161 results about "Reliability model" patented technology

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention belongs to the technical field of wind turbine generator maintenance, and particularly relates to an offshore wind turbine generator two-stage rolling maintenance method considering subsystem correlation, which comprises the following steps: S1, constructing a comprehensive reliability model; s2, taking the minimization of the total maintenance cost in the operation cycle of the wind turbine generator as a target, and forming a target optimization problem based on the comprehensive reliability model; solving the target optimization problem to form a first-stage long-term maintenance strategy; s3, when an operation window meeting the storm accessibility condition appears, determining a subsystem of which the current reliability is lower than a preventive maintenance threshold value; s4, the opportunity maintenance benefit ratio is calculated, and if the opportunity maintenance benefit ratio is larger than a processing threshold value, advanced maintenance of the subsystem is triggered; and S5, after the advanced maintenance is executed each time, the optimal preventive maintenance time point is solved again so as to dynamically update the subsequent maintenance plan. According to the method, the fault coupling and degradation evolution law between subsystems can be accurately described, and the storm accessibility window is actively utilized to dynamically adjust the maintenance plan.

The invention relates to the technical field of equal-strength design, and provides a high-reliability equal-strength design method and system for an intelligent cubic press, which considers structural characteristics of the cubic press, performs physical field analysis, modeling and simulation, determines deformation, temperature, stress distribution and stress concentration areas of key components, predicts structural crack initiation of the key components, and provides a high-reliability equal-strength design method and system for an intelligent cubic press. Crack propagation simulation is carried out; establishing a fatigue strength life prediction model based on deep learning and a simulation data set; constructing a fatigue strength reliability model, and establishing a mathematical association relationship among fatigue life, fatigue damage and reliability; a reliability calculation index is introduced into multi-objective optimization of the structure, a high-precision numerical solution of reliability robustness optimization design based on structure simulation is developed, and equal-strength design of the cubic press is achieved. According to the method, the optimal combination of design parameters can be obtained, the equal-strength design of parts and the whole machine structure is formed, and development of intelligent, high-precision and large-scale cubic press equipment is promoted.

The invention relates to the technical field of parameter extraction, and provides a reliability parameter extraction method based on a Lifetime equation and parameter mapping, and the method comprises the steps: S1, building a Lifetime degradation formula, and obtaining a Lifetime model capable of describing the degradation trend of a device based on a physical mechanism of the device; s2, judging whether a user has a parameter mapping formula or not, if so, extracting degradation parameters through an automatic reliability parameter extraction tool and a customized reliability model interface, and entering step S5; s3, if the user does not have the parameter mapping formula, collecting degradation data and extracting a parameter degradation curve through an automatic parameter extraction tool; s4, selecting a machine learning model to obtain a parameter mapping relationship, fusing physical constraints, taking a known physical relationship as an initial item input model, obtaining a reliability formula, and then entering the step S2; and S5, extracting a formula and verifying the formula, evaluating prediction precision in a test set, and performing extrapolation verification under a stress condition outside a training data range. The parameter degradation modeling time can be shortened, and the automatic parameter extraction efficiency can be improved.

The invention discloses a reliability-based titanium alloy medium plate welding optimization control system and method for a new energy vehicle, and the system comprises a data obtaining and preprocessing module, a performance prediction module, a reliability evaluation module and a process parameter optimization module, welding process parameters and the tensile strength of a welding joint, the welding width of the bottom surface of a welding seam and angular deformation under the corresponding welding process parameters are recorded; the performance prediction module is used for training and learning the sample data acquired in the data acquisition module, the trained performance prediction module is used for prediction, and the weight of an RBF neural network and the initial value of a Gaussian function parameter are optimized based on an improved genetic algorithm in the module; the reliability evaluation module is used for establishing a welding non-probabilistic reliability model based on an ellipsoid convex model and considering the tensile strength of a welding joint, the weld penetration depth and deformation, and evaluating the rationality of the current process parameter group by comparing the reliability obtained through calculation with a reliability threshold value; the process parameter optimization module is used for constructing a fitness function about the tensile strength of a welding joint, the weld width of the bottom surface of a welding seam and angular deformation by adopting a weight method, and optimizing an optimal process parameter group based on a particle swarm optimization algorithm of simulated annealing; according to the invention, efficient and reliable welding process optimization can be realized, the reliability and the performance stability of a welding joint are ensured, and the experiment trial and error cost is reduced.

The invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The invention discloses a multi-source data fusion aerospace electronic system reliability evaluation method based on design and manufacturing process constraints, and belongs to the technical field of electronic system reliability evaluation. The problem that the reliability evaluation accuracy of the spaceflight electronic system is poor under the condition of incomplete information is solved. The method comprises the following steps: firstly, establishing a digital prototype model of the aerospace electronic system, and establishing a parameter degradation model of key components of the electronic system in combination with a failure physical model so as to obtain a performance degradation reliability function of each key electrical performance parameter of the electronic system; establishing an electronic system function failure propagation network model based on a logical relationship between an electronic system function structure and basic function network nodes, performing state updating on each function network node of the electronic system by adopting a Bayesian inference method, and obtaining a function failure reliability function of each function network node of the electronic system; and then based on the two reliability functions, constructing a system-level reliability model to obtain an overall reliability curve of the electronic system.

The invention relates to a numerical control machine tool reliability modeling method and system considering a fault trend. The method comprises the following steps: checking the homogeneity of a numerical control machine tool based on the fault trend; constructing an AMSAA model of the multiple samples subjected to truncation at unequal time; evaluating the precision of the reliability model; aiming at the problem of insufficient reliability model precision of a multi-sample condition of a numerical control machine tool, a multi-sample AMSAA model modeling method of the numerical control machine tool considering a fault trend is provided, sample data classification is carried out through trend inspection and homotype inspection, and fault data of same-type machine tools are preprocessed by adopting a fault total time method; adopting a maximum likelihood method to estimate AMSAA model parameters of the same-type machine tool, and using Cramer-Von Mises to test the goodness of fit of the model; evaluating the precision of the reliability model by taking an average absolute percentage error (MAPE) of an instantaneous MTBF point estimation value and an MTBF observation value as an index; compared with a multi-sample AMSAA model established by a direct maximum likelihood method, the method is higher in prediction precision.

The present invention provides a dual-redundant servo valve and its application, belonging to the field of electro-hydraulic servo control; comprising a housing, two sets of electro-hydraulic servo valves and a hydraulic pressure sensor; the two sets of electro-hydraulic servo valves are connected in parallel and arranged in the housing, respectively defined as a left valve and a right valve; the hydraulic pressure of the left valve is monitored by the hydraulic pressure sensor; the left valve is set as the main valve, and the right valve is set as the backup valve; when the right valve is a hot backup, the two sets of electro-hydraulic servo valves are started at the same time to control the supply pressure, and if one of them fails, the other can independently complete the control target; when the right valve is a cold backup, the backup valve is started only when the main valve fails to complete the control target. The dual-redundant servo valve proposed in the present invention is composed of a pair of twin electro-hydraulic servo valves on a common housing. The reliability model of the left valve and the right valve of the valve system is a parallel relationship. As long as one of the electro-hydraulic servo valves is normal, the dual-redundant servo valve can complete the regulation and control of the output pressure.

The application relates to a failure prediction maintenance method and device with a preventable strategy, and relates to the field of equipment prediction and maintenance technology. In view of the problems in the prior art that a traditional two-state reliability model is difficult to accurately describe the degradation degree, failure condition and maintenance state of a system in a service life, the calculation process is relatively single, and the result error is relatively large, the technical scheme is as follows: the failure prediction maintenance method with the preventable strategy comprises the following steps: collecting a probability distribution obeyed by the residual life of a system; collecting maintenance time and maintenance cost; establishing an optimization model; iteratively processing the model to obtain an optimal failure prediction interval and a residual life threshold value; when the optimal failure prediction interval is reached, the residual life of the system is predicted, the residual life is compared with the threshold value, and if the residual life is lower than the threshold value, the system is subjected to corrective maintenance, and if the residual life is higher than the threshold value, the system is subjected to preventive maintenance. The application is suitable for application in preventive maintenance work, and establishes a new research basis for the research of preventive maintenance work.

