405 results about "Dependability analysis" patented technology

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention provides a non-probabilistic time-varying reliability analysis method based on ellipsoid correlation modeling. The method comprises the following steps: carrying out interval modeling on uncertainty input parameters; constructing a multi-dimensional ellipsoid uncertain domain based on correlation information among parameters; a system time-varying performance function is established, and an interval process of output response changing along with time is obtained through uncertainty propagation solution; identifying the maximum value point of the upper boundary and the minimum value point of the lower boundary in the interval process as reliability key points; respectively calculating non-probability reliability indexes based on the ellipsoid model at the moments of the two key points; and taking the smaller value of the two indexes as the non-probability time-varying reliability index of the system in the target time interval. According to the method, the problem that the evaluation result is conservative due to the fact that variable correlation is ignored in a traditional non-probability method is effectively solved, time-varying evolution of system performance can be accurately quantified, and an accurate and practical dynamic safety evaluation tool is provided for a complex engineering system with scarce data.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The invention belongs to the technical field of electric energy parameter monitoring, and particularly relates to an electric energy meter reliability analysis method and system, a computer device and a storage medium. The method comprises the steps of applying disturbance of a certain amplitude under a preset disturbance condition to original operation data of an electric energy meter to generate disturbance data under the corresponding disturbance condition, inputting the original operation data and the disturbance data under each disturbance condition into an inversion model to carry out parameter backstepping, and obtaining an inversion model under the corresponding disturbance condition. An inversion model under a corresponding disturbance condition is utilized to determine a predicted output value of the electric energy meter under the disturbance condition, the reliability of the electric energy meter is evaluated based on a residual error between a theoretical output value of the electric energy meter under a standard working condition and the predicted output value of the electric energy meter under each disturbance condition, and the inversion model is endowed with sensitive response capability to external fluctuation. The behavior change of the electric energy meter under a non-ideal condition can be observed, so that the performance change of the electric energy meter caused by disturbance can be identified.

The invention discloses an electrical equipment operation reliability analysis method based on RCM, and belongs to the technical field of electrical equipment operation and maintaining.The method comprises the steps that electrical equipment is decomposed into main function nodes and auxiliary function nodes through a function tree analysis method, a function performance standard library is constructed, and quantitative indexes of all the nodes and an incidence relation graph of the quantitative indexes are stored; constructing a failure mode library based on the function performance standard library, fusing a physical failure mechanism with historical fault data to generate a failure mode-function node mapping table, calculating a system-level function failure probability by adopting a Monte Carlo simulation method, and outputting a risk level matrix; using the risk level matrix to match the risk level to the node logic of the RCM decision tree, and generating a hybrid strategy set; and according to an execution result of the mixed strategy set, calculating a comprehensive health degree through an equipment health index model, and iteratively updating a failure mode library and a decision tree threshold parameter in combination with a reinforcement learning algorithm.

The invention provides an all-component multi-source solid waste asphalt mixture design method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of obtained test proportion data of a solid waste asphalt mixture, and generating a mixture data set; screening the mixture data set by using a preset constraint condition to obtain a plurality of candidate matching points meeting the performance index characteristics; the performance index characteristics comprise gradation structure characteristics related to particle size, bonding system characteristics related to high-temperature fluidity and water sensitivity, molding volume and construction characteristics related to porosity and saturation, and context embedding characteristics related to a service scene; splicing the performance index features and inputting the spliced performance index features into a trained gating expert network to obtain a corresponding performance prediction mean value and a performance fluctuation mean value; and performing reliability analysis on the candidate matching points based on the performance prediction mean value and the performance fluctuation mean value, and determining the candidate matching points of which the reliability is greater than a preset threshold value as target matching points.

