107 results about "Life span" patented technology

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 multi-dimensional feature fusion-based high-speed optical module life prediction and fault early warning method. The method comprises the steps of constructing a physical-signal double-layer high-frequency feature space containing bottom-layer physical state data and upper-layer signal quality data; a full-life-cycle self-adaptive physical constraint network model is constructed and trained, a degradation stage self-recognition module is arranged in the model, and the weights of physical constraint items such as monotonicity and curvature in a loss function can be dynamically adjusted according to the running-in period, the stable period or the aging acceleration period of the optical module; and finally, performing graded early warning based on the quantized prediction uncertainty. According to the invention, the physical constraint can be adaptively applied according to the life cycle stage of the optical module, the problem of inaccurate prediction caused by poor adaptability of the traditional static constraint in different stages is solved, and the accuracy and robustness of life prediction are remarkably improved.

The invention relates to the technical field of rubber materials, and relates to a rubber multi-stress aging life prediction method based on an Eyring model and a PSO algorithm, and the method comprises the steps of multi-stress aging experiment data collection, single degradation track curve modeling, reference condition and acceleration factor definition, Eyring model establishment and parameter identification, and extrapolation of life under natural use conditions. According to the method, the stability of parameter estimation is remarkably improved by introducing a two-stage parameter identification method, a parameter initial value is obtained through individual curve fitting, then a shape parameter alpha and a position parameter gamma are fixed, a rate parameter beta is identified again, and the parameter standard deviation can be reduced; the ratio of the reaction rates is introduced as an acceleration factor for parameter identification instead of directly identifying the reaction rates, so that the dimensional influence and experimental system errors are effectively eliminated, data of different batches of materials under different experimental conditions have comparability, and the engineering applicability of the model is remarkably improved; the PSO algorithm is adopted to carry out global parameter optimization, so that the prediction precision and the calculation efficiency can be effectively improved.

The invention belongs to the technical field of semiconductor testing, and discloses a WAT test probe card life cycle management method and system. The method comprises the following steps: collecting full-life-cycle multi-source data of a probe card, performing unified management, and constructing a probe card digital twin data model; carrying out change point detection and segmentation on monitoring time sequence data, and constructing a stable fragment library; extracting a unified feature vector including electrical contact, test distribution, space structure and life load; calculating a needle card health index based on a machine learning regression model; predicting the remaining service life and the failure probability by using a proportional risk survival model; according to the health index, the remaining life and the failure probability, generating a maintenance decision suggestion and recommending execution time; the model is updated through maintenance result feedback, and closed-loop optimization is achieved. By means of the mode, accurate evaluation and predictive maintenance of the health state of the probe card are achieved.

The invention discloses a life prediction method, electronic equipment and a readable storage medium, and relates to the technical field of equipment health management, and the method comprises the steps: obtaining a monitoring index curve for a target part, and determining a health index value of the target part according to the monitoring index curve; the health index value serves as an observation value to be input into a Kalman filter, a degradation state estimation value is obtained through output, and the degradation state estimation value is used for representing the degradation degree of the target part; and determining a residual life prediction value of the target component based on the degradation state estimation value and a preset failure threshold value. According to the invention, the robustness of equipment component life prediction is improved.

The invention provides a full-life-cycle data storage method and system for pressure-bearing equipment, and belongs to the technical field of data management. The method comprises the steps that a cross-stage material state record sequence is generated through full-life-cycle multi-source heterogeneous data standardization, and a complete pressure-bearing equipment state data chain is constructed. A material performance dynamic evolution path is tracked based on a time sequence analysis algorithm, high-risk scenes in the material performance dynamic evolution path are screened through a preset deviation degree threshold value, the causal relationship between defects and environmental indexes is analyzed so as to quantify environmental index influence weights, and then influence factor vectors are determined. And determining an influence factor vector set according to the influence vector, thereby establishing a defect growth trajectory prediction model, accurately predicting a defect state and residual life, determining a full life cycle management protocol by taking the residual life as a core, dynamically adjusting monitoring frequency, improving reliability and maintenance efficiency of pressure-bearing equipment, and reducing production safety risk and loss.