This invention provides a high-end scientific instrument reliability analysis and design method based on SysML, belonging to the field of instrument reliability analysis technology. The method includes: constructing a system model of the target instrument using SysML, the system model including a requirements diagram, internal block diagram, and attribute library; extracting all failure modes from all failure mode libraries and assigning them global numbers to establish a failure mode mapping matrix; performing failure mode and impact analysis (FMEA) for the target instrument based on the system model to obtain a failure mode and impact analysis table; establishing a parameterized reliability model for each functional path; calculating the reliability constraints of each functional path based on the system model and the impact analysis table; and performing reliability analysis based on the measured attributes and reliability constraints of sub-components to generate an optimization scheme. The advantages of this invention are improved accuracy, consistency, and automation of reliability analysis.

The invention discloses an underwater vehicle control system competitive failure reliability evaluation method, belongs to the technical field of underwater vehicle reliability evaluation, and is used for evaluating the underwater vehicle control system competitive failure reliability and preprocessing multi-source monitoring of an underwater vehicle control system. Constructing an underwater vehicle control system reliability model; constructing a non-time-delay parameter and a time-delay related parameter, and calculating estimated values of the non-time-delay parameter and the time-delay related parameter by using a least square method, a statistical inference method and a time-delay structure identification method; and introducing an alternating iterative optimization mechanism to carry out parameter estimation, and calculating the reliability of the underwater vehicle control system. According to the method, accurate modeling and reliability evaluation of the competitive failure degradation process and the impact coupling effect of the underwater vehicle control system are realized by constructing a dynamic adaptive feature extraction model and a time-delay related degradation theory and adopting a parameter estimation and alternate iteration optimization strategy.

A closed-loop selective attention system for resolving conflicts in multi-source or multi-speaker environments, including a plurality of internal attention models, each outputting a probability distribution over candidate sources and an associated confidence score, a fuser detecting conflicts when two or more of said attention models output high-confidence predictions that disagree, a selective sampling policy querying one or more external agents, wherein each external agent possesses a knowledge base, a reliability model, and a communication protocol, a trust and reliability module assigning and updating dynamic trust scores for internal and external agents based on past performance, an efficiency optimizer minimizing communication overhead and decision delay by balancing token usage cost and latency cost, and a dynamical system formulator ensuring convergence of the conflict resolution process under bounded trust, decaying step size, and limited sampling.

The present invention discloses a method and system for allocating production and maintenance coupled tasks that considers the operating status of equipment. The system includes: a digital twin platform that simulates the allocation scheme obtained by the task allocation decision module to verify its feasibility; an equipment model management module that establishes a reliability model for the status of the equipment and establishes a numerical model for the production process of the equipment; an order receiving module that decomposes the order into production tasks for each equipment; a maintenance strategy management module that manages the maintenance strategy and selects the maintenance strategy corresponding to the equipment based on the current equipment reliability; a task allocation decision module that unifies the planning of production tasks and maintenance tasks and provides an allocation scheme; and an allocation result interaction module that outputs the final allocation scheme and interacts with the physical production line to provide production guidance for the physical production line. The present invention solves the problem of conflict between production and maintenance in the use of time resources, and achieves simultaneous and efficient production and maintenance.

The invention discloses an open source big data system software reliability model with fault removal efficiency obeying Weibull distribution, and belongs to the technical field of software reliability analysis. Performance fitting and prediction experiments are carried out through eight fault data sets and nine software reliability models, 95% confidence interval analysis is carried out, and results show that the model provided by the invention has better fault fitting and fault prediction performance than other models. The model provided by the invention can effectively and accurately evaluate the reliability of the open source big data system software, and has certain guiding significance for actual software development and testing.

