529 results about "Life evaluation" patented technology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The invention provides an RV reducer online fault diagnosis method, and belongs to the technical field of reducer fault diagnosis based on computer data processing. The method comprises the following steps: firstly, constructing a fault data set covering multi-modal signals and multiple working conditions, and integrating vibration, current and high-frequency elastic stress wave data; performing segmentation, time-frequency conversion and multi-modal feature fusion on the original time series data to generate a three-channel time-frequency feature map, and strengthening visual expression of fault features; then constructing a domain adversarial attention neural network, and realizing cross-working-condition fault feature migration and accurate classification through adversarial training of a feature extractor, a fault classifier and a domain classifier; and finally, deploying an online diagnosis model, generating a health indicator in combination with the health state baseline model, predicting the remaining service life through a long short-term memory network, and completing fault early warning and life evaluation. According to the method, accurate fault identification and residual service life prediction under complex working conditions are realized, and reliable technical support is provided for the RV speed reducer.

The invention relates to the technical field of spline pair wear life prediction, in particular to a spline pair wear life prediction analysis method based on an Archard model.The spline pair wear life prediction analysis method comprises the steps that key parameters of a spline pair are obtained, then a standard parameter equation of an involute tooth profile is established, and an ideal geometric model of internal and external splines is constructed; manufacturing and assembling errors are introduced to form a three-dimensional geometric model, material parameters and boundary loading conditions are set, a nonlinear contact solving strategy is adopted, contact pressure distribution and relative slippage of a spline pair contact interface under the current calculation step length are obtained, then according to a wear model, a wear depth increment is calculated, and node coordinates are updated; and after each round of iteration, whether the accumulated wear depth reaches a preset limit or not is judged, if the accumulated wear depth does not reach the preset limit, circulation is continued, and if the accumulated wear depth reaches the preset limit, a service life prediction result is output, so that the precision and adaptability of spline pair service life evaluation can be improved.

The invention discloses a water quality monitoring probe fault prediction and life evaluation method and system based on time sequence deep learning, and belongs to the technical field of water quality monitoring. The objective of the invention is to solve the problems of passive maintenance, no performance attenuation quantification, inaccurate service life evaluation and high early warning false alarm of the existing probe. The system obtains and preprocesses multi-dimensional time sequence data including response time, measurement precision and the like, inputs the data to a time sequence feature deep learning module, and extracts depth features by using an LSTM-CNN hybrid network in combination with an attention mechanism; the probe performance attenuation quantification module establishes a baseline according to the initial data, calculates a three-dimensional attenuation index and weights the three-dimensional attenuation index to obtain a comprehensive attenuation index; the Weibull life evaluation module performs modeling according to the data and calculates the residual life, the reliability and the confidence interval; the fault risk scoring module fuses multiple parameters and generates 0-100 score risk scores and four-level early warning and maintenance suggestions; through core algorithm innovation, predictive maintenance is realized, cost is reduced, monitoring continuity is improved, and the method is suitable for full-life-cycle management of the probe.

The invention discloses a safety valve service life evaluation method based on pressure history statistics and dynamic damage accumulation. According to the method, only safety valve inlet pressure time sequence data is needed, a load cycle is extracted through an improved rain flow counting method, and the nonlinear damage increment is calculated according to an S-N curve model dynamically degraded along with the accumulated damage degree. According to the method, a self-adaptive failure probability threshold is established based on historical failure data, and probabilistic prediction of the residual life is realized. The model is clear in physical significance, only needs a single data source, significantly reduces the implementation complexity and cost, greatly improves the precision of life prediction by considering the degradation of material performance, and is especially suitable for predicting the accelerated failure risk of the safety valve in the later period of long-term service.