The invention relates to the field of electric vacuum devices, and provides a method, a device and equipment for evaluating the service life of a space traveling wave tube, and the method comprises the steps: generating an equivalent comprehensive stress profile according to an on-orbit working profile of the space traveling wave tube; under the action of the equivalent comprehensive stress profile, continuously collecting a life characteristic parameter set; performing data preprocessing on the life characteristic parameter set, performing stable degradation interval identification on the obtained purification parameter set, and determining a starting moment of a stable degradation interval; performing filtering smoothing processing on the purification parameter set from the starting moment, inputting obtained smooth degradation data into a preset machine learning model library, determining an optimal degradation function according to an information criterion, and obtaining a life point estimation value based on the optimal degradation function; and obtaining life interval estimation according to the life point estimation value. According to the invention, the problems of long test period and low accuracy of space traveling wave tube life evaluation in the prior art are solved, and rapid and accurate prediction of the long life of the space traveling wave tube in limited test time is realized.

The invention relates to the technical field of aeronautical materials, in particular to an additive manufacturing metal fatigue life prediction method and system. According to the method, a fatigue life analysis model considering the defect orientation angle, the stress level, the defect roundness and the defect size is established, then a corrected stress-fatigue life model based on the defect geometrical characteristics is established, and a data dimensionless processing mode and life analysis are adopted. According to the correction stress-fatigue life model based on the defect geometrical characteristics, the defect orientation parameters are considered, the formula model is improved, the index of each defect geometrical characteristic parameter is not fixed, the degree of freedom of the model is improved, and the goodness of fit of the model is higher.

The application discloses a precision belt wheel mold life cycle management method and system, and relates to the technical field of mechanical manufacturing, comprising the following steps: in the whole life management process of the precision belt wheel mold, continuously collecting design version change data, processing process record data, running condition waveform data and maintenance disposal track data, and uniformly writing the collected data into a data chain of the same time reference for time sequence management, and generating a time alignment index at the end of the data chain. The application realizes continuous connection and anti-overlapping collection of multi-source data by constructing a data chain with time as the main line and introducing a precursor anchor point, a previous priority constraint and a time sequence traction control, ensures that the deterioration signal is continuously visible in the time dimension, accurately reflects the mold performance attenuation process, and improves the reliability of life prediction and maintenance decision.

The invention is that the muons released in the atmosphere decrease in length until they reach the ground due to their short life span and that this decrease is used to measure altitude, thus measuring the altitude more easily and more safely.

The invention discloses a helium bubble size and density analysis method based on positron annihilation multiple parameters. The helium bubble size and density analysis method comprises the following steps: 1) calculating a low momentum annihilation ratio S and a high momentum annihilation ratio W according to Doppler broadening DBS of a sample to be detected; 2) obtaining the life value of each helium bubble in the sample to be detected according to the life spectrum PALS of the sample to be detected; (3) determining the helium bubbles in the sample to be detected and the types of the helium bubbles according to the service life values of the S, the W and the helium bubbles; 4) comparing the service life value of each helium bubble in the to-be-tested sample with the corresponding service life value calculated by the first principle to obtain the type of the helium bubble in the to-be-tested sample; the radiuses of the helium bubbles of different types are converted into sphere radiuses, and the helium bubble radiuses of the helium bubbles in the sample to be detected are obtained; 5, the matrix life, the helium bubble defect life and the average life of the sample to be measured are obtained according to the life spectrum PALS, and the helium bubble density is obtained through calculation according to the two-state capture model.The helium bubble evaluation robustness is improved.

This invention provides a method for processing streaming data throughout the entire lifecycle of an engineering project, relating to the field of engineering data processing technology. The method includes: constructing permanent spatial anchoring expression units for target structural units during the construction phase of deeply buried structures, generating a set of permanent spatial expressions for concealed components; performing lifecycle flow mapping modeling of the spatial anchoring expression units to generate a set of traceable spatial evolution paths for the concealed phase; performing spatial expression reconstruction and closed-state association processing for the concealed structural phase to generate a continuous field of spatial expression for the concealed structural phase; and performing continuous generation and flow expression modeling of the spatial semantics of streaming data throughout the entire lifecycle of the engineering project to generate a streaming data flow expression model for the entire lifecycle of the engineering project, completing a traceable flow expression of the spatial state of the concealed structural phase. This achieves continuous streaming data flow throughout the entire lifecycle, improves the digital management and safety assurance capabilities of engineering projects, and solves the problem of non-retrospective spatial flow after the closure of deeply buried structures.