The invention discloses a fault tree Boolean function equivalent mapping method based on an untrained neural network, and relates to the field of fault tree analysis. In order to solve the problems that in the prior art, a Boolean function mapping structure is not beneficial to parallel expansion, the calculation efficiency is limited, and the Boolean function mapping structure is difficult to efficiently realize on high-parallel platforms such as a GPU, the invention provides a method for generating topological structure data by analyzing a fault tree model; the basic events, the intermediate events and the top events are mapped into neurons of an input layer, a hidden layer and an output layer respectively, a feedforward network with fixed weight and bias is constructed, and a logic activation function is defined in nodes to realize Boolean logic propagation. The input layer receives a basic event state vector, outputs a top event result through forward propagation, and can realize large-scale Boolean function mapping on a parallel platform through batch input matrixes. The method is suitable for reliability analysis, minimum cut set simplification, top event probability calculation, parallelization fault tree solving and the like of a large-scale complex system.

The invention belongs to the technical field of reliability analysis, and discloses an electric energy meter adaptive residual life prediction method and system based on random degradation modeling, and the method comprises the steps: obtaining degradation data of an electric energy meter through an accelerated degradation test, removing failure data, and calculating a drift coefficient and a diffusion coefficient through a maximum likelihood estimation method; establishing a multi-source data fusion model of the drift coefficient and the temperature and humidity stress in combination with historical data, and fitting model parameters; expanding the model to a normal-temperature and normal-humidity condition, and calculating a corresponding drift coefficient; and based on the improved random degradation model, deriving a probability density function, an accumulative distribution function and a reliability function, and predicting the residual life of the electric energy meter at normal temperature and normal humidity. The system comprises a temperature and humidity test box, a power source table body and an upper computer, and is used for automatically collecting and analyzing data. Through multi-source data fusion and random degradation modeling, the prediction accuracy is improved, the test cost and time are reduced, and the method is suitable for electric energy meter reliability evaluation under complex working conditions.

The invention provides a random-interval hybrid uncertainty structure reliability analysis method and a related device, and belongs to the technical field of structure reliability analysis. The method mainly comprises two improvements: 1, determining a selection mode of a sampling center of an important sampling density function of a kth layer, constructing the important sampling density function according to the determined sampling center of the important sampling density function of the kth layer, and extracting a random sample of the kth layer according to the constructed important sampling density function; and 2, constructing a Kriging model according to the function relationship between the POD coefficient and the interval variable, constructing a mixed Kriging model on the basis of the Kriging model according to the function relationship between the output and the interval variable, and carrying out adaptive learning on the mixed Kriging model. According to the method, the problem that the reliability analysis efficiency of the random-interval hybrid uncertainty structure is not high is solved.

The invention discloses a time-varying reliability analysis method based on a double-layer BP neural network, and relates to the field of time-varying structure reliability analysis, and the method comprises the steps: generating a sample pool based on a joint probability density function which affects a random design variable of a to-be-analyzed time-varying structure; calculating weights of sample points, screening part of the sample points and establishing a first training data set; an inner-layer BP neural network model is constructed and trained, the minimum value of the time-varying performance function corresponding to each sample point in the first training data set about time is calculated, and a second training set is formed; an outer-layer BP neural network model is constructed, and a second training set is adopted for training; the reliability of a time-varying structure is calculated by adopting a trained outer-layer BP neural network model, the minimum value of a time-varying performance function about time is solved through an inner-layer model, and the outer-layer model is constructed based on parameters of sample points and the minimum value solved by the inner-layer model, is used for calculating the failure probability and has good applicability. The method can be used for time-varying reliability analysis of complex structures.

The present invention relates to the technical field of reliability analysis of IGBT modules in power semiconductor devices, and discloses a method for constructing an IGBT module life prediction model based on a GRU neural network, an IGBT module life prediction model with optimal parameters constructed using the construction method, and a method for predicting the life of an IGBT module using the prediction model. The present invention proposes a method for constructing a life prediction model based on a GRU neural network based on the aging characteristic quantity of the IGBT module. The present invention is rationally designed. When constructing the model, the grid search method is used to tune the parameters of the GRU neural network life prediction model, which improves the problem of overly random selection of recurrent neural network hyperparameters or only fine-tuning of existing reference neural network parameters. The proposed GRU neural network life prediction model with optimal parameters has higher prediction accuracy and better adaptability to the problem of life prediction of IGBT modules.