The invention relates to the technical field of electronic component reliability engineering and environmental engineering, and discloses an electronic component environmental adaptability reliability prediction and life evaluation system and method, and the system comprises an environmental parameter collection module, a reliability prediction module, a life evaluation module, and a data storage and interaction module. According to the invention, a multi-dimensional sensor array including temperature, humidity, vibration, electromagnetic radiation and salt mist concentration is adopted by an environmental parameter acquisition module, multi-physical field stress parameters are synchronously acquired, and common environmental interference factors of electronic components are covered; a Kalman filtering algorithm is built in the data preprocessing unit to suppress environmental noise, the noise suppression ratio is increased by 40%, then abnormal values are eliminated through the 3 sigma criterion, and the data integrity reaches 99% or above; besides, the stress simulation sub-module realizes single / multi-stress coupling simulation through a temperature control cabin, a vibration table and the like, and ensures that the simulation stress steady-state error is less than or equal to 1% in combination with PID feedback control, so that extreme environment data of a laboratory can be supplemented, and the problem of insufficient field data is solved.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.

The invention discloses a dry-type air-core reactor latent defect online diagnosis system and method based on multi-physics field cooperative monitoring. The system comprises a multi-dimensional sensing unit, an intelligent acquisition and triggering unit and a data analysis and intelligent diagnosis unit, the multi-dimensional sensing unit is used for collecting multiple physical quantity signals of the reactor in real time; the intelligent acquisition and trigger unit is used for synchronously acquiring all sensor signals after receiving the trigger signal; and the data analysis and intelligent diagnosis unit is used for early and accurate identification and early warning of latent defects. The system can accurately trigger and capture a switching transient process, through multi-physics-field cooperative monitoring penetration equipment encapsulation, perceive an internal state, and use a deep learning algorithm to construct an intelligent diagnosis model, early and accurate identification and early warning of latent defects such as insulation cracking, turn-to-turn short circuit and the like are realized. And finally, a complete solution integrating monitoring, diagnosis, early warning and life evaluation is formed.

The invention discloses a fatigue damage assessment method, system and equipment for a dangerous part of a blade and a medium, and relates to the technical field of fatigue life assessment of a blade structure.The method comprises the steps that firstly, a coupling correction coefficient obtained based on equivalent stress amplitude is introduced to improve the nonlinear cumulative damage Miner theory; errors caused by insufficient description of the load sequence effect by a traditional linear means are reduced, so that a nonlinear cumulative damage model capable of representing a nonlinear relation between the load sequence effect and cumulative damage is established; then applying a unit equivalent stress amplitude boundary field to obtain an equivalent stress amplitude field, and simulating scalar damage evolution during circulation in a load mode based on isotropic continuous damage mechanics, so that nonlinear effects of thermal-mechanical coupling, stress gradient change and microstructure evolution of the blade are realized; and the coefficients are corrected through sequence coupling, equivalent reflection is carried out in combination with the equivalent stress amplitude or the equivalent stress amplitude field, and the precision of predicting and evaluating the fatigue damage of the dangerous part of the blade is improved.

The invention discloses a piezoelectric ceramic fatigue life analysis method based on macro and micro multi-scale. The method comprises the following steps: firstly, automatically establishing a microcosmic unit cell model based on a Python script, realizing automatic assignment of material parameters and application of periodic boundary conditions, and calculating microcosmic unit cell homogenization mechanical parameters; then constructing a macroscopic driver model, inputting microscopic parameters as material attributes, and performing macroscopic fatigue life prediction and damage distribution simulation in combination with a Miner linear damage accumulation theory; and finally, through macro-micro coupling analysis, transmitting the strain of the macroscopic maximum stress unit to the micro model, and researching the intergranular stress distribution characteristics and the interface damage evolution law of the piezoelectric ceramic. According to the method, a multi-scale modeling strategy is adopted, cross-scale fatigue performance prediction from the micro grain scale to the macro structure scale is achieved, and an effective means is provided for reliability design and service life evaluation of the piezoelectric ceramic driver.

The invention belongs to the technical field of bridge cranes, and provides a bridge crane metal structure fatigue life evaluation method, which comprises the following steps: establishing a bridge crane simulation model through finite element software ANSYS according to a bridge crane structure, and respectively carrying out concentrated analysis on stress under a static working condition and a dynamic working condition to obtain the fatigue life of a bridge crane metal structure. The method comprises the following steps: determining a dangerous stress area, carrying out dominant analysis on stress components of the dangerous stress area under a static condition from a spatial distribution dimension of the stress components, and monitoring a uniaxial stress state of the dangerous stress area under the static condition. Deviation analysis is carried out through the multi-axis stress damage value of the dangerous stress area at the single-multi-axis change time point, the single-axis evaluation error is eliminated through multi-axis stress equivalent correction, and it is ensured that the fatigue life evaluation result is highly consistent with the actual stress damage rule of the structure.

