1461 results about "Fatigue damage" patented technology

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 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 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 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 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 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 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 relates to the technical field of ETFE film testing, and discloses an ETFE film structure life prediction method and system based on machine learning, and the method comprises the steps: carrying out the standardization preprocessing of the cyclic load fatigue data of an ETFE film structure in a biaxial stress state, and obtaining a standard feature vector; performing membrane structure geometric weight distribution on the standard feature vector to obtain a weighted feature vector; performing multi-time scale damage feature extraction based on the weighted feature vector to obtain a fused damage feature; inputting the fused damage features into a fatigue damage evolution physical constraint embedder for constraint calculation to obtain physical constraint damage features; bayesian probability life prediction is carried out on the physical constraint damage features, and a fatigue life prediction value of the ETFE film structure is obtained.According to the method, key damage parameters of different deformation stages can be recognized in a self-adaptive mode, the characterization precision of the fatigue behavior of the film structure is remarkably improved, and the life prediction accuracy of the ETFE film structure is improved.

The concrete-filled steel tube arch bridge detection system and method based on multi-modal fusion and quantum optimization are used for bridge structure health monitoring. The system comprises a multi-modal data acquisition module, a quantum optimization data fusion module, a defect self-growth simulation damage prediction module, a low-power-consumption intelligent sensor network and a quantum safety remote monitoring module. The multi-modal data acquisition module is fused with an ultrasonic radar, a millimeter wave radar, a hyperspectral image, a LiDAR and a wireless sensor, and bridge surface and internal damage detection is realized; the data weight is dynamically adjusted by adopting a quantum optimization algorithm, and the fusion precision is improved; in combination with a graph neural network and deformable grid calculation, crack propagation and fatigue damage development are predicted, the power consumption of the sensor is reduced through an energy collection technology, and the safety of remote monitoring data is ensured by adopting a quantum safety remote monitoring module. Compared with the prior art, the method improves the detection precision, prediction capability and data safety, and is suitable for health monitoring of various bridges and infrastructures.

The invention relates to the technical field of machining, in particular to an intelligent control system and method for sapphire substrate polishing, and the method comprises the following steps: S1, surface topography data acquisition: collecting surface roughness data and a three-dimensional topography distribution diagram of a sapphire substrate in real time; s2, generating characteristic parameters: extracting the surface waviness, the local recess depth, the bulge density, the lattice distortion index and the residual stress distribution gradient as the characteristic parameters; s3, dynamic parameter adjustment: generating a dynamic adjustment instruction of the polishing pressure, the rotating speed of the polishing disk and the supply rate of the polishing solution; s4, distribution optimization of the polishing solution: realizing uniform coverage of the polishing solution on the surface of the substrate; s5, stress real-time monitoring: generating a stress abnormity early warning signal; s6, outputting a closed-loop control instruction: feeding back a stress abnormity early warning signal to the adaptive control model, and iteratively correcting the dynamic adjustment instruction; according to the method, the risks of stress concentration and fatigue damage can be reflected in real time, and the safety and high efficiency of the polishing process are ensured.

The invention belongs to the field of asphalt mixture performance analysis, and particularly discloses a fatigue performance analysis method for an asphalt mixture of an asphalt pavement, which comprises the following steps of: synchronously acquiring a dynamic strain field and a temperature field in a material by fusing an embedded sensor in a paving and rolling construction process; a temperature-strain coupled fatigue damage evolution model is combined to construct an on-line fatigue performance analysis system oriented to the whole construction process, so that a complex heat-force coupling environment and a non-uniform compaction process in actual paving operation are effectively restored; scale effect deviation and boundary condition distortion caused by difference between laboratory test piece preparation and on-site working conditions are remarkably reduced, and meanwhile, in the online detection process of the fatigue performance of the asphalt mixture, instant quantitative judgment of the performance state of the pavement structure is realized by positioning a damage remarkable overrun area; and the in-situ monitoring capability on the initial fatigue damage is fully reflected, so that the abnormal strain accumulation process before microcrack initiation can be captured.

The invention relates to the technical field of offshore oil safety assessment, in particular to a process pipeline fatigue damage identification and prediction method, which comprises the following steps: step 1, key pipeline primary selection and failure possibility assessment; 2, collecting and preprocessing multi-modal data; 3, performing model construction and optimization on damage identification; 4, fatigue life prediction and failure early warning are carried out; 5, carrying out correlation analysis on the stress spectrum and the service life; 6, multi-parameter dynamic early warning is carried out; and 7, integrating a real-time monitoring system. According to the method, through multi-modal evaluation of fluid-to-vibration, fluid pulsation, high-frequency acoustic vibration and the like, a stress spectrum is generated by utilizing a rain-flow counting method, the crack growth rate and the residual life are predicted in combination with an S-N curve or a Paris formula, and through correlation analysis of the stress spectrum and the life and multi-parameter dynamic early warning, the crack growth rate and the residual life are predicted. Therefore, the method has the advantages that an online system integrating vibration, acoustic emission and stress monitoring is provided, and real-time evaluation and life prediction of fatigue damage are achieved.