The invention discloses a multi-mode fusion power semiconductor module full-automatic detection system and method, relates to the technical field of semiconductor testing, constructs a multi-topology loading module, and realizes mechanical load testing under a frequency converter working condition, electrical load simulation under a grid-connected working condition and static electrical parameter testing. And covering dynamic and static performance detection of the module in a multi-application environment. A multi-source temperature monitoring and weighted fusion technology is introduced, and precise regulation and control of local junction temperature, surface temperature and radiator temperature of the module are realized. Dynamic switch characteristics are extracted from various collected data through wavelet transformation, and module failure probability prediction is completed in combination with a Weibull life model. Automation of whole-process data analysis and detection report generation is achieved, and the test efficiency and the evaluation intelligent level are improved. The method is suitable for multi-scene performance evaluation and reliability analysis of the power semiconductor module.

The invention discloses a ship shafting reliability analysis and prediction method and system based on vibration data, and aims to solve the problems of insufficient signal processing, low fault recognition precision, inaccurate life prediction and the like in an existing ship shafting reliability evaluation method. According to the method, data such as vibration, rotating speed and load current are collected through a multi-source sensor array, and comprehensive monitoring and intelligent diagnosis of the ship shafting state are achieved in combination with improved Hilbert-Huang transformation, a quantum convolutional neural network (QCNN) and a multi-physics field coupling model. The method not only can effectively extract shafting vibration characteristics, but also can improve the safety and reliability of ship shafting operation through a dynamically updated fault mode library and a self-adaptive early warning mechanism.

The invention discloses a mixed time-varying reliability analysis method for a solid rocket engine shell structure. Uncertain parameters and distribution are determined according to complex characteristics such as high-temperature and high-pressure time-varying load borne by a solid rocket engine shell structure, geometric sudden change in a star hole charging combustion stage, a throat ablation non-stationary interval process and a time-varying correction coefficient random process, and the random and interval processes are processed in a unified mode through equivalent time-varying uncertainty conversion. Input dimension explosion is avoided, specifically, a random process vector and an interval process vector are converted into an equivalent random vector, so that a time-varying limit state function is equivalently converted into a static function, the input dimension of the RBF combination model is effectively prevented from being increased, the advantages of multiple basis functions are fused through the radial basis function RBF combination model, the calculation cost is reduced, and the calculation efficiency is improved. The modeling precision of the strong nonlinear limit state function of the solid rocket engine shell is improved, the nonlinear mixed time-varying reliability analysis problem is efficiently solved, and the calculation precision is high.

The invention provides a composite beam reliability optimization method based on a failure probability function. The method comprises the following steps: 1) defining a random input variable, a design variable, a target function and a performance function; 2) calculating a failure sample and an extended failure probability based on an autonomous learning Kriging agent model and a Monte Carlo method according to the Bayesian theory; the method comprises the steps of (1) calculating a design parameter failure sample, (2) calculating a joint probability density implicit function on the basis of an adaptive K-nearest neighbor method and autonomous learning Kriging according to the calculated design parameter failure sample, and (3) calculating a failure probability implicit function on the basis of the Bayesian theorem.By means of the method, the problem that in the reliability optimization process, the calculation cost is difficult to bear due to reliability analysis is solved.

