233 results about "Life assessment" patented technology

The invention discloses a multi-level feature fusion and time sequence modeling combined lithium battery life prediction method, and relates to the technical field of lithium ion battery health management and life prediction. The method comprises the steps of receiving time sequence observation data in a battery operation process, inputting the time sequence observation data into a pre-constructed local feature extraction model, and introducing a one-dimensional convolutional neural network into the local feature extraction model to perform feature extraction on the time sequence observation data to obtain local feature representation. According to the method, three structures of local feature extraction, global context modeling and bidirectional time sequence modeling are fused, and the battery degradation modeling capability and prediction precision are effectively improved. The TFN adopts an end-to-end architecture design, has good feature perception capability and time-dependent modeling capability, and can adapt to various degradation modes and complex time sequence environments. The method is suitable for life evaluation and health state monitoring in an intelligent battery management system, and has relatively high practical value and popularization prospect.

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

The present invention relates to the technical field of mechanical property testing and characterization. Disclosed are a method and apparatus for calculating a crack propagation life of an engine structure in vibration fatigue, which method and apparatus aim to obtain reliable fracture mechanics parameters and a crack propagation rate curve and provide an accurately quantified crack propagation life, so as to provide methodological guidance for the life assessment of reusable engines. The calculation method comprises: acquiring a time-domain random stress spectrum of stress responses in an assessment section in a finite element model of an engine structure; on the basis of the time-domain random stress spectrum, performing finite element analysis processing on a crack model of the engine structure, so as to determine fracture mechanics parameters of a cracked structure; performing a vibration fatigue performance test on a crack propagation rate model, which is set up according to a preset crack propagation mode, and determining, by using parameter identification, a target parameter value corresponding to the crack propagation rate model; and in combination with the parameter value corresponding to the crack propagation rate model and a preset critical crack length value, performing crack propagation life calculation, so as to obtain a target crack propagation life.

The invention discloses a submarine cable insulation multi-field aging prediction method and system, and relates to the technical field of material science and engineering. An existing model has the problems that parameter interaction effect analysis is insufficient, feature weight distribution is not scientific, a data standardization method is insufficient, and the capacity of capturing dynamic response and nonlinear aging behaviors is weak. The method comprises the steps of multi-physics field accelerated aging experiment design and data acquisition, key characteristic parameter screening through grey correlation analysis, support vector machine modeling and optimization, dynamic aging behavior evaluation and service life prediction and feedback adjustment. According to the technical scheme, aging behaviors are comprehensively captured through a multi-physics field cooperation experiment, key parameters are scientifically screened in combination with grey correlation analysis, a nonlinear relation is optimized through support vector machine modeling, long-term prediction precision is improved through dynamic feedback adjustment, and reliability is enhanced through quantitative life evaluation; the aging characteristics of the submarine cable insulating material in multiple physical fields are comprehensively and accurately reflected, and a scientific basis is provided for multi-dimensional evaluation of the aging performance of a high polymer material.

The invention relates to the technical field of semiconductor photoelectronics, and discloses a multi-physics field coupling silicon-based optical interconnection chip degradation model construction method and application, and the method comprises the steps: firstly constructing a multi-physics field coupling analysis model, and quantitatively representing the coupling relation of thermodynamics, optics, electricity and material aging through an inter-field coupling equation and a material aging dynamic model; simulating field distribution of a chip key area under multi-environment stress through finite element analysis, and extracting data; then historical degradation data is collected by combining an accelerated aging test and long-term operation monitoring, and a degradation parameter prediction model is established after data processing based on machine learning; and finally, inputting a finite element result and a prediction result into a material aging dynamic model, and finally forming a multi-physics field coupling degradation model. The problems that the prior art depends on a single physical field, degradation mechanism analysis is incomplete, and service life evaluation is inaccurate are solved, and support can be provided for reliability evaluation, structure optimization and service life prediction of the silicon-based optical interconnection chip.

The invention discloses an intelligent battery cell life prediction system and method based on multi-physics field data fusion, and the method comprises the following steps: carrying out the initial configuration of the system hardware composition corresponding to a battery cell life prediction electronic measurement system, achieving the distribution point optimization and visual mapping, and obtaining the distribution point visual configuration data; performing millisecond-level hardware synchronization and CRC verification based on the distribution point visual configuration data, and performing multi-physics field association fusion and cell coupling feature construction at the same time to generate a cell multi-physics field coupling feature matrix; carrying out life event self-injection processing and battery cell life intelligent prediction on the battery cell multi-physics field coupling characteristic matrix to obtain a battery cell life evaluation report; and performing visualization and archiving processing based on the battery cell life evaluation report to generate a battery cell life prediction electronic measurement file. According to the invention, high-precision, multi-point and synchronous closed-loop acquisition of the multi-physical field data of the battery cell can be realized, and the battery cell life prediction efficiency is improved.

