2153 results about "Fatigue damage" patented technology

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

Disclosed is a method for evaluating the fatigue damage and life of a bridge structure under a multi-factor coupling effect. The method comprises the following steps: S1, reproducing complex service environment action values; S2, analyzing performance values of a bridge structure; S3, calculating coupling fatigue damage of a vulnerable part; and S4, predicting a remaining life of the bridge structure. By adopting the method, the fatigue damage condition and remaining life of an in-service bridge under a multi-factor coupling effect can be accurately evaluated, and the effect of each factor / action and the coupling effect the factors / actions in fatigue damage of a vulnerable part are quantitatively analyzed, thereby providing data support and decision-making reference for the operation and maintenance management, reinforcement and repair, and optimal design of bridges.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

The invention relates to the technical field of material fatigue performance testing, in particular to a fatigue acceleration detection method and system for a high-fatigue-resistance cast wheel, and the method comprises the steps: generating a dynamic load spectrum containing a multi-axis combined load according to the actual operation condition of a target cast wheel; applying a dynamic load spectrum to the target casting wheel to perform an acceleration test, and monitoring dynamic strain responses on the surface and inside of the target casting wheel in real time based on the distributed sensing array; establishing a damage accumulation calculation model by taking a loading parameter of the dynamic load spectrum as an additional correlation condition, and calculating an equivalent fatigue damage accumulation value based on the dynamic strain response; when the equivalent fatigue damage accumulated value reaches a preset damage threshold value, it is judged that the target cast wheel reaches the fatigue life end point, and the damage evolution rule in the acceleration test is kept consistent with the damage mechanism of the actual operation working condition. According to the invention, the problems of multi-axis load simulation distortion, incomplete damage monitoring, low service life prediction precision and the like in traditional fatigue detection are effectively solved.

The present invention relates to the technical field of electrical equipment monitoring, and in particular to an electrical equipment state monitoring and dynamic test evaluation system, comprising a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module and a state evaluation and prediction module; wherein: the data acquisition module is used for real-time acquisition of raw data during the operation of the electrical equipment; the data processing module is used for processing the raw data collected by the data acquisition module to generate a standardized data set; the feature extraction module is used for extracting relevant feature parameters reflecting fatigue damage accumulation from the raw data; the dynamic test evaluation module is used for simulating the fatigue damage evolution process of the electrical equipment under complex working conditions; the present invention, by real-time monitoring of fatigue damage accumulation of the electrical equipment, combines dynamic test evaluation with finite element simulation technology, accurately predicts the remaining life of the equipment and the time of occurrence of a fault, and improves the accuracy and early warning capability of the equipment health state evaluation.

The invention provides a service life prediction method based on structural fatigue analysis of floating type offshore wind power equipment, and relates to the technical field of wind power generation. Equipment structure data of the floating type offshore wind power equipment is acquired, a sensor network is connected to acquire environmental load information, and a multi-body coupling simulation model is established based on the equipment structure data; environmental load information is input into the multi-body coupling simulation model for stress amplitude and load change, fatigue damage accumulation data is calculated based on the multi-body fatigue load spectrum, and the residual life of the floating type offshore wind power equipment is predicted through the fatigue damage accumulation data. The technical problem that in the prior art, unique stress characteristics and dynamic response of floating type offshore wind power equipment are ignored, and consequently the service life of the wind power equipment is predicted inaccurately is solved. The technical effects that the fatigue accumulation process of the floating type offshore wind power equipment is accurately calculated, and the operation safety and the service life prediction precision of the floating type wind power equipment are improved are achieved.

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 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 discloses an intelligent prediction method for fatigue damage evolution of an aero-elastic sheet based on digital twinning, and the method comprises the following steps: S1, constructing a finite element model, carrying out the modal calibration, and building a digital twinning model; s2, acquiring real-time monitoring data; s3, constructing a crack-induced potential energy tensor field to obtain a crack development potential energy probability distribution diagram; s4, generating a probability prediction map by adopting a potential energy driven map evolution coupling algorithm; s5, constructing a topological damage mapping function, and predicting a dynamic evolution process of topological damage along with load time; s6, applying a time folding algorithm in the dynamic evolution process to obtain a fatigue state submerged space representation; and S7, inverting the topological damage state of the current structure based on the contrast twinborn variational self-encoding network, updating the digital twinborn model, and finally outputting the structural damage evolution trend. According to the method, digital twinning and graph evolution algorithms are fused, and intelligent prediction of the fatigue damage of the aero-elastic sheet is realized.

