248 results about "Life span" patented technology

The invention relates to the technical field of distributed resource management and optimization, in particular to a distributed resource life cycle evaluation and dynamic updating method, which comprises the following steps: data acquisition: collecting multi-modal data of distributed resources, including resource state data, use behavior data, environment data and historical maintenance data; feature extraction: carrying out feature engineering processing on the multi-modal data, and extracting key indexes such as a resource health state, a degradation rate and environmental sensitivity; building a life cycle evaluation model, building the model by using the time sequence data of the resources based on a multi-task learning framework, and predicting the health state and the residual life of the resources; and optimizing a dynamic updating strategy, and monitoring and dynamically adjusting in real time. The method provided by the invention overcomes the problems of insufficient life cycle dynamics and rigid updating strategy in a traditional method, has the characteristics of high efficiency, intelligence and accuracy, and is suitable for intelligent management and optimization of new energy equipment, industrial assets and other distributed resources.

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 concrete bridge reinforcement carbon emission calculation method based on a fiber reinforced composite material, and the method comprises the steps: obtaining the carbon emission data and material attribute data of the fiber reinforced composite material, i.e., an FRP material, in a whole life cycle, and building an FRP material database; establishing a bridge building reinforcement information model according to the design model of the concrete bridge and the acquired bridge data, performing static data-driven open-loop semi-entity simulation on the bridge building reinforcement information model, and continuously optimizing and updating the selected FRP material; constructing a carbon emission calculation model of the full life cycle of the bridge reinforcement project based on the FRP material database; and determining the total carbon emission of the concrete bridge reinforcement construction based on a carbon emission calculation model and the optimally selected FRP material. According to the method, the carbon emission of the FRP material in the bridge reinforcement process can be accurately evaluated, the material selection is optimized, the influence on the environment is reduced, the sustainable development is promoted, and the scientificity and accuracy of decision making are improved.

The invention provides a humanoid robot joint life monitoring method, and belongs to the technical field of life monitoring. The method comprises the steps that 1, a multi-mode sensor array is constructed to collect original joint motion data of a target robot; 2, standardizing the original joint movement data according to feature types to obtain standard feature data of all parameter types; 3, establishing a joint degradation model of the target robot based on the standard feature data; 4, inputting real-time motion data of the target robot into the joint degradation model, and outputting a joint residual life index; and 5, when the residual life index of the joint is lower than a preset threshold value, a self-adaptive maintenance strategy is triggered. Through real-time monitoring of joint health, early warning and optimization maintenance, the reliability of the robot is improved.

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 relates to the technical field of data processing, in particular to a device and a system for predicting the service life of an automobile universal joint, and aims to realize personalized prediction of the service life of the universal joint according to different driving habits of users. The device comprises a driving style analysis model which analyzes driving habits in combination with historical data and identifies the driving style of a user; the personalized service life prediction module is used for constructing a personalized service life prediction model and adaptively selecting the service life prediction model according to the driving style to perform service life prediction on the universal joint; wherein the personalized life prediction model comprises a first prediction model, a second prediction model and a third prediction model; and the abnormal mode detection module is used for monitoring the running state of the universal joint, optimizing the personalized life prediction model when an abnormal condition is detected, and dynamically correcting a life prediction result.

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 discloses a service life prediction system and an air conditioning system, and the system comprises a data collection module which is used for collecting the operation data of a to-be-predicted device; the data analysis module is used for calculating the life loss of the to-be-predicted device according to the operation data; and the life prediction module is used for estimating the residual life of the to-be-predicted device according to the life loss. Compared with the prior art, the service life of the to-be-predicted device can be accurately predicted according to the environment parameters and the operation parameters of the to-be-predicted device, so that the to-be-predicted device is maintained and replaced in advance, the service life of the whole system is effectively prolonged, and fault risks and resource waste are reduced.

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 relates to the technical field of engineering management, in particular to a steel structure engineering full-life-cycle management method and system based on digital twinning, which can comprehensively and accurately grasp the condition of a steel structure in the full life cycle, and can monitor the dynamic evolution of the connection condition, the shape change and the design standard of the steel structure in real time. According to the method, potential safety hazards can be found in time and early warning can be carried out in advance, dynamic human-structure interaction and surface damage caused by equipment use are fully considered in aging analysis, the aging process of the steel structure can be predicted more accurately in combination with a quantitative model of environmental factors, and meanwhile, the accuracy of aging analysis is improved. According to the method, the data acquisition period can be dynamically adjusted according to safety risk conditions of different stages, resource waste is avoided, flexibility and pertinence of data acquisition are achieved, the scientific, comprehensive and efficient management method greatly improves the quality and efficiency of steel structure engineering management, and safe and stable operation of a steel structure in the whole life cycle is powerfully guaranteed.

