46 results about "Dependability analysis" patented technology

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.

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.

This invention relates to the field of image segmentation technology and discloses a construction site risk behavior recognition system based on big data. The system constructs a multi-view spatiotemporal representation of construction by uniformly aligning multi-view video streams, personnel positioning information, equipment operating status, and construction space area data at the construction site. Simultaneously, it constructs a segmentation model including an image encoder and a Bayesian mask decoder, and introduces a dynamic evidence adaptation mechanism based on Set Transformer and Hypernetwork to achieve dynamic segmentation of construction targets and hazardous areas in complex construction scenarios. Furthermore, by combining spatial posterior distribution, semantic posterior distribution, and a visual language model, it achieves reliability analysis and semantic discrimination of risk behaviors, thereby improving the reliability and adaptability of risk behavior recognition in complex construction scenarios.

This invention provides a method and system for stress analysis of subsea tunnel support structures, relating to the field of subsea engineering technology. The method includes: acquiring and comparing the differences between exploration data of the first geological layer after excavation and exploration data of the second geological layer before initial shotcreting of the tunnel cross-section to obtain fissure anomaly increment data; obtaining a first gain factor and a second gain factor affecting the formation of the water pressure field based on the first hydrodynamic pressure data and the fissure anomaly increment data; and continuously optimizing and adjusting the cumulative support strength corresponding to the corresponding support sub-process and the geological layer anomaly increment data corresponding to the second geological layer exploration data based on the first gain factor, the second gain factor, and the second hydrodynamic pressure data corresponding to each support sub-process in the initial support, to complete the stress reliability analysis of the initial support structure. The method and system provided by this invention can effectively ensure the safety of tunnel support.

The application discloses a wheel disc reliability optimization method considering multiple failure mode correlations, and belongs to the field of aerospace. The method comprises three steps: a pair-copula method is used to analyze correlations between failure modes; a reliability distribution method is used to distribute wheel disc reliability to the failure modes; and an improved multi-objective particle swarm optimization algorithm is used to realize wheel disc reliability optimization design. The application establishes a reliability analysis and distribution model based on a cost function from the perspective of system reliability, and realizes a low-cost reliability optimization method considering failure correlations.

A semantically secure identification and communication method based on classical quantum channels comprises the following steps: constructing a classical quantum channel, selecting the input signal distribution and dividing the codeword, constructing an encoder, joint decoding, verification and identification, reliability analysis, and information leakage analysis. This invention divides the communication codeword into a main block and an auxiliary block. The main block uses an output statistical approximation of the transmission codebook, making it impossible for eavesdroppers to distinguish the communication signal from background noise. The auxiliary block uses a hash function for random mapping, preventing eavesdroppers from extracting valid information, thus achieving semantic security. Mapping the identification message to a combination of seed and hash value reduces the probability of confusion between different identification messages, improves identification accuracy, and provides a coding basis for analyzing false alarms, missed alarms, and information leakage. This invention has advantages such as strong confidentiality, strong concealment, high identification accuracy, and the ability to achieve identification communication while satisfying semantically secure requirements. It can be used for classical quantum secure communication.

This invention discloses a homogenization model for wound iron cores and its electro-magnetic-thermal coupling simulation method for hot spot location determination and temperature prediction. The method establishes an anisotropic electromagnetic field model with conductivity and permeability using Maxwell to calculate electromagnetic loss distribution; it achieves dynamic coupling of circuit and magnetic field using TwinBuilder; it imports electromagnetic loss as a heat source into Icepak to perform thermal field simulation on the anisotropic thermal conductivity wound iron core model; and it completes hot spot tracking and temperature prediction through multi-physics iterative optimization. This method solves the problems of traditional models not considering material characteristics and insufficient accuracy of single-physics simulation, significantly improving the accuracy of hot spot analysis under complex operating conditions. It is suitable for reliability analysis and hot spot monitoring optimization of high-power, high-frequency dry-type wound iron core transformers.

This invention belongs to the field of underwater manipulator reliability analysis technology, and discloses a non-probabilistic reliability analysis method for underwater manipulators considering fluid-structure interaction effects. The method includes the following steps: quantifying the uncertainty of fluid load parameters under finite sample conditions using a non-probabilistic interval method; updating sample points based on Bayesian theory, and obtaining the relationship between interval parameters and reliability through analytical solutions; conducting strength and stability analysis of underwater manipulator components using the buckling stability criterion as the failure criterion to obtain the critical buckling load; performing uncertainty propagation analysis using the Taylor series expansion method to solve for the structural response interval corresponding to the non-probabilistic interval of the input uncertainty parameters; and comparing the response interval with the limit state equation using stress-strength interference theory or tolerance interference theory to establish a non-probabilistic Bayesian reliable reliability analysis model, suitable for reliability assessment of underwater manipulator components under small sample conditions.

