182 results about "Life span" patented technology

The invention discloses a digital twinborn model construction method, a life prediction method, a model construction device and equipment of a crane, and relates to the technical field of industrial Internet of Things, the method comprises the following steps: constructing a multi-dimensional digital twinborn model, the multi-dimensional digital twinborn model comprises a physical entity layer, a connection layer, a virtual model layer, a twinborn data layer and a service function layer, the virtual model layer comprises a target twin model, and the service function layer corrects key components of the crane; transmitting the physical parameters of the crane to a connection layer for preprocessing to obtain a physical data set; the physical data set is transmitted to a virtual model layer, and an initial twin model is obtained through kinetic analysis and knowledge graph processing; and transmitting the virtual data set to a twin data layer, calculating target correction parameters according to the physical data set and the virtual data set, and correcting model parameters of the initial twin model to obtain a target twin model. According to the method, the problem of insufficient physical mapping precision of the existing virtual model is solved.

The invention discloses a charging pile part life cycle prediction and management method, and relates to the technical field of charging piles, and the method comprises the steps: constructing a multi-source data collection system, and obtaining the operation state, environment parameters and user behavior data; key characteristic variables are extracted through principal component analysis, and health state evaluation indexes are generated through a coupling aging model; an improved Transform prediction network is designed to realize accurate prediction of the residual life; and making a dynamic operation and maintenance strategy based on a prediction result and optimizing a maintenance period. The prediction precision can be improved through the environment adaptation factor and the nonlinear mapping layer, the maintenance cost is reduced, and the equipment availability rate is improved.

The invention relates to a bearing life prediction method and system fusing depth separable cavity convolution and an attention mechanism, and the method comprises the steps: firstly collecting the whole life cycle vibration signal of a bearing, and constructing a training sample through standardization processing and sliding window reconstruction; secondly, establishing a DSDC-SE-BiGRU-ATT prediction model which adopts depth separable cavity convolution to enlarge a receptive field while reducing a parameter quantity, adaptively calibrating a feature channel weight through an SE channel attention mechanism, capturing bidirectional long-term time sequence dependence by using a BiGRU network, and introducing a zoom dot product attention mechanism to focus a key degradation stage; and finally, taking a mean square error as a loss function, adopting an Adam optimizer to train the model, inputting online monitoring data into the trained model, and outputting a life prediction value. The problems that in an existing method, calculation complexity is high, long-time-sequence feature capture is insufficient, and noise interference is serious are effectively solved, and the precision and efficiency of bearing service life prediction are remarkably improved.

The invention discloses a ship engine component cross-working-condition life prediction method and system, and the method comprises the steps: collecting the operation data of key components of a ship engine in real time, and carrying out the preprocessing of the operation data of each component, so as to guarantee the data synchronism and data validity of the operation data; inputting the preprocessed operation data into the corresponding trained life prediction network model, and outputting a life prediction result corresponding to each component; the life prediction network model comprises a feature extraction module used for extracting time sequence features under different scales, splicing all the time sequence features on a feature dimension, performing feature enhancement on a splicing result based on a Transform model, and outputting a global feature vector after compression of a global average pooling layer; the life prediction module is used for outputting a life prediction result according to the global feature vector; the domain discrimination module is used for outputting a domain discrimination result according to the global feature vector; in training, the feature extraction module and the domain discrimination module carry out adversarial training.

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 relates to a supply chain management method, in particular to a circulation box full life cycle management method and system based on a block chain and a program product. According to the method, logistics event information is collected and layered splitting is carried out, three parallel block chains including a logistics main chain, a carbon evaluation side chain and a prediction side chain are constructed, cross-chain atomic writing is realized in combination with two-stage submission and BLS aggregation signature, the residual life and path of a circulation box are predicted by using a digital twin model, and a carbon quota is dynamically allocated through deep reinforcement learning, so that the residual life and path of the circulation box are predicted. And finally, the position information privacy is protected by means of zero-knowledge proof. The circulation efficiency of the circulation box, the carbon emission reduction transparency and the system safety are remarkably improved.