The invention discloses a cable-stayed bridge tension and compression support service life evaluation method and system under random traffic flow load, and belongs to the field of bridge engineering and structure health monitoring, and the method comprises the steps: carrying out the statistical analysis of traffic load data, building a random traffic flow load model, and generating a load spectrum; correcting stay cable force and modal frequency based on bridge monitoring data, and constructing a refined multi-scale finite element model; a unit fatigue vehicle load is applied to the model, a stress influence line is obtained, a rain flow counting method is adopted to calculate stress amplitude, cycle index and average stress, the stress amplitude, the cycle index and the average stress are converted into daily average equivalent stress amplitude and equivalent daily cycle index through a fatigue damage equivalence principle, and the fatigue life of the tension-compression support is evaluated by utilizing a Miner criterion. According to the method, the problem that the influence of three-way alternating stress on bolt fatigue damage cannot be accurately reflected in the prior art is solved, the service life prediction precision of the cable-stayed bridge tension and compression support is improved, the damage of the tension and compression support can be early warned in advance, and data support is provided for safe operation and maintenance of the cable-stayed bridge.

The invention relates to the technical field of parameter simulation, in particular to a parameterized simulation model design method based on a standard part structure. The method comprises the following steps: collecting bolt connection data of standard components in the wind generating set; blade surface vibration feature information is extracted according to the standard component bolt connection data; acquiring stress concentration distribution data at the joint based on the blade surface vibration characteristic information; calculating a local deformation accumulated value of the standard part according to the stress concentration distribution data at the joint; measuring the friction coefficient variation trend of the contact surface based on the local deformation accumulated value of the standard component; deducing pre-tightening force attenuation characteristics according to the change trend of the friction coefficient of the contact surface; and constructing a standard part fatigue damage prediction model based on the pre-tightening force attenuation characteristics. According to the method, the vibration characteristics of the blade surface are collected, the stress concentration distribution at the bolt connection position is indirectly extracted, and the bottleneck that the internal connection state is difficult to directly sense is broken through.

The invention discloses a water turbine runner fatigue life prediction method based on multi-physics-field coupling calculation, and belongs to the technical field of water turbine runners, and the method comprises the steps: collecting the operation condition data of a water turbine set, carrying out the iterative solving of a residual vector based on a multi-physics-field coupling model, enabling the residual to approach zero, and outputting a time sequence result; extracting a vibration stress amplitude and a strain time history based on a time sequence result; accumulating the high-cycle fatigue damage and the low-cycle fatigue damage to obtain total accumulated damage of each runner unit, and performing weighted calculation by counting critical damage and operation duration to obtain the residual life of the hydraulic turbine set; and constructing a training data set containing historical working condition parameters, historical residual life and time sequence results of the hydraulic turbine set, training a Transform network, and generating a residual life prediction curve of the hydraulic turbine set under all working conditions. The method solves the problem that a traditional method is difficult to quantify coupling damage of the turbine runner temperature and the alternating load to predict the fatigue life of the turbine runner.

The invention relates to the field of mechanical fatigue simulation, and discloses a method for simulating and deducing the number of torsion cycles. Comprising the steps that a component three-dimensional parameterized model is constructed, and material fatigue characteristics are defined; applying a periodic torsion load spectrum containing a harmonic component; adopting a stress influence coefficient matrix method to execute quasi-static finite element analysis, and extracting a multi-axis stress state; calculating the maximum alternating shear stress and the average shear stress based on a critical plane recognition technology; converting into a single-axis equivalent stress amplitude through a gradient enhanced multi-axis correction rule; calculating node fatigue damage by combining an S-N curve and a damage accumulation theory; and when the damage reaches a threshold value, outputting a failure cycle number and a position. The innovation points are as follows: the prediction error is controlled within 10% by a critical plane and multi-axis correction technology; a stress matrix method realizes ten thousand times of cycle-level efficient simulation, and the efficiency is improved by 10 times; the risk early warning accuracy rate is improved to 96% by integrating a process correlation defect model; and the research and development period is shortened by 40% through a life cloud picture and an automatic optimization closed loop. The method is suitable for transmission shaft system reliability design.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.