This invention discloses a probabilistic-nonprobabilistic-probabilistic box hybrid reliability model and solution method. It employs truncated probability variables to describe uncertain parameters with sufficient samples in the structure; nonprobabilistic interval variables to describe uncertain parameters with insufficient sample numbers in the structure; and probabilistic box variables to describe uncertain parameters with both cognitive and random uncertainties. Based on interval analysis of interval variables, a decoupling method for probabilistic box variables is proposed, and then the double-nested problem is optimized and solved. A hybrid reliability index is introduced to represent the hybrid reliability analysis results, and a hybrid optimization algorithm combining an outer genetic algorithm and an inner interior point method is used to solve the problem, avoiding solution errors caused by getting trapped in local optima. Finally, the failure probability interval and the corresponding hybrid reliability index interval are obtained.

This application belongs to the field of robotic arm reliability analysis technology, specifically involving a time-varying reliability analysis system and method for robotic arm mechanisms with probabilistic box parameters. It obtains discrete motion moments within the robotic arm's working cycle, constructs random variables for link length machining errors, joint torsion angle assembly errors, and gear transmission clearance errors, and builds a basic time-varying reliability analysis model. The end effector gripping load, joint friction coefficient, and link material elastic modulus are represented as probabilistic box variables. Random variables and probabilistic box variables are jointly introduced into a time-varying function to form a double-layer nested optimization model. Combining a neural operator network surrogate model, Monte Carlo simulation, and Bayesian optimization, the upper and lower boundaries of the robotic arm's time-varying reliability probability are solved. This application solves the problems of difficulty in accurately assessing the time-varying reliability of robotic arms under the combined influence of random and probabilistic box variables, and the high computational cost after introducing probabilistic box variables, thus improving the accuracy of assessing the risk of robotic arm trajectory out-of-bounds failure.

This invention provides a calibration method for metrological data, relating to the field of metrological calibration technology, comprising the following steps: S1: Constructing a multi-dimensional data acquisition system to obtain multi-dimensional data information; and preprocessing the acquired multi-dimensional data; S2: Performing quality assessment and reliability analysis on the preprocessed data, and calculating adaptive weights for each dimension; S3: Using a weighted fusion algorithm, combining the calibration results of each dimension to perform multi-dimensional fusion calibration; S4: Constructing a machine learning-based prediction model to perform predictive calibration on the data after multi-dimensional fusion calibration; S5: Verifying the calibration results through mutual measurement using a quantum benchmark, and optimizing the prediction model parameters by combining historical calibration data; This invention improves data quality, calibration accuracy, and reliability through the multi-dimensional data quality assessment and adaptive weight calculation in step S2.

The present application belongs to the field of metrology and detection, and particularly relates to a measurement system reliability analysis system and method. Current measurement system analysis using computing function software has a high degree of human intervention, which introduces artificial uncertainty. The present application provides a measurement system reliability analysis system and method, which includes a stability degree data analysis module, a deviation and linearity analysis module, and a repeatability and reproducibility analysis module. The system analyzes the characteristics of the measurement system in a targeted manner and ensures compliance with standard requirements, standardizes the analysis method of the measurement system, and streamlines the process. Under the premise of ensuring analysis accuracy, the operation method is simplified. The analysis speed of the measurement system is improved, and the problems of uncontrolled calculation process caused by human factors and questionable effectiveness of analysis conclusions caused by human factors are reduced. Personnel costs are reduced, time is greatly saved, and the system is more accurate and reliable.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses an agent tool chain management method and device, equipment and a storage medium, and the method comprises the steps: maintaining and dynamically updating a real-time reliability score for each tool in a tool set capable of being called by an agent; determining a target tool from the tool set based on the user query task instruction and reliability analysis; executing the task through the target tool to obtain an execution result; if the execution result is execution failure, obtaining an original error log and failure context information, and converting the original error log and the failure context information into semantic error description information; multi-path backtracking and re-planning operations are performed based on the semantic error description information to switch to an alternative tool and continue to execute the task. The reliability and task success rate of the intelligent agent tool chain are improved, the fault-tolerant capability is enhanced, and resource waste is reduced.