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention relates to the technical field of equipment life management, in particular to a life prediction method and system based on an application analysis spindle, and the method comprises the steps: building a modal sensing network, collecting multi-source operation data in the operation process of the spindle, and generating a multi-source operation characteristic matrix; based on the multi-source operation characteristic matrix, establishing multi-application characteristic mapping, and generating a multi-scene characteristic vector for describing an equipment operation state; performing multi-dimensional coupling fatigue analysis on the multi-scene feature vector, and outputting a fatigue damage accumulated value of the main shaft; and according to the fatigue damage accumulated value, obtaining and combining historical motion data of the main shaft, establishing a different-parameter life prediction model, and outputting a residual life prediction value based on the different-parameter life prediction model. According to the invention, the problems of single data source, insufficient prediction precision under complex working conditions, difficult modeling of nonlinear fatigue damage and lack of dynamic feedback and correction capability in a traditional main shaft life prediction method are effectively solved.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention belongs to the field of wind power plant grid-connected control, particularly relates to a wind power plant active power efficient distribution method considering the fatigue damage degree of fans, and aims to solve the problem that scheduling instruction power is uniformly distributed to all fans in the prior art. And the fan fault probability is increased due to the fact that the influence of power adjustment on fan fatigue damage is not considered. The method comprises the steps of fan load estimation based on stress analysis and energy conservation, low-complexity fatigue damage quantification based on an improved energy method, and construction of a multi-target optimization model with the minimum total fatigue load of a wind power plant and the most uniform fatigue load distribution between fans as targets based on a Gulin method, an entropy weight method and an improved matter element extension model. According to the method, rapid and accurate power distribution is realized, so that the fan runs in a proper state, the accumulation of fatigue damage and the risk of failure occurrence are reduced, and the overall running efficiency and economic benefit of a wind power plant are improved.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The invention relates to the technical field of stress detection, in particular to an absolute stress detection method and system for a concrete bridge, and the method comprises the following steps: obtaining stress data of a bridge girder, a support, a box girder and a main tower, collecting wind speed and wind direction sensor data, calculating a wind load gradient value, and extracting wind load primary and secondary influence regions and a transmission buffer region. And generating a wind load area stress data set. According to the method, through environmental factors and stress data, the influence of wind load on structure stress is identified, data comprehensiveness is ensured, through wind load gradient change rate calculation and stress sudden change area extraction, the structure weak point identification capability is improved, the stress fluctuation trend is quantified through time sequence analysis, and the cumulative change rate is deduced, so that fatigue damage prediction is more accurate; abnormal point identification is combined with deviation ratio analysis, measurement data is corrected, analysis reliability is improved, fatigue damage information is extracted based on stress attenuation features, local fatigue evaluation is optimized, a damaged area is accurately locked, and the accuracy of structural health monitoring is improved.

The invention belongs to the technical field of plate spring performance analysis and evaluation, and discloses a high-strength two-stage gradual-change-stiffness plate spring performance analysis and evaluation system and method.The method comprises the steps that plate spring initial mechanical data collection and heterogeneous load response integrated processing are conducted on the basis of a multi-dimensional vehicle-mounted test experiment, and plate spring integrated mechanical response data are generated; performing double-rigidity gradient recognition analysis to generate rigidity transformation critical data; performing critical region stress field deconstruction analysis to generate stress field distribution characteristic data; carrying out local microstructure change characterization to generate structural deformation data; constructing a multi-scale stress transfer link, and generating critical region stress transfer path data; performing fatigue damage accumulative mapping to generate accumulative feature data; constructing a fatigue life prediction model, and generating life prediction data; carrying out comprehensive performance index quantitative evaluation to generate a performance evaluation result; executing plate spring optimization improvement scheme generation and verification; and a scientific basis is provided for performance optimization of the two-stage gradual-change-rigidity plate spring.

The invention relates to the technical field of performance detection, and discloses a blade structure performance detection device and method, and the method comprises the steps: carrying out the laser ultrasonic scanning of a titanium alloy engine blade, and obtaining multi-mode spectrum feature data; performing parameter inversion and iterative optimization calculation according to the multi-modal spectrum characteristic data to obtain material parameter distribution data of a local area of the blade; performing annular scanning analysis on the titanium alloy engine blade based on the material parameter distribution data to obtain local structure feature data; performing modal decomposition on the local structure feature data to obtain fatigue damage accumulation data; and inputting the accumulated fatigue damage data into a dual decoupling diagnosis model for comparative learning and antagonistic feature separation to obtain a target diagnosis result, the method overcomes the limitation of a traditional linear superposition method, accurately predicts the fatigue damage evolution law under a complex load, effectively solves the problem of working condition interference, and improves the diagnosis accuracy. And the robustness and generalization of fault diagnosis are enhanced.