The invention provides an electric power micro sensor life prediction model construction method and system, and the method comprises the steps: determining a plurality of acceleration test stages and acceleration factor combinations corresponding to the acceleration test stages based on the obtained historical degradation state information of a sensor; and carrying out an acceleration test on the sensor based on the acceleration factor combination corresponding to the plurality of acceleration test stages to obtain an acceleration test data set, and training the life prediction model by taking the acceleration test data set as an input and taking the residual life of the sensor as an output to obtain a trained life prediction model. The complex working condition of the electric power microsensor in the actual working environment is simulated more truly in a multi-stage multi-acceleration-factor combination mode, interference and mutual influence between different failure mechanisms are avoided, and the accuracy and comprehensiveness of life evaluation are improved.

A lithium ion battery polycrystal / single crystal positive electrode material multi-physics field coupling modeling and failure analysis method comprises the steps that a three-dimensional positive electrode particle structure model is constructed, a polycrystal structure is generated by randomly filling spherical boundaries with non-overlapped spherical crystal grains, and single crystals are complete spheres; importing the model into a finite element simulation platform, and setting a multi-physics field coupling interface; establishing a diffusion, migration and electrochemical reaction kinetic model; establishing a lithium concentration-strain-stress coupling relation and solving the lithium concentration-strain-stress coupling relation; setting a failure criterion based on stress distribution to identify a damaged area; and outputting a simulation result and carrying out structure-performance correlation analysis. The method can reveal the evolution behavior difference of the polycrystalline positive electrode and the monocrystalline positive electrode in the electrochemical-mechanical process, provides reliable theoretical and simulation support for electrode material design, optimization and life evaluation, and provides powerful technical support for optimization design of the high-performance lithium ion battery positive electrode material.

The invention discloses a heavy-duty gas turbine blade creep fatigue life design method considering an anisotropic effect, and relates to the technical field of gas turbine key component creep fatigue life evaluation.The method comprises the steps that orientation factor parameters on characteristic orientation are determined based on tensile data on [001] crystal orientation; constructing a modified stress relaxation formula; constructing an anisotropic failure strain energy density equation based on the pure creep performance data and the orientation factor parameters, and determining an upper platform of failure inelastic strain energy density on [001] crystal orientation; further determining an upper platform of the failure inelastic strain energy density on the feature orientation; constructing a creep damage prediction model based on a modified stress relaxation formula and an anisotropic failure strain energy density equation; and adopting the creep damage prediction model to obtain the creep fatigue life of the anisotropic turbine blade material sample. The method meets the actual requirements of service life evaluation of the high-temperature part of the heavy-duty gas turbine.

The invention relates to the technical field of life evaluation, in particular to a circuit breaker life evaluation method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the steps: collecting multi-dimensional operation data in the operation process of a circuit breaker in real time; extracting a high-frequency-band energy ratio as a contact system abrasion loss characteristic parameter; establishing an arcing energy accumulation model according to the arc light intensity time sequence signal, and calculating an effective arcing energy value of a single operation; constructing a life weight distribution model, inputting the wear loss characteristic parameters, the arcing energy accumulation value and the environment temperature and humidity data into the model, and outputting a mechanical wear index, an electric corrosion index and an environment degradation index; establishing a three-axis linkage correction function; generating a comprehensive life evaluation index based on the corrected mechanical wear index, electric corrosion index and environmental degradation index, and triggering an early warning signal when the index exceeds a preset threshold value; according to the invention, by establishing the life evaluation model based on multi-dimensional data fusion, the precision and reliability of life prediction of the circuit breaker of the extra-high voltage converter station are significantly improved.

The invention relates to the crossing field of rail transit intelligent operation and maintenance and digital twinning technologies, and discloses a subway train full-life-cycle intelligent maintenance decision-making system and a subway train full-life-cycle intelligent maintenance decision-making method. The system comprises a digital twin modeling module used for constructing a train full-dimension virtual model and realizing real-time data synchronization with a physical entity; the full-life-cycle data acquisition and fusion module is used for integrating multi-source heterogeneous data of all stages of design, manufacturing, operation, maintenance and decommissioning to form a unified data asset library; the intelligent analysis and decision-making module is used for performing state monitoring, fault prediction and residual life evaluation based on the virtual model and the data asset library, and generating a maintenance decision-making scheme through multi-objective optimization; and the maintenance execution and feedback module is used for issuing a decision and executing data feedback to realize closed-loop optimization of the system. The method is realized based on the system. The method can support the subway train maintenance mode-to-state guide conversion.