The present application relates to the technical field of pipeline structure reliability analysis, in particular to a pipeline structure reliability analysis method under interval uncertainty, comprising: collecting basic parameters affecting the vibration fatigue life of the pipeline structure; establishing a function function in reliability analysis; standardizing the input vector and the function function; establishing a reliability model of the pipeline structure under interval uncertainty with the standard function function as a constraint; introducing an intermediate variable for equivalent conversion; solving by using the dichotomy combined with the random search feasibility judgment method to obtain the reliability index of the pipeline structure. The present application solves the problems of difficulty in solving the black box function and difficulty in ensuring global convergence in the vibration fatigue life analysis of the engine pipeline structure by using the interval reliability analysis framework of the dichotomy combined with the random search feasibility judgment.

This invention discloses a method and system for data expansion and reliability analysis of avionics equipment, comprising: collecting reliability growth test data of avionics equipment to obtain a full sample dataset and a candidate subsample set; evaluating the information content of the candidate subsample set by maximizing Rényi entropy; constructing a dual-objective collaborative optimization model of entropy constraint and distribution distance by minimizing the distribution difference between the candidate subsample set and the full sample set by minimizing the Wasserstein distance metric; determining the globally optimal subsample set and sample reuse degree by using a greedy search to traverse the sample reuse degree; establishing an avionics equipment reliability analysis model based on a two-parameter Weibull distribution; adaptively selecting a hyperparameter estimation method according to prior information conditions and model complexity and constructing a posterior distribution based on Bayes' theorem; and using a step-by-step sampling method for avionics equipment reliability analysis and life prediction. This invention achieves both information content and distribution representativeness in data expansion and reliability analysis.

The present application provides a method and an apparatus for determining a failure probability in a simulation test for autonomous driving and relates to the technical field of autonomous driving, wherein the method comprises: performing a high-coverage uniform sampling in a high-dimensional parameter space corresponding to a scene of autonomous driving logic to obtain a collection of first test scenes; performing a simulation test for each first test scene in the collection of first test scenes to obtain a first test indicator value of a system under test of a vehicle under test in each first test scene; determining a scene weighting of each first test scene in the high-dimensional parameter space; determining a first test failure probability of the system under test based on the scene weighting and the first test indicator value.Performing a sensitivity analysis for initial scene parameters that influence the first test indicator value to determine sensitive scene parameters; reducing the high-dimensional parameter space to a low-dimensional parameter space consisting of the sensitive scene parameters if the first test failure probability does not meet the accuracy requirement; and performing a reliability analysis of the low-dimensional parameter space to determine a second test failure probability of the system under test, using the second test failure probability as a final failure probability. The method and apparatus for determining a failure probability in an autonomous driving simulation test solves the problem that the relevant failure probability determination methods have low computational efficiency.

The present disclosure relates to a turbine blade system failure probability function solving method based on sample information sharing, and relates to the technical field of reliability analysis, and the method comprises the following steps: constructing a unified sampling density function independent of distribution parameters according to the distribution parameters of input variables of a turbine blade system, and constructing an alternative sample pool for solving the failure probability function of the turbine blade system according to the distribution parameters; extracting first training sample points from the alternative sample pool according to the unified sampling density function, and constructing initial Kriging surrogate models under different failure modes according to the first training sample points; selecting second training sample points according to the system state misjudgment probability of the turbine blade system, and updating the initial Kriging surrogate models based on the second training sample points to obtain converged Kriging surrogate models; and using the converged Kriging surrogate models to solve the failure probability function of the turbine blade system. The present disclosure improves the calculation efficiency of the failure probability function.

The application belongs to the field of structural reliability analysis and uncertainty quantification, and specifically discloses an active learning reliability analysis method based on spectral normalized Gaussian process, which comprises the following steps: generating a Monte Carlo sample set according to a joint probability density function of random variables; then constructing an initial training data set and a spectral normalized Gaussian process (SNGP) surrogate model of a structural performance function; predicting the Monte Carlo sample set by using the surrogate model, estimating a system failure probability and screening a candidate sample pool; judging whether the model converges and whether the system failure probability meets the accuracy requirement according to the prediction result; if yes, outputting the surrogate model and the system failure probability estimation result, and completing the analysis. The application can significantly reduce the number of calls to the original calculation model in complex high-dimensional nonlinear reliability analysis, greatly improve the analysis efficiency while ensuring the accuracy, and is suitable for reliability analysis and design optimization of high-dimensional strong nonlinear systems in the fields of aerospace, vehicle engineering and the like.