The invention provides an unmanned system cluster task importance analysis method based on task reliability. The method comprises the following steps: constructing a task scene through unmanned system cluster constituent elements; based on the task scene, performing task reliability calculation according to the unmanned system cluster constituent elements to obtain an unmanned system cluster task reliability model; performing importance calculation on the unmanned system cluster task reliability model through an equipment state and a cluster killing chain to obtain an unmanned system cluster task importance analysis model; and performing maintenance optimization sorting according to the unmanned system cluster task reliability model and the unmanned system cluster task importance analysis model to obtain an unmanned system cluster optimization strategy. According to the method, reliability and importance analysis is carried out on the unmanned system cluster, and key task node identification and maintenance priority ranking are realized, so that the task execution flexibility, robustness and cost effectiveness of the unmanned system are remarkably improved.

The application discloses a kind of dynamic multi-energy coupling and conversion comprehensive energy system reliability evaluation method.Establish the multi-state reliability model of multi-energy production element considering dynamic multi-energy coupling, establish the multi-state reliability model of multi-energy conversion element considering dynamic multi-energy conversion, establish the multi-state reliability model of multi-energy transmission element considering dynamic multi-energy coupling, according to the multi-state reliability model of three kinds of elements, the multi-state reliability model of three kinds of subsystems is constructed;According to the multi-state reliability model of three kinds of subsystems, the reliability of comprehensive energy system considering multi-energy coupling and conversion is obtained by using improved general production function method, and reliability evaluation is realized.The dynamic characteristics of the comprehensive energy system can be more accurately characterized by using the multi-state model, and the reliability of the comprehensive energy system that meets the accuracy requirements is accurately calculated.

The application discloses a kind of multi-energy coupling and conversion comprehensive energy system multi-state reliability evaluation method.Establish the multi-state reliability model of multi-energy conversion element considering multi-energy conversion, using clustering algorithm reliability aggregation, and mapping formation aggregated multi-state reliability model;Multi-state reliability model of multi-energy coupling element considering multi-energy coupling is established, and then using Gaussian approximation and sampling approximation reliability approximation processing in sequence obtains multi-state approximate reliability model;The aggregated multi-state reliability model and multi-state approximate reliability model are processed according to the fast reliability evaluation method to obtain the reliability of the comprehensive energy system, and the reliability evaluation is realized.The application improves the traditional reliability evaluation method from time and accuracy, reduces the calculation time, and quickly and effectively realizes the estimation of system reliability.

This invention discloses a method for generating operation and maintenance schemes for deep-water explosive containers based on generative adversarial networks (CGANs), relating to the field of deep-water explosive container safety operation and maintenance technology. Based on the maximum strain prediction value output by the optimal GRU strain prediction model, the fuzziness of material performance parameters is first processed using a fuzzy interval reliability model to obtain a reliability interval. Then, using this reliability interval as input, a random interval hybrid reliability model is combined to fuse the randomness of the predicted strain, calculating a reliability index η used to quantify the container's safety status. This invention generates virtual data that conforms to the real distribution and has physical rationality through conditional generative adversarial networks (CGANs), effectively compensating for the lack of historical data under extreme conditions such as high explosive dosage and high water pressure, enhancing the generalization ability of data-driven models, and significantly improving strain prediction accuracy in scarce sample intervals.

The application provides a task reliability modeling method and system, and belongs to the technical field of reliability modeling. The method comprises the following steps: establishing a product structure tree based on different levels; constructing a virtual unit for a node corresponding to different reliability models with different working conditions, and adding the virtual unit to the product structure tree; establishing a reliability model set of each node under various working conditions in a hierarchical manner; selecting a reliability model of each level under a general working condition scene from the reliability model set according to a top-down principle of the product structure tree, and assembling the reliability model into a general working condition model under each working condition; dividing a task into different stages, establishing a reliability model of a task profile, and generating a reliability model file of the task profile. The application can not only realize effective reuse of the model, but also greatly improve the efficiency and accuracy of modeling, and provides a more efficient and convenient solution for reliability evaluation of a complex system.

