113 results about "Failure probability" 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.

The application discloses a dynamic reliability evaluation method for a permanent magnet planetary gear transmission system, and belongs to the technical field of reliability analysis of electromechanical coupling transmission systems. The method is characterized in that: firstly, the service process of the system is divided into stages, and a random variable model is established; based on the wear evolution relationship, parameters of each stage are obtained through parameter mapping; then, stage high-fidelity unified electromechanical dynamics solving is performed, and frequency-related characteristic indexes are extracted; a multi-failure mode limit state vector is constructed, and a stage failure event is defined; on this basis, a multi-response Gaussian process proxy model is used for fitting and iterative updating of the limit state function through stage-by-stage active learning; the U function is used for initial screening of candidate samples, EFF is calculated and sorted, and a new sample point is selected according to the Top-K criterion to update the proxy model, until the convergence criterion is met; finally, the dynamic failure probability of the system is calculated by using the updated proxy model. The application realizes efficient and accurate evaluation of the failure probability of multiple failure modes, reduces the calculation cost, and improves the engineering applicability.

The system comprises a data access module, a power grid flood disaster field knowledge graph, a digital twin body environment, and a digital twin body agent (DTA), a monitoring and early warning agent (MWA), a fault diagnosis agent (FDA) and a case handling agent (CHA). The DTA provides simulation deduction services. The MWA generates a flood field and a device-level risk list using a physical model chain. The FDA predicts device failure probability using a GWO-BPNN model. The CHA retrieves cases based on the knowledge graph to generate a scheme, and requests the DTA to perform virtual deduction and iterative optimization. The application interaction layer integrates visual output. The application realizes a full-process closed-loop intelligent decision from disaster perception, risk diagnosis, scheme generation to virtual pre-performance, solves the problems of insufficient early warning accuracy, static risk evaluation and lack of forward-looking verification of the disposal scheme, and improves the emergency response capability and decision scientificity of the power grid in response to flood disasters.

The tunnel lining reliability evaluation method considering space-time coupling provided by the application comprises the following steps: based on the target tunnel site geological survey data, a probability distribution model of a karst cave radius is established; based on the probability distribution model of the karst cave radius, a quantile sampling method is used to generate a plurality of karst cave radius samples; the karst cave radius samples are input into numerical simulation software to simulate and calculate the minimum principal stress value of the tunnel vault lining under different karst cave radii; based on the karst cave radius samples and the minimum principal stress, a function relationship fitting model between the karst cave radius and the minimum principal stress is established; based on the probability distribution model of the karst cave radius and the function relationship fitting model, a stress ratio probability distribution model is derived through a random variable function distribution conversion method; a strength degradation time-varying model of concrete under the action of freeze-thaw cycles under different stress ratios is established; based on the stress ratio probability distribution model and the strength degradation time-varying model, the marginal failure probability of the tunnel lining is calculated by coupling using a total probability formula.

This invention relates to the field of new energy cable management technology, specifically to an intelligent management method and system for new energy cables based on the Internet of Things (IoT). The system includes: an array of sensors deployed on the new energy cable itself and at monitoring points along the cable line to continuously collect real-time operational status data such as cable sheath temperature, conductor current load, joint vibration amplitude, and ambient air humidity; the data is transmitted to an edge computing node for preprocessing and standardization to form a cable health data package; this data package is then input into an improved fault probability prediction algorithm based on a historical fault database and an environmental stress evolution model to calculate the instantaneous probability values ​​of different levels of faults in each monitoring section at a specific time in the future; adaptive inspections are triggered based on these probability values, generating intelligent work orders that are sent to a mobile inspection terminal; and the algorithm parameters are calibrated using the returned on-site verification results. This method can accurately predict fault risks, rationally allocate inspection resources, continuously optimize algorithm accuracy, and ensure the safe and stable operation of new energy cables.

This invention discloses a method and system for full life-cycle management of roads and bridges, relating to the field of road and bridge technology. The method includes acquiring the BIM model of the bridge during the design phase and structural state data and environmental-load data for each stage of the bridge's life-cycle, and constructing a unified model for structural performance degradation. A performance function is generated through multi-factor temporal coupling processing. The performance function is then spatiotemporally aligned and fused with the BIM model, structural state data, and environmental-load data for each stage of the bridge's life-cycle to form a digital structural ontology. Structural state data is extracted from the digital structural ontology, and a dynamic knowledge graph with semantic reasoning capabilities is constructed to output the structural health status. Based on the performance function and structural health status, the failure probability trend is determined and high-risk components are identified, taking into account the bridge's road network level, design capacity, and preset weights for traffic interruption losses, public safety risks, and ecological environment disturbances.

The invention relates to a method for determining a failure probability, in particular at least one state of increased failure probability, of at least one cell system (10a) with at least one electrochemical cell (12a), wherein in at least one determination step (14a; 14b; 14c; 14d) at least one degradation parameter of the at least one electrochemical cell (12a), which in particular allows conclusions to be drawn about a degradation state of the at least one electrochemical cell (12a) in a defined state, is determined by means of a virtual sensor unit (16a). It is proposed that in one determination step (14a; 14b; 14c; 14d) a failure probability of the at least one electrochemical cell (12a) is determined as a function of the degradation parameter.