This invention discloses a hybrid reliability analysis method based on an active learning kriging model, belonging to the field of reliability analysis technology. First, an initial experimental design set is defined using a small number of initial samples and true response values. Based on this initial experimental design set, an initial kriging model is constructed. Then, based on the current kriging model, the failure probability corresponding to each candidate interval sample is calculated. Using a new selection criterion, the optimal interval sample and the optimal random sample are determined, together constituting the optimal sample. The optimal sample and its corresponding true response value are expanded to the initial experimental design set. The initial kriging model is updated iteratively using the experimental design set. If the hybrid convergence criterion is satisfied, the iteration terminates, and the lower and upper boundaries of the failure probability are output. This invention avoids selecting useless samples; each iteration selects the two optimal samples corresponding to the minimum and maximum failure probabilities, reducing the total number of function calls and improving computational efficiency.

The application discloses a kind of composite bolt connection uncertainty failure prediction proxy model construction methods, belong to composite structure uncertainty analysis field, this method includes: the material constitutive relation with damage evolution characteristics is constructed, and parameterized finite element model of composite material bolt connection is established;The factor leading to strength dispersion is classified into two scales of micro and macro, and is further classified into random and interval uncertainty variable;Sampling points are generated by optimal Latin hypercube sampling, and equivalent material properties in original design space are obtained by inverse transformation method and homogenization method;Strength observation values at sampling points are obtained by batch simulation, and sample set is obtained;Model training is carried out based on Kriging principle, and a high-precision composite material bolt connection uncertainty failure prediction proxy model is established.The application can lay a foundation for reliability analysis of composite material bolt connection under multi-source multi-scale uncertainty.

The application provides a bipolar transistor reliability analysis method and system and electronic equipment, and belongs to the field of semiconductor device reliability analysis. The method comprises the following steps: based on a boundary model of a bipolar transistor device, performing initial mesh division on the boundary model; performing mesh encryption on the junction surface of the emitter and the base of the bipolar transistor device and the junction surface of the base and the collector, and performing mesh reconstruction; defining a simulation physical model and a defect evolution model, introducing related terms of defect concentration and carrier recombination rate through defects into the control equation of the simulation physical model, and coupling the defect reaction rate of the defect evolution model with the current intensity of the device; determining an application area of the defect evolution model, and assigning the defect evolution model to the application area; and performing analysis based on the simulation physical model and the defect evolution model to obtain reliability degradation data. The application can restore the reliability state of the bipolar transistor device in the actual application scenario, and perform efficient and accurate reliability analysis.

The application discloses a kind of hybrid uncertainty under numerical control machine tool rotary shaft positioning error reliability analysis method.The application proposes the unified representation and modeling method for the positioning error of numerical control machine tool rotary table rotary shaft, structure geometric error and friction and return gap error are included in the same parameterization framework, can simultaneously depict direction insensitivity and direction related two key error components, and positioning error reliability interval is given under hybrid uncertainty framework, and the identification calculation efficiency of weak angle area is realized.The application is based on no-load two-way positioning test data, error component separation is realized by even, odd symmetry decomposition, and least square and cross validation are used to complete parameter identification and model bias statistical representation, improve the model explainability and robustness.

A reliability analysis method for control rod drop based on random collocation perturbation is disclosed, belonging to the field of reliability analysis. First, based on the control rod drop structure, an uncertain drop dynamics model is constructed, and the random variables, random variable vectors, and their distributions during the drop process are determined. Second, considering the random variables, the statistical characteristic values ​​of the drop time are calculated using the random collocation perturbation method based on the uncertain drop dynamics model. Third, using the statistical characteristic values, the probability density function of the drop time is obtained based on the maximum entropy principle. Finally, based on reliability theory, the reliability index of the control rod drop time is calculated. This invention performs reliability analysis based on the calculated probability density function of the control rod drop time, which significantly improves computational efficiency; it fills the gap in the research on reliability analysis of control rod drop when random variables are present; and compared with other reliability analysis methods, it is more efficient in calculating reliability indices.

The application discloses a kind of multi-direction die forging hydraulic press reliability design method based on adaptive aggregation model belongs to mechanical engineering reliability design technical field.For the problem that global and local are difficult to optimize in non-uniform response surface modeling of heterogeneous agent model and the key area prediction accuracy is insufficient due to static weight distribution, a dynamic weighting strategy is proposed. An optimization model is established with the minimum weight of the structure as the target and the stress deformation and modal frequency reliability as the constraint. Latin hypercube sampling is used. A polynomial chaos expansion and Kriging heterogeneous agent model is constructed. The precision radius is dynamically defined according to the weighted error ratio to divide the local high confidence area and the global smooth area. The global and local weights are calculated and the contribution rate of each base model is dynamically adjusted using the mixed weight. Reliability analysis is performed based on the aggregation model and iterative optimization is performed. The prediction accuracy of the key failure boundary area is significantly improved, the calculation cost is reduced, and the lightweight and reliability of the multi-direction die forging hydraulic press are optimized.