The application discloses a kind of based on digital twinning unmanned mine car reliability rapid early warning method, system, it is related to unmanned mine car technical field, including the following steps: the digital twin simulation model of unmanned mine car is constructed;Based on the digital twin simulation model, the digital twin reliability model that the reliability state change of each module of the unmanned mine car is characterized is constructed;Based on the measured data of the unmanned mine car, the consistency of the digital twin simulation model and the digital twin reliability model is judged, and after judging consistent, reliability state database is constructed;Based on the reliability state database, digital twin reliability rapid early warning model is obtained by training, and the early warning model is used to identify and output the current reliability state of the unmanned mine car according to real-time acquisition vehicle-mounted sensor observation data, to realize early warning.The reliability state of each subsystem and module of unmanned mine car is rapidly and accurately warned.

This application relates to a method, apparatus, and computer device for constructing a reliability digital twin model. The method includes: acquiring an initial digital prototype model of a product; determining the failure modes of each component in the initial digital prototype model; determining the component reliability model for each component based on the failure modes; associating each component reliability model with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model; acquiring actual measurement data obtained from actual measurements of the product; using the initial reliability digital twin model to perform fault analysis on the actual measurement data corresponding to the model input parameters to obtain model fault analysis results; and updating the initial reliability digital twin model by combining the model fault analysis results and the actual measurement fault analysis results to obtain a reliability digital twin model. The reliability digital twin model constructed using this method can evaluate the reliability of a product, meeting the requirements of forward design work.

The invention belongs to the technical field of reliability modeling of fiber-optic gyroscopes, and particularly relates to a definite reliability modeling and analysis method of a high-precision fiber-optic gyroscope, which is based on performance margin and considers a degradation mechanism and uncertainty, and can accurately evaluate the reliability of the fiber-optic gyroscope. The problem that degradation and uncertainty are not considered in the prior art is solved. The method comprises the following steps: S1, performing function, performance and margin analysis on the fiber-optic gyroscope, and determining key performance parameters and threshold values of the fiber-optic gyroscope; determining the type of the key performance parameter in combination with the threshold value of the key performance parameter and a product design specification; s2, aiming at key performance parameters of the fiber-optic gyroscope, establishing a disciplinary cross equation of performance, internal dependent variables, external dependent variables and reversible time; s3, establishing a corresponding margin equation according to the type of the key performance parameter; s4, constructing a performance margin degradation equation based on the margin equation; and S5, on the basis of a performance margin degradation equation, considering internal and external cause uncertainty, and further constructing a definite reliability model.

The application discloses a kind of multi-stage system task reliability modeling evaluation methods considering complex dynamic characteristics, belong to system reliability modeling and evaluation technical field, including: according to the structure of system component unit, function principle and task flow, establish system function model and stage model;Based on the finite state machine with constraint, the state-transition model of each structure component unit is established respectively;According to the task activity of each stage and the success and failure criterion of task, the task flow model of system in the corresponding stage is constructed;Integrate the above steps, obtain the reliability model of multi-stage task, carry out task reliability simulation, calculate the occurrence probability of the structure component unit state or structure component unit state combination concerned.This application guarantees the efficient and stable operation of complex dynamic multi-stage system.

A method for generating a training data set includes receiving, by a processing circuit, warranty claim data associated with one or more building devices or building device components; processing, by the processing circuit, the warranty claim data using natural language processing to generate a training data set comprising one or more causes and solutions associated with failure of the one or more building devices or the building device components; and training, by the processing circuit, a component reliability model using the training data set to produce a trained model.