The invention discloses an MEMS inertial sensor reliability analysis method based on long and short term memory deep learning, and belongs to the technical field of microelectronic reliability evaluation, and the method comprises the steps: collecting an original output signal and environmental parameter data of an MEMS inertial sensor in a target scene, and recording a collection timestamp; then preprocessing is carried out to obtain a time sequence processing data set; performing feature extraction on the time sequence processing data set to form a multi-dimensional feature vector; carrying out correlation analysis on the multi-dimensional feature vectors, and screening out a key feature set; constructing an LSTM model based on the key feature set, and performing training and parameter optimization on the model to enable the model to capture a time sequence dependency relationship of sensor performance degradation; inputting the real-time key feature set subjected to preprocessing and feature extraction into the trained LSTM model, and outputting probability distribution of the current health state of the sensor; and then reliability evaluation is carried out and an early warning signal is output through the system terminal to prompt maintenance personnel to carry out timely intervention.

The invention belongs to the technical field of transistor detection, and particularly relates to a field effect transistor quality detection system and equipment thereof.The system comprises a transistor scanning module, a structural reliability analysis module, a signal excitation generation module, an intelligent feature analysis module, a transistor quality evaluation decision module and an interaction alarm end; according to the invention, the structural reliability analysis module analyzes based on the transistor image to assess the structural reliability of the field effect transistor, and generates various excitation signals suitable for field effect transistor detection when generating the structural reliability signal; the intelligent feature analysis module collects various electrical parameters and non-electrical parameters of the field effect transistor under signal excitation and performs deep analysis, and performs comprehensive and objective quality evaluation on the field effect transistor according to an analysis report in combination with a preset quality standard and an evaluation model, so that comprehensive, efficient and accurate detection on the field effect transistor is realized, and the detection accuracy is improved. And the quality detection level and the production efficiency can be improved.

The invention discloses an air-space-ground Internet of Things performance analysis method and system based on SWIPT and NOMA, and belongs to the technical field of air-space-ground integrated Internet of Things. Comprising the following steps: constructing an air-space-ground Internet of Things system model comprising an LEO satellite, an unmanned aerial vehicle relay and a ground user, and considering heterogeneous channel characteristics and node dynamics; deducing a closed-form solution of the outage probability of the system through joint transformation of a moment generation function and a characteristic function of a random variable; a high-order Taylor expansion and moment matching method is adopted to carry out approximation on the closed-form solution, a first-order approximate solution and a second-order approximate solution are obtained, and an upper bound of an approximate error is deduced. According to the method, the problem of accurate modeling of the outage probability under heterogeneous link coupling in the air-space-ground Internet of Things is solved, theoretical preciseness and engineering practicability are both achieved, and an effective tool is provided for network reliability analysis and optimization.

The invention relates to the technical field of unmanned aerial vehicles. Particularly relates to an unmanned aerial vehicle cluster multi-stage task reliability analysis method and system, a storage medium and a program. According to the method, a task stage process, a subsystem dependency relationship and reliability parameters are embedded into a SysML architecture model, a fault tree of each stage is automatically generated and converted into a binary decision diagram (BDD), and single-machine multi-stage task reliability calculation is achieved. On this basis, the reliability of the cluster in each stage and in the whole task is evaluated in combination with a k-out-of-n system model and a Monte Carlo simulation method, and the minimum delivery quantity is automatically optimized based on a reliability target. Finally, simulation and optimization results are automatically written back to the MBSE model through a parameter diagram, and design-simulation-decision closed-loop linkage is achieved. The method has the advantages of high modeling consistency, high calculation efficiency, optimal resource configuration and the like, and is suitable for reliability evaluation and optimal configuration of unmanned aerial vehicle cluster tasks in a complex environment.

A flight control equipment reliability allocation iterative optimization and dynamic prediction method comprises the following steps: constructing a Bayesian network through a system reliability block diagram of FMEA at an offline stage, expanding the Bayesian network, updating the network by taking a failure probability as a prior probability, and performing system reliability check to obtain a Bayesian network considering weight nodes; setting a conditional probability table and prior distribution of weight nodes; in the online stage, reliability is obtained through calculation according to the service life data of the parts, then the reliability is updated to be the prior probability in the Bayesian network, upward reasoning is conducted through the Bayesian network, and a real-time reliability predicted value is obtained. The Bayesian network is used for integrating prior knowledge and sample data, the variable dependency relationship is expressed through the conditional probability table, joint probability distribution is simplified, and the method is suitable for complex causal relationship modeling in reliability analysis.