The invention relates to the technical field of fault diagnosis and bearing life detection, in particular to a high-speed rail axle box bearing life cycle test bench and a test method.The test bench comprises a test bench body, a test head, a driving system, a loading system, a temperature control system, an electrical control system, a computer monitoring system and a data acquisition system; the speed spectrum and the load spectrum can be preset through program control, complex and strong load conditions and variable environment temperatures of a high-speed train can be comprehensively simulated, full-life-cycle tests can be carried out on high-speed rail axle box bearings of various models under high-speed, heavy-load and high-frequency vibration conditions, and the device has the advantages of being easy to operate, safe, reliable and efficient in testing and has a wide application prospect. The problems that in the prior art, the high-speed rail axle box bearing operation environment simulation is incomplete, and the service life evaluation credibility is low can be effectively solved.

The invention belongs to the technical field of transformer insulation loss and life evaluation, and discloses a power transformer thermal life loss calculation method, system, device and medium, and the method comprises the steps: calculating the bidirectional load rate when a transformer is connected with distributed new energy, and judging the current power flow operation state of the transformer; obtaining a rated loss parameter of the transformer under a rated condition, and calculating the load loss of the transformer in combination with a harmonic loss factor corresponding to the transformer under the bidirectional power flow; voltage per unit values of the high-voltage side and the low-voltage side of the transformer are obtained, and the no-load loss of the transformer is calculated in combination with the iron core loss factor of the transformer; calculating the hot spot temperature of the transformer under the power flow switching condition; and calculating the relative aging rate of the transformer. The method is suitable for calculating the hot-spot temperature and the insulation life loss of the transformer in the scene of transformer power flow switching caused by high-proportion new energy penetration, the hot-spot temperature and the relative thermal life loss of the transformer under the condition of power flow reversal can be predicted, and operation and maintenance management is facilitated.

The invention discloses a crane full life cycle evaluation method based on digital twinning, and relates to the technical field of cranes, the method comprises the following steps: determining a primary loss structural component set and a secondary loss structural component set; constructing an initial digital twin structure model; performing time sequence updating on the initial digital twinning structure model to obtain a digital twinning structure monitoring model sequence; performing multi-receptive-field space-time state interaction enhancement on the digital twin structure monitoring model sequence, and determining digital twin space-time state features of the target crane; and carrying out life evaluation based on the digital twin space-time state characteristics of the target crane, and determining a full life cycle stage and a life evaluation result of the target crane. The technical problems that in the prior art, crane operation data analysis depth is not enough, so that full life cycle evaluation is not comprehensive, and hysteresis exists are solved. The technical effects of carrying out multi-level component interaction analysis on the crane and improving the accuracy of full-life-cycle evaluation are achieved.

The invention provides a ship and ocean engineering structure corrosion fatigue crack propagation rate prediction method based on machine learning. The method comprises the following steps: S1, obtaining all characteristic variables; s2, screening core influence factors from all feature variables through a feature selection algorithm to obtain feature data; s3, establishing a machine learning prediction model which adopts one of a plurality of machine learning algorithms, and inputting the feature data into the machine learning prediction model to learn a nonlinear mapping relationship between corrosion fatigue crack growth rate data and the selected variables, optimizing the machine learning prediction model by using a hyper-parameter optimization method to obtain a trained model; and S4, inputting feature data of a to-be-predicted target into the trained model for prediction to obtain the corrosion fatigue crack growth rate of the to-be-predicted target. The method is suitable for service safety analysis and life evaluation of key facilities such as ships, ocean platforms and submarine pipelines.

The invention discloses a life protection method and device for a dynamic reconfigurable energy storage system, and relates to the field of new energy power system energy storage, and the method comprises the steps: obtaining the charge state, health state, capacity and historical cycle index of each battery pack; according to the charge state and the health state of each battery pack, determining the health coefficient of each battery pack in an empowerment mode; determining a load coefficient of each battery pack in an empowerment mode according to the capacity and the historical cycle index of each battery pack; the health coefficient and the load coefficient of each battery pack are utilized to calculate the health degree of the dynamic reconfigurable energy storage system; and carrying out life protection on the battery pack according to a preset strategy based on the health degree of the dynamic reconfigurable energy storage system. According to the invention, the accuracy of life evaluation of the dynamic reconfigurable energy storage system is improved, and the adaptability and accuracy of life protection of the battery pack are improved.