This embodiment discloses a dynamic evaluation method for the reliability of railway tunnels along the time axis. By considering the time-effect control equations for the initial lining of railway tunnels, as well as initial and boundary conditions, a loss function for a physical driving model is established using the finite difference method to train the model. This solves the problem that the solutions of traditional AD sampling points are independent and it is difficult to effectively utilize information from adjacent areas. After training the physical driving model, the initial lining displacement values ​​of the railway tunnel are predicted, and a system limit state function is established based on the predicted initial lining displacement values ​​to obtain the time-varying failure probability of the railway tunnel. By establishing a time-effect control equation for the initial lining of railway tunnels, this invention can fully consider the spatial and temporal variation characteristics of surrounding rock parameters, improve the efficient fusion capability of real-time monitoring data, and make the evaluation results close to the actual state of the tunnel, greatly reducing potential railway traffic hazards.

The application discloses a downhole tool structure reliability judgment method based on real-time working conditions and belongs to the technical field of oil drilling, and comprises the following steps: S1, acquiring a downhole load working condition time sequence table and recording a downhole tool structure load change curve; S2, sampling downhole tool structure parts that have failed, and establishing a downhole tool structure load change sample library before failure occurs; S3, comparing samples before failure of the same downhole tool structure through time sequence curve similarity, extracting multi-factor coupling load change characteristics before failure of the downhole tool structure, determining a failure identification threshold of the downhole tool structure, and judging downhole tool structure reliability. The application can accurately and timely warn downhole tool failure by recording cumulative load of the downhole tool structure failure through the downhole load working condition time sequence table, reduce the downhole tool failure probability of ultra-deep wells, and comprehensively improve the safety and reliability of drilling engineering.

This invention proposes an intelligent prediction method for performance degradation and durability limit state assessment of reinforced concrete structures, comprising: establishing a probabilistic model of random variables for environmental and structural parameters; constructing an intelligent prediction model for chloride ion diffusion by fusing convolutional neural networks, and using Monte Carlo sampling analysis to analyze the probability distribution of the initial corrosion time of the reinforcing bars to predict the depassivation time of the reinforcing bars; establishing a time-varying corrosion model of the reinforcing bars to obtain the change of the corrosion loss rate of the reinforcing bars over time; further, using an incremental static analysis method, establishing a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions through random sampling; obtaining the critical load value under the limit state to obtain the failure probability curve under different corrosion degrees; and finally, by defining a bearing capacity reduction coefficient, obtaining the bearing capacity failure time of the reinforced concrete structure to achieve the durability limit state assessment of the reinforced concrete structure under corrosion.

A comprehensive safety evaluation method, system, equipment, and storage medium for gas storage injection and production wells includes the following steps: S1, calculating the effective internal and external pressures, remaining internal pressure resistance, and external extrusion resistance of the gas storage injection and production well casing to evaluate the remaining strength of the casing; S2, calculating the failure probability of the gas storage injection and production well casing and cement sheath, generating curves showing the variation of casing failure probability with service life and cement sheath failure probability with formation pressure to measure the reliability of the gas storage injection and production wells; S3, predicting the failure probability of gas storage injection and production wells by reading and learning failure probability prediction sample data; S4, calculating and generating curves showing the variation of corrosion rate of gas storage injection and production wells with service life to predict the remaining life of the gas storage injection and production wells, using the prediction results to control the service life of the gas storage injection and production well casing, and providing a decision-making basis for whether to seal or modify the gas storage well.

This invention relates to the field of disaster prevention and mitigation technology for marine engineering structures, and discloses a method for plotting vulnerability curves of guide seawall structures based on random load combinations. The method includes: constructing a nonlinear finite element model of a deep-water guide seawall; introducing a two-dimensional phase coupling vector to generate random dynamic load samples containing temporal coupling characteristics of earthquakes and waves; inputting the samples into the model, tracking the structural frequency drift in real time and considering pore flow abrupt change damping, and calculating the multidimensional dynamic response of the structure; constructing a multidimensional dynamic limit state surface that shrinks with cumulative plastic damage, and determining the failure state based on the relative relationship between the dynamic response and the state surface; estimating the failure probability using extreme value statistics theory, and establishing a probabilistic demand model containing cross-coupling terms of earthquake and wave intensity; and plotting a vulnerability surface describing the change of the failure probability with bivariate strength indices based on this model. This invention can accurately assess the dynamic reliability of deep-water guide seawalls under the coupling effects of multiple disasters.