The invention provides an elevator ascending overspeed protection device fault prediction method and system based on big data, and relates to the technical field of elevator safety monitoring, and the method comprises the steps: setting a thermocouple array on the surface of a brake to collect temperature data, constructing a temperature distribution diagram, and extracting a hot spot region; calculating a hotspot migration track and stress distribution by using a big data analysis platform; fitting a damage development curve based on the fatigue damage degree; and space-time correlation analysis is carried out through a multi-point monitoring network, the fault source position and the expansion trend are determined, and fault early warning is realized. Potential faults of the elevator ascending overspeed protection device can be predicted in advance, and the operation safety of the elevator is improved.

The invention discloses a submarine pipeline scour-vibration response analysis method based on a cross-scale coupling framework. The method comprises the following steps: establishing a multi-physics field coupling model according to geographical and structural information of a pipeline layout area and initializing the multi-physics field coupling model; marine environment data are obtained through real-time monitoring, the model is imported for scouring dynamic deduction, and a scouring boundary data set is generated. And reconstructing a multi-physics field solving environment, and forming a real-time physics solving environment package. After the model is updated, pipeline vibration response is analyzed, and a vibration data set is obtained. And performing fatigue damage analysis, deducing a flow guide field disturbance coefficient, updating the model again, performing next time step coupling calculation, and finally obtaining a full-period coupling analysis data set. And evaluating the pipeline operation safety situation based on the data set, and generating and pushing a safety report. Various physical field effects on the submarine pipeline in the marine environment are comprehensively considered, the safety of the submarine pipeline is evaluated more comprehensively, and cross-scale coupling analysis between the scouring effect and the vibration effect is achieved.

The invention provides a remote maintenance and intelligent inspection early warning management method and system for a weight meter, and relates to the technical field of intelligent measurement and monitoring, and the method comprises the steps: arranging a multi-source sensor array on a weighing mechanism to collect detection data, carrying out the three-domain conjoint analysis through an edge calculation unit, and extracting multi-dimensional coupling features; the remote monitoring platform establishes a multi-scale damage evolution prediction model based on deep learning to realize the prediction of microscopic crack propagation and macroscopic fatigue damage; an intelligent material is adopted to realize self-adaptive active compensation, and prediction-compensation-evaluation-optimization closed-loop control is formed. The operation reliability and the metering precision of the weight meter can be improved, and the service life of equipment is prolonged.

The invention relates to the technical field of industrial safety, and provides a rupture disk service life prediction method based on intelligent perception, which comprises the following steps: constructing a multi-mode intelligent sensor array, and collecting data in the running process of a rupture disk in real time; establishing a data transmission network, and transmitting data to a data processing center in real time; preprocessing the collected real-time data to form a standardized data set; and constructing a deep learning model, calculating the fatigue damage cumulant of the rupture disk according to the feature data output by the deep learning model based on a fatigue damage accumulation theory, predicting the residual life of the rupture disk, and generating life early warning information. According to the method, the running data of the rupture disk are collected in real time through the multi-mode intelligent sensor array, the life prediction model is constructed based on the fatigue damage theory, dynamic evaluation and early warning are achieved in combination with a three-level early warning mechanism, multi-source data are fused, the prediction precision is improved, the risk and cost are reduced, and it is guaranteed that industrial production is safe and reliable.

The invention discloses a humanoid robot flexible power cable anti-fatigue design simulation system and method, and relates to the technical field of robots. According to the anti-fatigue design simulation system and method for the flexible power cable of the humanoid robot, through multi-field coupling simulation, cross-scale damage modeling, multi-objective optimization and virtual verification, fatigue damage of the cable in a complex working environment is accurately predicted, and an efficient simulation platform is provided for anti-fatigue design of the cable. The system specifically comprises a multi-field coupling and critical positioning module, a cross-scale damage modeling module, a multi-objective optimization module and a virtual verification and iteration module. Multi-modal data of cable deformation, friction heat generation and resistivity change are dynamically generated through multi-physics field coupling simulation, the evolution process of cable damage is analyzed, and design parameters are optimized to improve the fatigue durability of the cable.

The invention relates to the technical field of load spectrum analysis, and discloses a gear random fatigue load processing method and system based on a GCM counting method.The method comprises the steps that actual operation load data and actual operation environment data of a gear transmission system are collected, and torque is converted into instantaneous contact stress; dividing into a plurality of discrete cells based on discretization processing; carrying out clustering analysis on all discrete cells; constructing a two-dimensional gear counting matrix; the asymmetric cyclic stress is converted into equivalent symmetric cyclic stress, and the total service life of the gear is determined; comparing the equivalent symmetric cyclic stress with cyclic stress under a raindrop counting method to determine an adjustment coefficient, and determining an early warning threshold value according to the total service life of the gear and the adjustment coefficient; and comparing the total fatigue damage value with an early warning threshold value to determine an early warning grade. According to the method, joint statistics of the stress amplitude, the average stress and the environmental characteristics is achieved, and the defects of a raindrop counting method in the aspects of processing meshing discontinuous loads and lacking environmental adaptability are overcome.