The application discloses a kind of underwater vehicle degradation characterization method, belong to underwater vehicle technical field, steps are as follows: the full life cycle data collected by sensor carried on underwater vehicle is collected, data is preprocessed;Feature extraction is carried out to the data after pre-processing;Design a kind of layered cooperation particle swarm optimization algorithm based on fuzzy analytic hierarchy process, the selected feature is obtained by screening the extracted feature, and the final optimal feature combination is obtained;Dynamic correlation mahalanobis distance is used to weight and fuse the selected feature, and the fusion feature is obtained;Using the UMAP dimension reduction method with degradation perception, the degradation process is divided into three stages of early, middle and late according to the full life cycle data, an independent UMAP submodel is trained in each stage, and then the degradation index is fused by transfer learning.This application breaks through the limitation of single signal, realizes multi-component coupling degradation recognition, strengthens early degradation capture, reduces the rate of missed judgment and fault risk, has environmental dynamic adaptability, and adapts to complex deep-sea working conditions.

The application discloses a kind of health state monitoring and life prediction method and system of running machine key components, facing the online monitoring and maintenance decision of product whole life cycle of manufacturer.Method includes: establish factory baseline and initialize degradation model;Online acquisition multi-source signal, extract state characteristic parameter set and calculate current ripple ratio;Equivalent damage cycle value is obtained by constructing unit time damage rate based on speed, gradient and ripple and integrating;Characteristic is normalized and weighted fusion to obtain health index by baseline;Life sample library is constructed and sequentially searched, and when insufficient homologous sample, calibration is carried out, and remaining life mean and confidence interval are simultaneously corrected;According to the convergence degree of confidence interval, output rejection review or maintenance suggestion, and write into full life file.The method of the application can reduce working condition prediction deviation and wrong repair risk, improve maintenance executability and quality traceability ability.

The application provides a track vehicle running part service performance quantitative evaluation method considering life distribution, and belongs to the technical field of track vehicle state evaluation and reliability engineering, and the method comprises the following steps: classifying components according to component failure mechanism; selecting corresponding life distribution models for different categories of components to obtain component time-varying reliability and failure probability; constructing a hierarchical Bayesian network consistent with the running part structure, and combining a variable elimination algorithm to calculate the posterior failure probability of each layer node; based on the component time-varying reliability, the posterior failure probability and the multi-source state monitoring quantity, a component health index with unified dimensions is constructed; the system service performance index is obtained by fusing the component health indexes from bottom to top, and a grading standard is set. The application solves the problems in the prior art, such as the difficulty in realizing unified evaluation of service performance between component and system levels, the insufficient adaptability of life models under different failure mechanisms and the difficulty in quantitative fusion of multi-source state quantities.

Proposed are a lithium secondary battery cathode active material doped with B and Sn and a lithium secondary battery including the cathode active material. The B and Sn doping enables a cathode capable of solving problems occurring in a high-nickel cathode, such as (1) an electrolyte side reaction on the surface, 2) a crystal structure collapse, 3) oxygen release, 4) inert Ni4+ generation; 5) cation mixing, and 6) transition metal elution. This improves the life span of a lithium secondary battery.

The invention provides a part service life prediction method based on multi-scale adaptive characteristic decomposition. The method comprises the following steps: acquiring various signals in the use process of a part; decomposing the plurality of signals into a plurality of components by using an empirical mode decomposition method; decomposing each component into a plurality of sub-components by using a multi-scale sliding window method, wherein the sliding window step length of each component is related to the dominant frequency of the component; generating a target feature sequence meeting time sequence consistency and correlation requirements according to each component and each sub-component; weighting the target feature sequence according to a weight related to an aging stage label and a time sequence evolution gain, and generating a first weighted feature vector sequence representing aging sensitive features; inputting the aging stage label and the first weighted feature vector sequence into a recurrent neural network model to obtain a hidden state vector sequence; and inputting the hidden state vector sequence and the first weighted feature vector sequence into a dynamic regression network fused with Bayesian inference to obtain residual life probability distribution of the part.

The invention relates to the technical field of life cycle management, and discloses a base station optical network life cycle management method and system based on deep learning, and the method comprises the steps: collecting manufacturer information, aging parameter definition, a historical temperature sequence and a temperature sensitivity coefficient of each optical fiber segment of a link; a pre-training language model is used for coding the parameter text to generate a semantic alignment matrix, and manufacturer parameter definition deviation is eliminated; on the basis of a thermal activation aging model, the actual operation time is converted into accumulated accelerated aging equivalent time at the reference temperature; mapping the attenuation data to generate a standardized sequence, and fitting and extracting aged fingerprint features; inputting a Weibull model to calculate residual life distribution and an overall failure probability, and carrying out inverse transformation to identify a link life bottleneck section; according to the invention, the first deteriorated weak section in the link can be accurately identified, and a reliable basis is provided for differentiated maintenance decisions.