The invention relates to the technical field of wind turbine generator operation and maintenance, in particular to a wind turbine generator probability fatigue life prediction method and device, equipment and a medium. The method comprises the following steps: constructing a digital twinborn model of a wind turbine generator; based on operation load data, collected in real time, of the wind turbine generator, a digital twinborn model is adopted to determine corresponding stress data; and inputting the stress data into a pre-trained probabilistic fatigue life prediction model to obtain a current probabilistic fatigue life result of the wind turbine generator. According to the method, the current stress data of the wind turbine generator are calculated by constructing the digital twinborn model of the wind turbine generator, and the current probability fatigue life result of the wind turbine generator is obtained based on the pre-trained probability fatigue life prediction model. Therefore, according to the method, through real-time interaction of the physical unit and the virtual model, accurate prediction of the fatigue damage of the wind turbine generator is achieved, that is, the method can reflect the operation state and the fatigue damage condition of the wind turbine generator in time, and the real-time performance is high.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention relates to the technical field of fatigue evaluation, and discloses an offshore wind turbine blade frequency domain fatigue evaluation method and system based on an MIMO nonlinear dynamic system, and the method comprises the steps: collecting the load data of an offshore wind turbine blade; preprocessing the load data; an MIMO nonlinear dynamic system is established; establishing a dynamic mapping relation between the preprocessed load data and the frequency domain response by using an MIMO nonlinear dynamic system; decomposing the time domain dynamic response data by using a frequency domain decomposition method, and quantifying the influence weight of each frequency band load on the blade stress based on a frequency domain energy distribution matrix to obtain a frequency domain energy distribution function of the total stress; and calculating the fatigue damage of the blade through a frequency domain fitting optimization method based on the frequency domain energy distribution function of the total stress. The method solves the problems that fatigue damage evaluation confidence is insufficient under the multi-load coupling effect and actual engineering requirements are difficult to meet in the prior art, and has the characteristic that the coupling effect of the wind load and the wave load can be accurately processed.

The invention relates to the technical field of ocean engineering and mechanical engineering, in particular to a method and a device for analyzing crack propagation of a fan blade considering a real ocean environment. According to the method, the real marine environment working condition is considered, dynamic simulation analysis is conducted on the fan blade, distribution information of loads borne by the fan blade is determined and loaded on a finite element model of the fan blade, a fatigue hot spot area is determined according to a finite element fatigue damage analysis result, and crack arrangement is conducted. And calculating a stress intensity factor of a crack tip and performing crack propagation simulation according to a finite element analogue simulation result so as to evaluate the fracture damage risk of the fan blade in a real marine environment. The wind turbine blade crack propagation analysis thought under the complex stress condition is provided, equipment faults and risks caused by blade breakage can be prevented, a scientific basis is provided for overhaul and operation and maintenance of a wind turbine, and therefore economic losses caused by failure of the wind turbine blade are reduced.

The invention provides an offshore floating type fan foundation fatigue analysis method, electronic equipment and a medium, a stress superposition-based multi-load fatigue analysis method is established, and linear superposition is performed on a time sequence tower bottom load response, a time sequence mooring response and a time sequence wave response under the same working condition to obtain a stress time calendar curve under the corresponding working condition; carrying out rain flow counting on the stress time calendar curve, and combining an S-N curve to obtain the total fatigue damage of the working condition; and performing weighted calculation on the total fatigue damage of all working conditions in the target service time of the fan foundation to obtain full-working-condition fatigue damage. According to the method, the influence of each load is fully considered on the overall scale, different loads of the same working condition are matched, the method is suitable for various complex wind and wave coupling conditions, stress time sequences of different loads are superposed and subjected to fatigue treatment, the full-working-condition fatigue damage of the fan foundation under multiple load components is obtained, and the precision of fatigue damage analysis is improved.

The invention relates to a simulation prediction method for the plugging life of a contact element of an electric connector, and the method comprises the steps: building a three-dimensional simulation model of a plug end and a socket end of the electric connector, and defining the material attributes and contact relationships of all parts in the three-dimensional simulation model; performing finite element grid discretization on the three-dimensional simulation model; applying a fixed constraint to the socket end, applying a displacement load for simulating the plugging process to the plug end, and performing statics solution to obtain plugging force in the plugging process of the plug end and the socket end and contact pressure data of the plug end contact piece and the socket end contact piece; and mapping a statics solution result into a fatigue analysis module, and simulating, calculating and outputting a life distribution result of the plug end contact element and the socket end contact element under repeated plugging and unplugging loads. The method has the effect of analyzing the stress-strain state of the internal contact element of the electric connector and the fatigue damage of the internal contact element under repeated plugging and unplugging so as to help monitoring personnel to discover the potential mechanical failure problem in time.