The invention discloses a method for evaluating a fatigue weak area of a welded joint based on crystal plasticity, which aims at the problem of fatigue failure of the welded joint caused by complex microstructures such as grain orientation difference and uneven grain size distribution, establishes a quantitative correlation model of grain orientation and size evolution through CPFEM, and combines a macro-micro cross-scale analysis method to evaluate the fatigue weak area of the welded joint. And precise prediction of local stress concentration and crack initiation behaviors of the welding joint under a real fatigue load working condition is realized, so that scientific evaluation of a fatigue weak region is completed. According to the method, an innovative framework of fatigue weak area evaluation is constructed through load working condition reduction and grain characteristic multi-scale coupling, so that excessive simplification of a traditional method on real load conditions is made up, the limitation that a traditional model is difficult to couple a microstructure and a real load is overcome, and the fatigue weakening quantization precision is remarkably improved; and a high-precision analysis technology is provided for anti-fatigue design, process optimization and service life evaluation of the engineering welding structure.

The invention relates to the technical field of gear life evaluation, in particular to a gear contact fatigue life evaluation method and system considering multi-stress coupling. The method comprises the following steps: S1, constructing a gear three-dimensional geometric model according to gear geometric parameters to be evaluated; s2, constructing a gear thermal analysis model based on the gear three-dimensional geometric model; s3, constructing a gear contact analysis model based on the gear thermal analysis model, and calculating to obtain gear tooth surface contact stress; s4, based on a Dang Van multi-axial fatigue criterion, introducing a residual stress correction term, and constructing a gear contact fatigue life evaluation model; and S5, on the basis of the relevant actual working condition data of the gear, calculating the contact fatigue life of the gear according to the gear contact fatigue life evaluation model. Deep fusion evaluation of multiple stresses of the tooth surface contact stress, the thermal stress and the residual stress is realized, and a guidance basis is provided for evaluating and prolonging the service life of the gear.

The invention relates to a digital twinning method, device, medium and equipment for evaluating the service performance of a structural material, and the method comprises the following steps: building a digital geometric model of the structural material, and configuring boundary conditions and loads according to the real service environment of the digital geometric model; collecting multi-source service state data of the structural material in the multi-factor coupling service environment; calibrating and updating the digital geometric model by adopting the state data, and constructing a physical-data fusion digital twinborn model of the structural material; and performing analysis and life prediction on the service state of the structural material based on a machine learning algorithm, and outputting a performance degradation trend and a residual life evaluation result of the structural material. According to the method, digital twinning is applied to service performance evaluation of the structural material, performance prediction and health state evaluation of the material in a complex environment are achieved, the full-life-cycle safety guarantee capacity of the structural material is improved, and the method has important engineering value and strategic significance.

The invention discloses a thermal protection coating accelerated life evaluation method close to a service environment profile. The method comprises the following steps: constructing a damp heat test spectrum for simulating a storehouse storage environment and a combined test spectrum for simulating a hang-off duty environment; determining the equivalent test time of a constant / chord variation damp heat test equivalent to one year of actual storehouse storage in the thermal protection coating life evaluation combined test profile, and carrying out an accelerated test equivalent to the expected service life of the thermal protection coating; and after the test is finished, evaluating whether the service life of the thermal protection coating meets the expected service life according to a comparison result. The method can be used for accurately and effectively evaluating the service life of the thermal protection coating in the environment close to the actual service environment, and can provide method support for material / process screening, service life estimation and model environment adaptability and reliability improvement of the thermal protection coating in the model development stage.

The invention relates to the technical field of aero-engines, and discloses a three-class cyclic stress determination method and system for wheel disc life evaluation, and the method comprises the steps: carrying out the stress finite element simulation analysis of a wheel disc under a transient load, so as to obtain the stress history of a key part of the wheel disc at each time point in the same stress direction and at the same temperature; and constructing a stress inflection point course of the key part of the wheel disc according to stress inflection points in the stress course so as to realize determination and extraction of three types of cyclic peak-valley value stress. The method not only considers the influence of transient load, but also ensures that stress analysis is carried out under a unified direction reference through conversion of the direction cosine matrix, so that stress data and life data at different time points can be compared under the same temperature and stress direction conditions, thereby providing support for effective determination of fatigue damage of the wheel disc, and improving the accuracy of fatigue damage determination of the wheel disc. The situation that in the prior art, the stress direction difference is not considered, so that deviation danger exists when fatigue damage of the wheel disc is analyzed according to three kinds of cyclic stress is avoided.