The application discloses a kind of series joint mechanism error correction method, actuator and storage medium, it is related to robot kinematics precision and reliability analysis technical field, it is difficult to correct when the end pose deflection of multiple joint series mechanism in the existence of machining and assembly error is solved Technical problems.The method includes establishing series joint mechanism coordinate system according to the degree of freedom of series joint mechanism on three-dimensional space section, and calibrating joint center, bar length and joint kinematic pair;Effective length parameter is obtained by effective rod length theory;The joint local coordinate pose correction amount is obtained by vector sum method;D-H conversion matrix is corrected by effective length parameter and joint local coordinate pose correction amount, and error transmission chain is formed based on motion transmission sequence, end pose uncertain position vector is obtained by combining D-H conversion matrix, and the joint error of series joint mechanism is corrected according to the end pose uncertain position vector.

This invention relates to a method and system for identifying reverse recovery conduction paths and predicting dynamic characteristics of SiC MOSFETs, belonging to the field of power semiconductor device testing and reliability analysis. The method includes: Step 1: Constructing a dual-pulse test circuit and acquiring waveform data; Step 2: Preprocessing the data; Step 3: Extracting feature parameters and constructing feature vectors; Step 4: Outputting the conduction path type and device degradation state; Step 5: Based on the conduction path type, combined with the total current decomposition relationship and the dynamic response characteristics of the device under different test conditions, establishing a conduction path decomposition model, and calculating the current proportions of the MOS channel conduction path, body diode conduction path, and Schottky diode conduction path; Step 6: Predicting reverse recovery parameters; Step 7: Assessing device status, predicting remaining lifetime, and assessing failure risk. This invention automates the processing of dual-pulse test data and, for the first time, achieves quantitative identification of conduction paths.

This invention discloses a method, actuator, and storage medium for correcting errors in a series joint mechanism, relating to the field of robot kinematic accuracy and reliability analysis technology. It solves the technical problem of difficulty in correcting end-effector pose offset in multi-joint series mechanisms when machining and assembly errors exist. The method includes: establishing a coordinate system for the series joint mechanism based on its degrees of freedom in a three-dimensional spatial plane; calibrating the joint center, link lengths, and joint kinematic pairs; obtaining effective link length parameters through effective link length theory; obtaining the joint local coordinate pose correction amount through a vector sum method; correcting the D-H transformation matrix using the effective link length parameters and the joint local coordinate pose correction amount; forming an error propagation chain based on the motion transmission sequence; obtaining the end-effector pose uncertainty position vector by combining the D-H transformation matrix; and correcting the joint error of the series joint mechanism based on the end-effector pose uncertainty position vector.

This invention relates to the field of product reliability analysis technology, specifically to a reliability assessment method for a non-fixed shape parameter dependent competing failure model. The method includes the following steps: constructing a dependent competing failure model, designing an adaptive stepwise type II hybrid truncated constant-load life test, estimating parameter points based on test data, establishing an acceleration equation, calculating product reliability under normal stress, and performing interval estimation. This invention constructs a MOBG distribution description that assumes fixed shape parameters and does not consider the dependencies between unknown failure mechanisms and causes. It designs an adaptive stepwise type II hybrid truncated constant-load life test to obtain data, solves for parameters based on maximum likelihood estimation and Bayesian estimation, and combines the acceleration equation to obtain parameter estimates under normal stress. This leads to the calculation of product reliability and interval estimation, improving the accuracy and applicability of reliability assessment. It is suitable for reliability analysis of various products with multiple failure mechanisms and potentially unknown causes.

The application provides a fault tree generation method and computer equipment for cloud computing services, acquires heterogeneous spatio-temporal data of a first cloud platform, which includes spatial data and monitoring time series data from different data sources, then determines each fault event existing in the first cloud platform and the causal logical relationship between each fault event based on the heterogeneous spatio-temporal data, carries out fault tree modeling on each fault event and the causal logical relationship, obtains at least one logical fault tree, and at least based on the heterogeneous spatio-temporal data, carries out attribute labeling on each event object contained in the at least one logical fault tree, generates a target fault tree for the first cloud platform, and is used for reliability analysis of cloud computing services.