The invention relates to a bearing life prediction method based on deep learning of CBAM attention. The method comprises the steps of collecting a to-be-predicted bearing vibration signal; inputting the to-be-predicted bearing vibration signal into a preset bearing life prediction model, and predicting the service life of the bearing; wherein the bearing life prediction model is a CBAM attention mechanism-based parallel time convolutional network and long and short term memory network model; the training set comprises the bearing vibration signals of the whole life cycle of the normal operation stage and the fault stage of the bearing. According to the technical scheme, richer features can be extracted, and the service life of the bearing can be predicted more accurately and reliably.

The invention discloses a life bearing capacity degradation prediction method for a port and wharf caisson structure, which comprises a data acquisition module, a coupling analysis processor and a dynamic early warning output module which are arranged, and is characterized in that the data acquisition module comprises a machine vision unit, a distributed optical fiber sensing unit and a parameterized seismic oscillation input module which are arranged; through cooperative work of multiple modules, accurate prediction of the caisson structure is realized. According to the life bearing capacity degradation prediction method of the port and wharf caisson structure, machine vision and optical fiber sensing are combined, surface-interior synchronous three-dimensional quantification of caisson corrosion damage is achieved, the problem of data discreteness of a traditional method is solved, physical mechanism constraints are provided through a finite element model, model errors are corrected through machine learning, and the service life bearing capacity degradation prediction method is achieved. The bearing capacity prediction precision under near-fault pulse type seismic oscillation is improved, a lightweight parallel computing architecture is designed, dynamic coupling calculation of corrosion monitoring data and seismic oscillation input is supported, and minute-level bearing capacity threshold value updating is achieved.

The invention discloses application of rapamycin combined with a vascular endothelial growth factor A in improvement of an ovarian tissue function. The improvement of the ovarian tissue function comprises any one or more than two of the following aspects: increasing the survival rate of follicles, prolonging the functional life of the ovarian tissue and improving the quality of oocytes. The effective concentration of the rapamycin is 500nM, and the effective concentration of the vascular endothelial growth factor A is 5ng / mL. The follicle loss dynamic after OTCT is tracked through a mouse model, it is found that rapid follicle loss is related to tip cell damage and primordial follicle activation, then a rapamycin and vascular endothelial growth factor A combination strategy is provided, follicle loss can be remarkably reduced, the reproductive function period can be prolonged, and the oocyte quality can be improved; according to the invention, a systematic solution is provided for OTCT technology optimization.

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 invention belongs to the technical field of data processing, and particularly relates to a flexible film production equipment operation state monitoring system based on big data, and the system comprises a data obtaining module which is used for obtaining an operation data sequence of a part and a corresponding production data sequence and environment data sequence; the component life change model and life index construction module is used for acquiring the life change model and life index of each component; the running state model building module is used for acquiring the stability of each related sequence of the part at each moment, acquiring the linear relation between all related sequences of any type of part and the type of part at each moment, and building a running state model of each type of part; and the operation state monitoring module is used for completing state monitoring of each operation part of the flexible film production equipment. According to the invention, the predictability and interpretability of the operation state monitoring of the flexible film production equipment are improved, and the production efficiency of the flexible film is improved.

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.

The present invention discloses a remaining useful life prediction method for a hidden Wiener degradation process integrating multi-source information, comprising the steps of: A. establishing a hidden linear Wiener process degradation model; B. estimating offline parameters: (1) for the case where the prior degradation information is imperfect, integrating the failure life data to estimate the prior drift parameter of the model; (2) for the case where the prior degradation information is scarce, integrating the failure life data and the historical degradation data to estimate the prior drift parameter of the model; C. online updating the parameters; D. predicting the remaining useful life. The present invention can not only overcome the problems of imperfect or scarce prior degradation information, predict and analyze the individual life and overall reliability life characteristic quantities of the equipment, but also serve as an effective analysis tool for predicting the remaining useful life of the equipment, providing a strong theoretical basis and technical support for the condition-based maintenance and support of the equipment, thereby saving the expenditure and avoiding unnecessary economic losses, and having good engineering application value.

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.