spring is smaller than a set threshold value, it is judged that the to-be-detected spring reaches a dynamic balance state; in a dynamic balance state, loading the to-be-tested spring based on the variable amplitude load time sequence signal, and synchronously acquiring stress-strain response data; identifying the stress characteristics of the to-be-tested spring in the load period; generating a life evaluation report based on the stress characteristics; the accuracy of the fatigue life of the spring can be improved.

The invention discloses a fatigue life evaluation method for a single-crystal high-temperature alloy blade with a recrystallization defect, and relates to the technical field of blade life evaluation, and the method comprises the following steps: obtaining single-crystal high-temperature alloy data through a first experiment, and carrying out data analysis; a three-dimensional finite element model of the single-crystal high-temperature alloy blade is constructed, and first simulation of crack propagation is carried out; and constructing a recrystallization defect-fatigue life database. According to the fatigue life evaluation method for the single-crystal high-temperature alloy blade with the recrystallization defect, the fatigue life of the single-crystal high-temperature alloy blade with the recrystallization defect is predicted in a damage-free mode, the serviceable blade can continue to serve, and the economic loss of blade scrapping caused by dissection is reduced; the fatigue life of the blade is evaluated on the basis of crack parameters such as initial crack positions, initial crack front edge shapes and three-dimensional crack propagation tracks generated by recrystallization defects distributed at different parts of the blade, and the evaluation result is more accurate.

The invention belongs to the technical field of mechanical structure health monitoring and life prediction, and particularly relates to an engineering machinery bearing structure fatigue life intelligent monitoring method, which comprises the steps of 1, building a high-fidelity geometric model, 2, building a finite element model, 3, building a finite element simulation data set, 4, building a neural network agent model, and 5, building a neural network model. 5, working parameters and loading history are obtained; and 6, a visual operation and maintenance platform is integrated. According to the intelligent monitoring method for the fatigue life of the engineering machinery bearing structure, the evolution process of fatigue damage of the structure can be intelligently deduced based on the working parameters and the loading history of the structure, dynamic calculation of the residual life and real-time early warning of the service risk are achieved, calculation resources are saved, the fatigue life evaluation efficiency is improved, and the engineering machinery bearing structure fatigue life monitoring method is suitable for popularization and application. The problems of dependence on off-line simulation, response lag, discontinuous evaluation and the like in traditional structure life management are solved, the intelligent operation and maintenance level of the engineering machinery is effectively improved, and safe and efficient operation of the engineering machinery is guaranteed.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention discloses a button switch service life detection method, a detection device and a medium, button use parameters and physical response signals are collected in real time, instantaneous pressing force and accumulated fatigue damage amount are separated by adopting a signal decoupling technology, and a composite damage index is calculated based on a nonlinear rule. And the life prediction module dynamically selects an exponential decay model or a power law decay model in combination with the total damage amount, and outputs a residual life evaluation result. A calibration unit is arranged in the device, standard pressure is periodically applied, response signals are collected, the model sensitivity coefficient and fatigue parameters are dynamically adjusted through a recursive optimization algorithm, and long-term stability of prediction precision is guaranteed. According to the method, full-process closed-loop control from data acquisition, damage feature extraction to intelligent prediction is realized, the accuracy and real-time performance of button service life evaluation are remarkably improved, and the equipment shutdown risk and maintenance cost caused by button failure are effectively reduced.

The invention discloses a method and a system for testing corrosion fatigue of a bone implantation device under a pathological condition, and relates to the technical field of orthopedic medical device evaluation, and the method comprises the following steps: constructing a dynamic load spectrum database covering physiological and pathological states; in-vitro reproduction of a dynamic mechanical environment borne by the bone implantation instrument in physiological and pathological states is completed through a multi-axis servo system; and acquiring service state data of the bone implantation instrument, and analyzing the service state data of the bone implantation instrument to obtain coupling experiment data. According to the invention, the defects of single load mode and environment simulation distortion in the traditional test method are overcome, high-fidelity simulation and comprehensive monitoring of the corrosion fatigue behavior of the bone implantation device under the pathological condition are realized, the consistency of the in-vitro test result and the in-vivo actual service performance is improved, and the application prospect is wide. And a scientific and unified evaluation basis is provided for material screening, structure optimization and service life evaluation of different types of bone implantation instruments.

The invention discloses a method for rapidly evaluating the fatigue life of a steel trestle for extreme heavy load impact, and relates to the technical field of bridge fatigue life evaluation. Establishing an extreme impact load probability model; multi-source bridge monitoring; establishing and correcting a finite element model; efficiently simulating a fatigue damage field; carrying out multi-stage modeling and prediction on crack propagation; performing multi-scale damage accumulation fusion analysis; and carrying out residual life evaluation and multi-dimensional risk grading. Through combination of extreme impact load modeling, multi-source data fusion and a physical constraint neural network technology, the impact load can be accurately identified and the stress response of the structure can be calculated under an extreme heavy load working condition, and meanwhile, a three-stage crack propagation model is introduced to comprehensively describe the whole process from microscopic damage to structure fracture. And the efficiency and reliability of fatigue life evaluation are obviously improved.

The invention relates to the technical field of bridge engineering detection and life evaluation, and discloses a bridge deck residual fatigue life prediction method and system based on real-time image information. The objective of the invention is to solve the problems of low subjective efficiency, time-consuming numerical simulation, low automation and intelligence level and poor generalization ability of the existing in-service bridge RC deck residual fatigue life evaluation means. According to the scheme, an evaluation system including image acquisition, physical simulation data set generation, deep learning evaluation model and result output is constructed; generating a bridge deck crack image-residual fatigue life pairing data set covering mirror symmetry and extreme crack modes by using a high-fidelity physical simulation platform, and training an end-to-end deep convolutional neural network regression model; a bridge floor image is collected on site and preprocessed, the residual fatigue life is directly output by an input model, and finally a visual evaluation report is generated.

The invention discloses a high-frequency induction intelligent control system and a thermal fatigue test method, and belongs to the technical field of thermal fatigue tests.The method specifically comprises the steps that a to-be-tested sample piece is installed, heating parameters are set, heating is started, heating is stopped after the to-be-tested sample piece is heated to a preset temperature, and the to-be-tested sample piece is cooled until the temperature drops to a preset lower limit; in the heating and cooling process, the surface and core temperature of a to-be-detected sample piece is collected in real time, the heating-cooling step is repeatedly executed according to the preset number of cycles or to a preset damage threshold value, the temperature curve, the power curve and the cycle count of each cycle are generated, the recorded temperature curve, the power curve and the cycle count are analyzed, and the temperature of the to-be-detected sample piece is obtained. Outputting the thermal fatigue life value of the to-be-tested sample piece; the temperature is intelligently controlled through high-frequency induction, the uniformity of the temperature is ensured, the thermal fatigue life is accurately predicted through multi-dimensional life evaluation and layered modeling, and the speed, precision and reliability of thermal fatigue testing are remarkably improved.