The lifespan of flash memory is predicted based on the actual usage profile. Typical use cases are defined. The number of flash memory block writes for each of the typical use cases is collected. Regions of flash memory are defined, wherein each region includes continuous blocks of flash memory and serve a dedicated function in the file system, each functional region are accessed at different frequency, thus region is the smallest and lowest unit where end of life failure of flash part manifests at file system level. During development testing, data is collected to determine a rate at which writes are occurring in each of the regions for each use case and the data is stored in a number-of-writes-per-region-per-use-case table. During use of the electronic device by an end user, occurrences of each of the typical use cases are periodically tracked. An average number of periodical page writes for each of the defined regions of flash memory is calculated. An estimated life span for each region of flash memory is calculated. A flash-file-system-failure date is estimated based on the estimated life span for each region of flash memory. An alert is provided when the flash-file-system-failure date is within a predetermined number of days.

The cathode active material for a lithium secondary battery includes lithium-transition metal composite oxide particles having a crystal grain size of less than 300 nm measured through XRD analysis and an XRD peak intensity ratio of 7% or more. The present invention provides a lithium secondary battery with improved life-span properties and output properties by controlling the crystal grain size and XRD peak intensity ratio of lithium-transition metal composite oxide particles.

This invention provides a dynamic management method and system for multi-level assessment indicators of the resilience of nearshore bridges throughout their entire life cycle, relating to the field of structural resilience assessment and management technology. The method includes: acquiring multi-source heterogeneous maintenance data streams throughout the entire life cycle of the nearshore bridge; processing these data streams through time-series alignment and feature dimensionality reduction mapping to obtain a basic feature matrix of the bridge's service status; and performing conditional routing and data stream processing based on this basic feature matrix to obtain corrosion evolution calculation potential field distribution data, structural dynamic stiffness attenuation tensor, and structural life-cycle degradation confidence distribution vector. This invention accurately obtains resilience indices that truly reflect the bridge's resistance to degradation through multi-step quantification calculations, clarifies the sensitivity differences of assessment nodes, formulates differentiated dynamic maintenance strategies, and constructs a closed-loop management system throughout the entire process, thereby achieving intelligent bridge maintenance, improving maintenance efficiency, and reducing costs.

The invention discloses an OLED module aging life test system, and relates to the technical field of OLEDs, and the system comprises the following steps: carrying out OLED module stress condition division and equivalent stress dose modeling; calculating the equivalent life of the OLED module on different stress paths; performing multi-dimensional quantitative characterization to obtain a color shift coupling degradation index; carrying out life threshold mapping and dynamic life calculation to obtain equivalent residual life; performing degradation path inversion to obtain a mechanism sensitive factor; and constructing an evaluation model, and rating a test result. According to the invention, complete chain type parameter transmission from stress application, optical measurement and degradation quantification to life prediction and mechanism discrimination is realized; the system not only can form a unified degradation evaluation dimension under different voltage, current and temperature conditions, but also can integrate brightness, color cast and life indexes into a multi-dimensional evaluation system, and greatly improves the precision and stability of OLED module life prediction.

The invention discloses a rubber V belt residual life prediction method fusing multi-dimensional features and double attention. The method comprises the steps that multi-source time sequence data such as slip ratio, tension and temperature are collected and preprocessed; extracting local coupling features of multi-sensor signals at the same moment by using one-dimensional convolution, fusing the local coupling features with original features, and capturing long-term time sequence dependence through a long-short-term memory network; introducing a damage increment inversion module based on rubber fatigue physics, and mapping a running state into a physical quantity sequence such as accumulated damage degree; and focusing key stages and states by using a double attention mechanism aiming at the fusion feature sequence, and outputting a life value by a regression layer. According to the method, the problems of poor interpretability and insufficient small sample generalization in the prior art are solved through fusion of a physical mechanism and data driving, and the prediction precision, the generalization ability and the engineering credibility are improved.