The invention discloses an alloy functional material fatigue performance prediction model building method, and relates to the technical field of material scientificity, the method comprises the following steps: installing a sensor group on an alloy surface, using an instrument to collect alloy performance influence data, and transmitting the data to a data processor through a wireless network for preprocessing, data storage and analysis; the method comprises the following steps: constructing a load performance prediction model, calculating a thermal coupling fatigue damage coefficient and a multi-axial load fatigue damage coefficient, summarizing and calculating to obtain a composite fatigue damage coefficient, and carrying out preliminary evaluation on the composite fatigue damage coefficient and a safety fatigue damage threshold value. The environment corrosion influence coefficient is calculated by combining the corrosion influence data set and the precipitation object integral number, corrosion degree prediction risk assessment is carried out on the environment corrosion influence coefficient and the safety fatigue damage threshold value, finally, the comprehensive fatigue damage coefficient is obtained, comprehensive corrosion degree assessment and fatigue damage prediction are carried out, real-time early warning and optimization suggestions are provided, and the safety fatigue damage risk is improved. The safe use of the alloy material is ensured; the service life is prolonged.

The invention discloses an intelligent monitoring, operation and maintenance platform and method for a soil covering tank based on digital twinning, and belongs to the technical field of digital twinning. A digital twinborn platform is used as a core, a physical geometry simulation module, an algorithm prediction module, a sensing communication module and a virtual visualization module are integrated, and the functional modules perform data interaction and cooperative work through the digital twinborn platform. Firstly, a physical geometric simulation module establishes a three-dimensional geometric model based on the actual structure of the soil covering tank and obtains multi-working-condition mechanical response data; secondly, collaborative prediction of external load distribution, structural response characteristics and fatigue damage is achieved through an algorithm prediction module; thirdly, the sensing communication module collects and processes sensor data in real time, and the model is driven to be dynamically updated; and finally, the virtual visualization module dynamically presents the multi-physical field state and damage evolution trend of the soil covering tank through a multi-source data fusion rendering technology. According to the invention, the comprehensive monitoring capability and failure early warning precision of the earthing tank are obviously improved, predictive maintenance decision is supported, and the operation and maintenance cost is reduced.

The invention discloses a method and a system for predicting and monitoring frame fatigue based on twinning of the Internet of Vehicles. The method comprises the following steps: analyzing a vehicle use scene and a working condition ratio based on historical data of the Internet of Vehicles; carrying out a frame real vehicle load test, and converting a strain-time history into a load-time history in combination with static loading calibration; performing weighted combination on each load history according to a working condition ratio to obtain a weighted composite load-time history; reconstructing a stress-time history of each measuring point based on finite element analysis, and constructing a stress cyclic spectrum by adopting a rain flow counting method; calculating the fatigue life of the frame by combining a stress-life method and a Miner linear damage criterion; and constructing a frame twinborn model corresponding to the real vehicle at the cloud, accessing vehicle operation data in real time, dynamically updating a stress process and fatigue damage, calculating the residual fatigue life, and triggering early warning when the life is lower than a threshold value. The method can achieve the precise evaluation and online monitoring of the fatigue life of the vehicle frame, and is suitable for the durability design and structural health management of new energy commercial vehicles.

The invention discloses a real-time dynamic optimization adjustment method and system for a tunneling process, and relates to the technical field of intelligent tunneling, and the method comprises the following steps: obtaining the operation state data of tunneling equipment in real time, and recording the dynamic change characteristics of a stratum; the tunneling parameters are optimized and adjusted in real time, the tunneling machine is prevented from entering a resonance interval, fatigue damage of key components such as a cutter head and a thrust cylinder is reduced, the stability of equipment is improved, and the service life of the equipment is prolonged. A multi-objective optimization control strategy is adopted, and an intelligent algorithm is combined, so that the tunneling mode is automatically adjusted, the tunneling efficiency is improved, and manual intervention is reduced. According to the stratum prediction method based on machine learning, the adaptability of the heading machine to complex stratums is enhanced, stratum sudden change risks are warned in advance, and construction potential safety hazards are reduced. And meanwhile, an abnormal state emergency processing mechanism ensures that the tunneling process is stable and controllable, and the construction safety and reliability are improved.

The invention discloses a fatigue load response analysis method for a non-uniform beam of a ship, and particularly relates to the technical field of fatigue load response analys.The vibration coupling degree between the non-uniform beam and other parts of the ship is judged by obtaining the inherent frequency of the non-uniform beam and the other parts of the ship and calculating the vibration coupling degree coefficient; the method comprises the following steps of: acquiring load time-history data of a non-uniform beam, generating a dynamic load spectrum of the non-uniform beam, effectively capturing real load conditions of a ship under various working conditions, and calculating a fatigue damage coefficient of the non-uniform beam through local stress-strain analysis of the non-uniform beam so as to obtain a dynamic load spectrum of the non-uniform beam. A self-adaptive fatigue damage model is constructed based on the vibration coupling degree coefficient and the fatigue damage coefficient, the fatigue damage value of the non-uniform beam is dynamically corrected, the fatigue damage evolution process of the non-uniform beam under different working conditions is reflected through self-adaptive adjustment, and the dynamic property and accuracy of fatigue damage evaluation are improved.