The invention relates to the technical field of circuit breaker service life evaluation, and particularly discloses a circuit breaker service life evaluation system based on loss analysis. Comprising a circuit breaker operation time division module, a circuit breaker operation data acquisition module, a circuit breaker loss analysis module, a circuit breaker loss monitoring module, a circuit breaker performance analysis module, a circuit breaker service life evaluation module, a circuit breaker service life evaluation correction module and a circuit breaker service life early warning module. According to the method, the running loss and performance data of each monitoring sub-region are collected, the loss change coefficient and the performance evaluation coefficient are obtained through analysis, the service life evaluation index is further obtained comprehensively, and correction is performed by using the correction factor; the loss monitoring module can compare loss change coefficients in real time, screen abnormal data and control the abnormal data; according to the invention, accurate evaluation, real-time regulation and early warning of the service life of the circuit breaker are realized, the operation and maintenance cost is reduced, the operation reliability and economical efficiency of a power system are improved, and powerful support is provided for intelligent management of the power system.

The invention discloses a lithium ion battery outlier early warning method and system, a product, a medium and equipment, and relates to the technical field of battery fault prediction. The method comprises the steps that electrical performance testing is carried out on the lithium ion battery, a life evaluation model comprising three branches is constructed, the life evaluation model is trained, and the three branches comprise a first branch used for local outlier detection, a second branch used for residual analysis and a third branch used for deep learning; the three branches in the life evaluation model process the direct current internal resistance, the constant voltage stage capacity ratio and the constant current stage capacity ratio in parallel, fusion decision is carried out on results obtained by the three branches respectively, and an outlier detection result is obtained. According to the method, the service life attenuation rule of the battery is calculated based on the capacity ratio in the constant-voltage stage, the advantages of actual experiments and model learning can be brought into full play, and accurate recognition and early warning of the lithium ion battery outlier phenomenon are achieved through comprehensive analysis of multi-modal data.

The invention discloses a mold life management system based on vehicle lamp production mold monitoring, which comprises a data acquisition module, a mold health assessment module and an early warning and decision module, and relates to the technical field of mold life management. According to the mold life management system based on vehicle lamp production mold monitoring, data are preprocessed, various characteristics of the temperature change rate, the pressure value, the strain amplitude and the vibration frequency are extracted for analysis and processing, and the extracted characteristic parameters are integrated to establish and form a mold life evaluation model; then real-time data is extracted and introduced into the mold life evaluation model to evaluate the health index of the mold, and the actual wear condition and performance change of the mold can be more accurately mastered by monitoring various physical quantity data of the mold in real time and utilizing an advanced algorithm and model to evaluate the life; and subjectivity and uncertainty of artificial experience judgment are avoided.

The invention discloses an aero-engine structure surface defect intelligent characterization and life evaluation method, and relates to the technical field of aero-engine structure health management. According to the method, defect data are fused and collected through a multi-source sensing technology, and a knowledge graph is constructed; utilizing a multi-branch deep learning model to realize automatic identification of defect types and intelligent extraction of key geometric features; determining a standard defect size based on the statistical distribution model of the group defect data; a defect stress concentration coefficient is rapidly calculated through the trained AI agent model, and mechanical equivalence of irregular defects is achieved; and finally, implanting the equivalent defect into the digital twinborn model of the component, and dynamically simulating and predicting the evolution path and the residual life of the equivalent defect. According to the method, the technical problems that characterization depends on experience, analysis links are isolated, and evaluation is static and conservative are solved, intelligent processing from accurate digital characterization to individualized life prediction of the surface defects of the engine is achieved, and support is provided for condition-based maintenance and predictive health management of the engine.

The invention provides a structural damage evolution digital twinning prediction method applicable to multiple working conditions, and belongs to the field of structural life evaluation. Comprising the following steps: 1) acquiring sensor layout information, recording mechanical response and damage parameters at a t1 moment, and calculating working conditions and increment of the damage parameters; (2) establishing a parameterized finite element model, obtaining a simulation result, extracting mechanical response data of a sensor measuring point at multiple moments and corresponding working conditions and damage parameters, and calculating increments of the working conditions and the damage parameters from the moments t1 to t2; 3) training to obtain a deep neural network, and obtaining a digital twinborn prediction model to predict a damage evolution state; and 4) obtaining a target load history, detecting real-time working conditions and damage parameters, collecting mechanical response of a sensor measuring point, realizing damage parameter increment prediction of a new working condition, and finally realizing damage evolution digital twinning prediction of the new working condition. According to the method, a large number of accurate predictions of the structural damage evolution state under new working conditions are provided, and the structural life evaluation cost can be greatly reduced.