The invention discloses a decoupling design method and system for reliability optimization of an angular contact ball bearing. The method comprises the following steps: determining initial design parameters of the angular contact ball bearing; constructing a reliability optimization design model which takes maximization of the rated dynamic load as a target and comprises probability reliability constraint processed by a penalty function; constructing a design variable and failure probability mapping agent model, specifically, identifying and quantifying uncertainty parameters, constructing a quasi-statics model and a limit state function, calculating the failure probability of a sample point through a probability reliability method, and training the agent model taking the design variable as input and the failure probability as output; and based on the proxy model, solving the reliability optimization design model by using an optimization algorithm, and outputting optimal bearing design parameters. According to the method, the reliability analysis process and the optimization iteration process are decoupled, so that the optimization efficiency is greatly improved while the design robustness is ensured.

The invention relates to the technical field of automatic driving perception, and provides an online vector map construction method and device, a storage medium and a program product. The map construction method comprises the steps of performing reliability analysis on a vehicle laser radar point cloud and a multi-view image, and fusing the laser radar point cloud and the multi-view image into a fused BEV feature based on a reliability analysis result; generating point column data for representing map elements based on the historical high-precision map; performing cross attention mechanism processing on the fused BEV features and the point column data to obtain current map elements; and processing the historical high-precision map based on the current map element to obtain a high-precision vector map. According to the technical scheme, the point cloud and the image can be fused based on the reliability analysis result of the laser radar point cloud and the multi-view image, so that the influence of unreliable data points on the whole data can be automatically reduced when the visual field is limited or the sensor data is abnormal, and the accuracy of vector map construction can be improved.

The invention discloses a vehicle structural strength reliability analysis method and device, electronic equipment and a medium. The method comprises the following steps: acquiring a vehicle component structure to be subjected to reliability analysis, determining a corresponding component function according to the vehicle component structure, and determining a fault mode generated due to the influence of structural strength based on the component function; establishing a structural strength finite element simulation calculation model corresponding to the vehicle part structure based on the vehicle part structure, extracting node stress data based on the structural strength finite element simulation calculation model, and determining strength data based on the vehicle part structure; and determining a strength reliability probability, a strength failure probability and a structural strength reliability index according to the node stress data and the strength data, and generating a vehicle structural strength reliability analysis result according to the component function, the failure mode, the strength reliability probability, the strength failure probability and the structural strength reliability index. And carrying out reliability evaluation on the vehicle part structure based on the vehicle structure strength reliability analysis result.

The invention belongs to the technical field of spacecraft product quality and reliability analysis modeling, and particularly relates to a fault tree generation method and device based on a knowledge graph and a large model fusion technology. The method comprises the following steps: constructing a spacecraft product fault domain ontology model, establishing a tag system comprising entity tags and relation tags, and extracting related knowledge from a technical document, a SysML model and an FMEA table by using a large language model to form a triple; according to entity types and relation types of different source knowledge, fusion is carried out, and a fault knowledge graph is constructed; obtaining a function dependence sub-graph, a fault behavior sub-graph and a redundant sub-graph of a function or a composition structure from the fault knowledge graph, and retrieving a specific sub-graph from the fault knowledge graph according to a user question; analyzing fault propagation relation and redundant structure information based on function dependence in the specific sub-graph to construct an OR gate and an AND gate, and mapping nodes in the specific sub-graph to a basic event, an intermediate event, a top event and a gate to generate a fault tree.