The invention discloses a lithium ion battery calendar life evaluation method, which comprises the following steps of: firstly, obtaining test data based on a calendar life test design of an attenuation factor: setting a plurality of temperature stress gradients and SOC stress gradients to carry out a standing test on a battery, and keeping the battery in a set SOC and temperature state in a constant-temperature environment; regularly taking out in a test period to carry out a recoverable capacity test; then constructing a temperature stress attenuation model and an SOC stress attenuation model: according to temperature stress test data, establishing a temperature attenuation rate function based on an Arrhenius formula to obtain the temperature stress attenuation model; establishing an SOC attenuation rate function based on an inverse power law formula according to the SOC stress test data to obtain an SOC stress attenuation model; and finally, model compounding and life evaluation: compounding the temperature stress attenuation model and the SOC stress attenuation model to obtain a battery calendar life evaluation formula, and substituting the battery calendar life evaluation formula into the temperature, SOC and time of the target working condition to calculate the calendar life attenuation rate of the battery under the working condition.

The invention discloses a method and device for determining the fatigue degree of an inlet and outlet pipeline of a compressor and electronic equipment. The method comprises the steps that structural process parameters, pressure parameters and vibration parameters of the inlet and outlet pipeline of a compressor unit are collected; constructing an alternating stress loading spectrum based on the pressure parameters, performing spectrum analysis on the vibration parameters, and extracting characteristic frequency and amplitude information to construct a vibration test spectrum; a full-wall-thickness circumferential weld sample is prepared according to structural process parameters, and a coupling loading system is built; an alternating stress corresponding to the alternating stress loading spectrum and a multi-frequency vibration load corresponding to the vibration test spectrum are synchronously applied to the circumferential weld sample through the coupling loading system; and multi-source response data of the circumferential weld sample under alternating stress and multi-frequency vibration loads are collected, the fatigue degree of the compressor inlet and outlet pipeline is evaluated based on the multi-source response data, the fatigue damage evolution law of the compressor inlet and outlet pipeline circumferential weld under the real service working condition is obtained, and a data basis is provided for circumferential weld fatigue life evaluation.

The invention relates to the technical field of fatigue tests, and discloses a vibration device for a fatigue test of a passenger ropeway carriage, the vibration device comprises a support control positioning mechanism and a height control positioning mechanism, the middle position of the support control positioning mechanism is connected with a vibration adjustment positioning mechanism, and the support control positioning mechanism comprises a positioning frame; a positioning column is fixedly arranged at the bottom end of the positioning frame, a fixing frame is fixedly arranged at the bottom end of the positioning column, and a plurality of supporting legs are fixedly arranged at the outer end of the fixing frame. The vibration mechanism can move along a square track, so that reciprocating linear vibration or rotating circular vibration is realized; due to the multidirectional vibration capability, the test working condition is closer to the actual operation scene, and the accuracy of fatigue life evaluation is improved; the vibration mechanism supports multidirectional movement and can simulate complex vibration working conditions caused by passenger walking, goods unbalance loading and the like, and the working condition coverage of the test is remarkably improved.

The invention provides a prediction method based on the wear state of a steel-based ceramic wear-resistant component, and relates to the technical field of material life evaluation, and the prediction method comprises the following steps: transmitting an excitation eddy current containing a first frequency and a second frequency to a steel substrate, and receiving a response signal modulated by the state of a steel substrate and ceramic-steel bonding interface; processing the response signals corresponding to the first frequency and the second frequency, and extracting a first phase variation of the first frequency relative to a first reference signal corresponding to the initial health state of the component and a second phase variation of a second reference signal corresponding to the second frequency; the difference quantity between the first phase change quantity and the second phase change quantity is calculated, and the heat conduction state change of the ceramic-steel bonding interface is represented; and acquiring the real-time working temperature of the wear-resistant part, evaluating the current state grade of the ceramic-steel bonding interface based on the real-time working temperature and the difference quantity, and predicting the residual safe service time of the ceramic wear-resistant layer in combination with the change trend of the difference quantity along with time.