The invention relates to the technical field of fan blade life prediction, and discloses a large fan blade life prediction method based on a simulation model. The method comprises the following steps: acquiring a stress-strain data stream and an operating environment data stream of a blade, and analyzing the stress-strain data stream by means of a preset life constraint index to obtain a blade life distribution characteristic; combining a prediction decision rule base and a life fitness condition, integrating the life distribution characteristics and the operation environment data flow to generate a preliminary life prediction scheme, and pre-judging the leaf life distribution characteristics and environment data change by adopting a trend analysis technology to obtain predicted life distribution characteristics and predicted environment data; and optimizing and correcting the initial scheme according to the prediction data to form an optimized life prediction scheme, thereby realizing the life prediction of the large-scale fan blade. According to the method, multi-source data are integrated, trend analysis and scheme optimization are combined, prediction reasonability and accuracy are improved, and reference is provided for blade operation and maintenance.

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 method for predicting the residual life of a reinforced concrete bridge based on monitoring data, and belongs to the technical field of bridge life prediction. The method comprises the steps of obtaining a residual life prediction result of a target bridge before reinforcement, and obtaining a reinforcement scheme and a predicted life prolonging effect adopted when the target bridge is reinforced; determining a plurality of to-be-monitored key parameter types through the reinforcement scheme, and generating a corresponding monitoring scheme according to the plurality of key parameter types for deployment of monitoring equipment to complete acquisition of key parameters; and acquiring a key parameter standard value corresponding to the predicted life prolonging effect during numerical simulation, performing numerical comparison on the key parameter standard value and a plurality of acquired key parameters, correcting the predicted life prolonging effect according to a comparison result, and prolonging a residual life prediction result according to a correction result to obtain the residual life of the reinforced concrete bridge. The method can reduce the dependence on the finite element model during prediction of the residual life of the reinforced concrete bridge.

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.

A tool life prediction system capable of predicting the life of a tool with higher accuracy is provided. The tool life prediction system (1) includes: a machine tool main body (11) that performs machining of a workpiece (W) using a tool (T); a detector (13) that detects observable state data in the machine tool main body while machining the workpiece; a learned model storage unit (81) that stores, for each of a plurality of life cases, a learned model generated by performing machine learning using a training data set including explanatory variables of the state data detected by the detector and a target variable of the number of remaining machining of the workpiece before reaching the life in one tool; and a remaining machining number prediction unit (83) that selects one learned model from the plurality of learned models based on the state data and predicts the number of remaining machining using the selected one learned model and the state data.

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.

A method of estimating a remaining life span of a driver for driving a load, wherein said driver comprises a power stage arranged for receiving an input voltage and for providing an output to said load, wherein said method comprises the steps of receiving, by a state observer model, a temperature value related to said driver, and receiving a loss value related to a power loss of said driver, determining, by said state observer model, estimated internal states representing component temperature dynamics based on said received temperature value and said received loss value making use of an observer model for mimicking a thermal behavior of said driver, providing, by said state observer model, estimated temperatures of a plurality of electrical components in said driver based on said estimated internal states representing component temperature dynamics using a component matrix, and estimating, by a life span model, said remaining life span of said driver based on said estimated temperatures.

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 discloses a power equipment asset management method and system based on an asset wall, relates to the technical field of the asset wall, and comprises the following steps: collecting equipment full-life cycle data, and modeling a life distribution based on a logarithmic normal; establishing an initial asset wall based on the life distribution; acquiring a first extrapolation window and deriving a plurality of second extrapolation windows; extrapolating future retirement and updating quantities based on the second extrapolation windows and the life distribution; performing data analysis based on the extrapolation results, evaluating and screening the second extrapolation windows, and generating a third extrapolation window; and extrapolating based on the third extrapolation window and performing asset management. The application solves the problem that the prediction window in the prior art is difficult to balance short-term precision and long-term coverage.

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.