The invention provides a fastening connection system reliability dynamic modeling method which comprises the following steps: establishing a reliability model of fracture failure and loosening failure of a single threaded connection pair to obtain the reliability of the single threaded connection pair in different failure modes; based on a multi-competitive failure mode of a single threaded connection pair, establishing a dynamic reliability model of the fastening connection system under consideration of dynamic degradation by introducing an exponential decay model; wherein the exponential decay model is used for representing the decay process of the bolt pre-tightening force and strength along with time; and constructing a general generation function of each threaded connection pair for performing dynamic reliability calculation on the dynamic reliability model of the fastening connection system, and determining an optimal maintenance interval. According to the method, the multi-competition failure problem of fracture failure and loosening failure of a single threaded connection layer is effectively solved, the method better conforms to engineering practice, calculation is convenient and easy, implementation is easy, and engineering technicians can conveniently master the method.

A method and system are provided for monitoring operation and predicting service life of an active suspension system or another component or assembly. Sensor data representing operating conditions such as pressure, current, temperature, or actuator position are processed to generate usage indicators derived from binned or weighted values according to a damage function reflecting stress intensity or the frequency of high-load events. Usage indicators may be stored locally or in a cloud-based database and used by a controller to estimate component degradation or remaining service life in real time or retrospectively, and to communicate maintenance recommendations or update reliability models.

The invention discloses a whole life cycle carbon emission prediction method for a solid waste-based concrete structure, and the method comprises the steps: extracting historical production process characteristics, calculating historical production process carbon emission, building a production process carbon emission prediction model, and obtaining a production process carbon emission prediction amount. The method comprises the following steps: constructing a solid waste cementing material service performance evolution function and a full-life-cycle material reliability model, determining aging repair times, calculating a service process carbon emission predicted quantity, extracting historical dismantling process characteristics, calculating historical dismantling process carbon emission, and constructing a dismantling process carbon emission prediction model to obtain a dismantling process carbon emission predicted quantity. And determining the full-life-cycle carbon emission predicted quantity of the solid waste-based concrete structure. According to the method, not only is the crossing from static evaluation to dynamic prediction and from segmented calculation to full-period integration realized, but also an accurate and reliable quantification tool and decision support are provided for low-carbon design, material optimization and sustainable operation and maintenance of the solid waste-based concrete structure.

The invention relates to the technical field of reliability models, and discloses a method for establishing a reliability model of a biological aerosol analyzer, which comprises the following steps of: respectively establishing a corresponding model unit for each component unit in the biological aerosol analyzer, and assigning an index value; obtaining a reliable hierarchical link and a working state of the biological aerosol analyzer in each task mode; performing link index assignment on each model unit; according to the working state of each component unit in each task mode, carrying out working state parameter assignment on each model unit so as to obtain reliability parameters of the model units in different task modes; respectively establishing a model tree for each model unit; establishing an input condition, and adaptively establishing a corresponding reliability model according to the input condition; wherein the input conditions are the current task mode and the current environment parameters. According to the invention, different reliability models can be adaptively established according to the task mode change of the biological aerosol analyzer.

The invention belongs to the technical field of offshore wind power operation and maintenance and reliability engineering, and discloses an offshore wind turbine generator system-level health degree evaluation method based on dynamic weight distribution, and the method comprises the following steps: S1, collecting and preprocessing the multi-source data of an offshore wind turbine generator; s2, constructing a layered health degree evaluation index system; s3, fusing the mechanism model, the data driving model and the statistical reliability model, and constructing a subsystem degradation model to obtain a subsystem health degree; s4, performing weight fusion in combination with the static weight and the dynamic correction factor to obtain a dynamic weight; s5, fusing the subsystem health degree and the dynamic weight to obtain a system-level health degree; and S6, performing confidence interval sorting and risk grade division according to the system-level health degree. According to the evaluation method provided by the invention, through fusion of a mechanism model, a data driving model and a statistics model, the physical interpretability is reserved, the fitting capability of nonlinearity and coupling degradation is improved, and the misjudgment and missed judgment risks are reduced.