The application discloses a kind of abrasive grain flow microchannel polishing quality reliability analysis systems, including abrasive grain flow polishing system, quality detection system and reliability analysis system, the abrasive grain flow polishing system is used in the polishing processing of microchannel in microfluidic chip;The quality detection system is used to carry out quality detection to the microchannel after processing, and obtains detection value R1;The reliability analysis system carries out reliability analysis to processing quality, and the information basis of analysis is processing parameter P and the microchannel processing quality detection value R1 obtained by detection;The reliability analysis system transmits adjusted processing parameter to the abrasive grain flow polishing system, and is used to control microchannel polishing.The application also discloses a kind of abrasive grain flow microchannel polishing quality reliability analysis method.The application utilizes new learning function, and the correlation coefficient introduced by new learning function, improves the accuracy of model in threshold value vicinity, and can avoid the accuracy in low correlation area, ensures the accuracy of reliability analysis.

The invention discloses a space station on-orbit maintenance SA network analysis method based on DSA and a Bayesian network, and the method comprises the steps: firstly building a topological structure of an SA reliability network, then obtaining the structure parameters of the SA reliability network, carrying out the SA reliability analysis, building an applicable proposition network according to a given manned spaceflight extravehicular activity research object, and carrying out the analysis of the SA reliability. And finally, carrying out systematic and qualitative DSA analysis on the researched manned spaceflight extravehicular activity tasks based on the propositional network. The method can effectively provide support for personnel decision making of the task system, provide support for training design of task personnel, evaluate rationality of information interaction design of the current task system, identify key nodes of information interaction and provide support for information interaction design of the task system. The method is suitable for evaluation and optimization of EVA task system design of space stations in China and astronaut ground training system design.

The invention provides a reliability evaluation system and method for a two-degree-of-freedom rotational vibration energy collector, and belongs to the field of nonlinear rotational vibration energy collection, and the method comprises the steps: S1, constructing a kinetic model, and converting the kinetic model into a Hamiltonian form; s2, reducing dimensions by adopting a random average method; s3, calculating a drift coefficient and a diffusion coefficient; s4, constructing and solving a boundary value problem to verify the calculation precision; and S5, parameter sensitivity analysis and design optimization. According to the reliability evaluation system and method for the two-degree-of-freedom rotary vibration energy collector, the problems that in reliability analysis of an existing two-degree-of-freedom rotary vibration energy collector, high-dimensional coupling characteristics are difficult to capture, an analysis frame is incomplete, calculation efficiency is low, and parameter optimization lacks theoretical guidance are solved.

The present application relates to the technical field of data processing technology and discloses a method for analyzing the reliability of avionics products using multi-source interval data, comprising: summarizing and classifying the multi-source interval data of a specified avionics product to obtain multiple groups of interval data, substituting the multiple groups into an exponential distribution model, and constructing corresponding quality functions according to a unified identification framework; obtaining conflict coefficients and credibility coefficients between the quality functions corresponding to different groups of the interval data, and determining fusion weight coefficients of the respective quality functions; obtaining a weighted quality function according to the fusion weight coefficients, fusing the weighted quality functions based on the D-S evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability transformation method; obtaining the reliability of the avionics product at any time according to the transformation result, fitting and obtaining a reliability curve with respect to time changes to perform reliability analysis.

The application provides a reliability analysis method for positioning accuracy of a shield tool changing robot, realizes sensitivity analysis of error parameters by constructing a kinematic model and an error proxy model, and accurately evaluates the failure probability of positioning accuracy. The assembly and machining errors are comprehensively considered, the error parameters are treated as random variables, the accuracy and efficiency of the analysis are improved. The mean and standard deviation of the random variable are used for sensitivity calculation, and a failure calculation method of positioning accuracy sensitivity is obtained, which provides an important basis for optimization design. The application not only helps to reduce the tool changing time, improve the construction efficiency and reduce the safety risk, but also provides an accurate and reliable tool changing robot manufacturing scheme for designers. The method has universality and flexibility, and can be widely applied to performance improvement and fault prevention of the shield tool changing robot. The implementation of the application will promote the development of shield construction technology and provide strong support for industrial automation and efficient production.