According to the relay service life prediction method based on the multi-dimensional data, by introducing multi-dimensional operation data collection and fusion analysis, the defect that relay micro-sparks and complex coupling disturbance are difficult to accurately capture through a traditional single signal monitoring means is effectively overcome. High-frequency signal components are extracted through variational mode decomposition, high-sensitivity detection of micro-sparks is achieved in combination with micro-arc energy entropy, and the early-stage anomaly recognition capacity is remarkably improved. Through coupling characteristic analysis of a magnetic field distortion sequence, a micro transient coupling index is constructed, dynamic quantification of a multi-source disturbance coupling effect is realized, and a potential failure risk of a relay can be accurately evaluated. And a service life prediction model is constructed by integrating working condition labels and structural parameter empowerment, so that the accuracy and applicability of service life evaluation are enhanced. The overall method improves the comprehensiveness and reliability of relay operation state monitoring, provides a scientific basis for equipment maintenance decision, effectively prolongs the service life of the relay, reduces the maintenance cost, and improves the operation safety of the system.

The invention relates to the technical field of power device simulation, in particular to a simulation system and a simulation method for power distribution automation. The method comprises the following steps: constructing a power distribution device test platform; performing automatic driving operation on the power distribution device to be tested by using the power distribution device test platform, synchronously monitoring a device circuit electromagnetic field, and generating transient excitation electromagnetic field distribution data; acquiring a three-dimensional model of the power distribution device; thermal resistance values among thermal resistance units of the three-dimensional model of the power distribution device are calculated through transient excitation electromagnetic field distribution data, so that a dynamic thermal impedance matrix of the device is constructed; performing internal circulation heat conduction simulation of the device based on the dynamic thermal impedance matrix of the device to generate temperature circulation simulation data of the device; and performing thermal damage value life evaluation according to the device temperature cycle simulation data to obtain power distribution device life evaluation data. According to the invention, through automatic simulation of the temperature response of the power distribution device under the dynamic working condition, the accuracy of power distribution device life evaluation is significantly improved.

The invention relates to the technical field of clutches, and discloses a clutch service life evaluation method and device, a vehicle and a medium, and the clutch service life evaluation method comprises the following steps: establishing a relation curve of a clutch friction material at different working temperatures and the working duration of reaching a set abrasion loss, and performing segmented regression analysis on the relation curve in combination with a set reference temperature; the accuracy of the segmented regression equation near the reference temperature is guaranteed, and then a clutch service life evaluation model with the working time and the working temperature of the clutch as variables is constructed through the segmented regression equation, so that clutch service life evaluation is conducted through the real relation between the working temperature of the friction material and the working duration. According to the method, the accuracy of the evaluation result of the service life of the clutch is guaranteed, unified real-time quantifiable fine evaluation on the service life of the clutch is realized, the wear trend and the health decline state of the clutch friction plate in long-term use can be reflected more accurately, and accurate maintenance suggestions and vehicle use suggestions are conveniently provided for users.

The invention relates to the technical field of control and regulation systems, in particular to a boiler four-tube life evaluation system, which acquires tube wall temperature gradient, water quality parameters and soot blowing operation data through a distributed sensor network, and performs noise reduction and timestamp alignment through a preprocessing unit to generate a multi-dimensional data matrix; the dynamic coupling analysis module integrates a thermal-hydraulic coupling model, an oxidation kinetic equation and an ash deposition model, calculates thermal stress distribution, an oxide skin growth rate and a heat transfer coefficient decrease range, generates a comprehensive damage degree evaluation value according to a preset weight, and pushes the comprehensive damage degree evaluation value to a DCS system for execution through an OPC protocol; and the decision output module generates a customized detection scheme and a causal analysis atlas, and optimizes sensor arrangement in combination with closed-loop verification data. According to the method, multi-dimensional data collaborative analysis, life prediction model dynamic correction and preventive maintenance strategy closed-loop optimization are realized, and the problems of parameter isolation, prediction deviation and high manual dependence in a traditional method are solved.