The application discloses a kind of construction methods of automobile parts full life cycle digital twinborn model, it is related to digital twinborn technical field, including, life cycle dataset is input initial life digital twinborn model, time domain local feature fusion is carried out in feature extraction layer, degradation trend is captured in time series analysis layer, form the life prediction value of automobile parts, extract actual detection value in life cycle dataset, calculate the deviation of life prediction value and actual detection value of automobile parts, deviation is mapped in time series relationship, generate dynamic causal graph, graph traversal is carried out to dynamic causal graph, obtain key degradation path node, correlation analysis is carried out to key degradation path node using dynamic causal reasoning, generate degradation characteristic parameter, the application is realized by using depth-first search and analytic hierarchy process, dynamic causal correlation accurate modeling of life digital twinborn model, significantly enhance the robustness of life digital twinborn model under actual complex working conditions.

The application discloses a kind of life prediction method and system based on bearing wear state monitoring, comprising the following steps: step S1, obtain the wear state historical time series data of a plurality of bearings representing the entire operating life, and fit out life curve function based on the wear state historical time series data;Step S2, the wear state data of the target bearing to be predicted is input into the life curve function to predict the remaining life of the target bearing, the application uses the wear state historical time series data of a plurality of bearings representing the entire operating life to fit out life curve function, can realize only the wear state data of bearing is obtained to obtain the remaining life of bearing, and based on principal component extraction when the wear state historical time series data is acquired, only the state monitoring data with strong correlation with life is retained, realize the effective compression of data, guarantee the prediction correctness of life curve function while improving the fitting efficiency.

The invention relates to the technical field of constructional engineering, and discloses a method and system for predicting the service life of an anticorrosive coating of a steel structure building, and the method comprises the steps: obtaining multi-source environment data of different parts of the steel structure building, extracting environment fluctuation characteristics through time sequence decomposition and clustering analysis, and carrying out the grouping of similar regions; fitting the relationship between the environmental factors and the aging rate of the coating by using a regression model, and determining an aging acceleration coefficient and a rate estimation value of the specific region; matching historical performance records to generate a preliminary life distribution map; adopting Monte Carlo simulation to generate a multi-scene environment evolution path and a coating degradation track, and determining a comprehensive life prediction interval; dynamically adjusting the prediction parameters based on the comprehensive life prediction interval to obtain an optimized life distribution description; and generating visual mapping, identifying and sorting high-risk parts, and outputting a maintenance decision report. According to the method, the problem that the coating service life prediction precision is insufficient in a complex environment is solved, and accurate and dynamic service life prediction and risk positioning are realized.

The invention specifically discloses a self-adaptive multi-stage residual life prediction method and device for a bearing, and belongs to the technical field of life prediction. The method comprises the following steps: performing time-frequency domain analysis processing on vibration signal data of a full life cycle of a bearing, and dividing the data of the full life cycle into a healthy stage, a slow degradation stage and an accelerated degradation stage; putting the obtained multi-stage data into a dense connection network for supervised learning, and then performing adaptive stage division on an unlabeled data set of a target domain by using a prediction model obtained by training; the method comprises the following steps of: training a basic model by taking health stage data as a source domain, and realizing feature alignment to a target domain through a multi-kernel maximum mean difference (MK-MMD) method; a layered parameter freezing and fine tuning strategy is adopted, and data in the slow degradation stage and the accelerated degradation stage are gradually integrated for model optimization. According to the method, time migration of the model between different degradation stages and space migration of the model to the target domain are considered, and prediction of the whole life cycle RUL under the complex working condition is achieved.

The present application relates to oilfield production engineering technical field, especially a kind of for beam-pumping unit's suspension rope device.Mainly solve the problem that the load of some beam-pumping unit traction rope is large and affects traction rope life-span.Problem.It includes weight reduction device (3), the weight reduction device (3) includes pulley assembly (31), pulley assembly (31) includes main pulley (312) and installation shaft (311), wherein main pulley (312) is connected in the outside of installation shaft (311), and the lower end of installation shaft (311) is connected with intermediate shaft (313) by connecting rod (314);The traction rope (6) is fixed in the inside of main pulley (312) after passing through main pulley (312), and the upper end of wellhead suspension rope device (2) is connected wellhead traction rope, and wellhead traction rope is connected on intermediate shaft (313).The suspension rope device can greatly reduce the load borne by traction rope, thereby prolonging the service life of traction rope, reduce the loss caused by the breakage of traction rope.