This invention discloses a method for obtaining the wind-induced vulnerability curve of transmission towers based on failure probability, relating to structural engineering technology. The method includes the following steps: establishing a probability distribution model of the extreme response of the transmission tower to wind load; constructing a probability distribution model of the structural bearing capacity of the transmission tower; obtaining the failure probability based on vulnerability analysis using Latin hypercube sampling; plotting vulnerability curves with wind speed as the horizontal axis and failure probability as the vertical axis, drawing curves under different failure criteria to obtain vulnerability curves based on failure probability; performing vulnerability analysis in conjunction with engineering estimates to obtain the loss rate; plotting vulnerability curves; and finally, plotting a curve with wind speed as the horizontal axis and loss rate as the vertical axis to obtain vulnerability curves based on engineering estimates. This invention's method is characterized by its high efficiency in analytical calculation and is suitable for analyzing the failure probability and loss rate of transmission towers in engineering contexts.

This invention discloses a method and system for calculating spare parts demand in subway operation and maintenance based on health status prediction, belonging to the field of intelligent operation and maintenance technology. The method includes: generating a failure probability distribution based on component health prediction; simulating concurrent failure scenarios and their probabilities of occurrence through sampling; constructing a virtual spare parts space; calculating train operation value entropy based on real-time operation data to determine resource allocation priorities; simulating high-value priority resource allocation within the virtual space; calculating competition penalty factors to derive collaborative spare parts demand under a single scenario; constructing a macroscopic time-varying demand field by combining scenario occurrence probabilities and single-scenario demands; performing time-domain rolling optimization under dynamic window constraints; and finally outputting an adaptive dynamic spare parts demand list. This achieves an intelligent scheduling closed loop from underlying probability prediction to global dynamic optimization.

The invention discloses a pressurized water reactor fuel assembly damage position prediction method based on machine learning, and the method comprises the steps: building and training a machine learning model through a decision tree algorithm based on time evolution data of damaged primary loop nuclide concentration obtained by reactor core physical analysis software and evolution simulation calculation software; according to the method, a preprocessed multi-dimensional nuclide concentration stable value feature vector is taken as input, a prediction result of a damage position is taken as output, model overfitting is effectively controlled through pre-pruning and post-pruning strategies, generalization ability is ensured, the damage probability of each position can be provided, and prediction of the damage position of the pressurized water reactor fuel assembly is realized.

The application discloses a tank normal-pressure management platform multi-dimensional risk assessment system and particularly relates to the technical field of tank safety risk assessment, and the system comprises data acquisition, parameter preprocessing, multi-dimensional dynamic coupling calculation, risk grade assessment, visual output and model optimization feedback modules. The multi-dimensional dynamic coupling calculation module is internally provided with four units, namely, corrosion rate dynamic calculation, failure probability coupling analysis, overhaul cycle rolling optimization and foundation settlement layered evaluation. A dynamic coupling closed loop architecture is formed between the units through bidirectional data flow. The application realizes the collaborative analysis and dynamic optimization of corrosion rate, failure probability, overhaul cycle and settlement risk by constructing a multi-dimensional dynamic coupling assessment system, solves the problems of single assessment dimension and static calculation model in the prior art, significantly improves the risk assessment accuracy and the scientific nature of maintenance decision, and can be applied to the safe operation monitoring and maintenance management of normal-pressure tanks in the petroleum and chemical industries.

This invention relates to the fields of video processing, copyright protection, and digital forensics, specifically a pirated video tracing system based on video decoding. The system includes a data acquisition module, a status assessment module, a strategy decision-making module, and a tracing execution module. It acquires video stream data and decoding pipeline status data, calculates the computing power overdraft rate, and predicts the decoding failure probability. The system compares the decoding failure probability with a preset failure probability threshold to generate a downgraded tracing instruction or a regular tracing instruction. Based on this, it performs low-computing-power tracing feature extraction or deep tracing feature extraction on the video stream data, and sends the obtained target tracing features to a tracing comparison terminal for comparison, outputting the tracing location result. This invention achieves a balance between tracing tasks and decoding stability, and reduces the risk of playback failure caused by continuous tracing operation.

This invention discloses a dimension-reduction reliability assessment method for switched reluctance motors (SRMs) that considers repairability. This method analyzes the faults of the SRM at structural levels, comprehensively converting the failure probabilities of each underlying component into overall system state transition parameters. Based on this, the repairability rate is calculated according to the actual periodic maintenance cycle of the equipment, and a Markov state transition equation system containing maintenance recovery paths is established. To avoid complex calculus operations involving high-order matrices, this invention utilizes Laplace transform to convert the dynamic process into algebraic calculations in the complex frequency domain, achieving analytical order reduction of the model. By performing algebraic analysis on the complex frequency domain function, the dynamic reliability function and mean time between failures (MTBF) of the system are directly obtained. This invention overcomes the shortcomings of traditional models, such as cumbersome computation and unrealistic lifespan predictions. While effectively reducing the computational complexity of microcontrollers, it significantly improves the accuracy of lifespan prediction for motor drive systems and enhances their engineering application value.

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 invention belongs to the field of gas pipeline failure prediction and analysis technology, and specifically relates to a method and system for dynamically predicting the emergency material demand of gas pipelines. The method includes: collecting gas pipeline failure data; calculating the pipeline failure probability based on the data; establishing a demand prediction model based on the failure probability; using the demand prediction model to predict the emergency material demand for gas pipelines, generating an initial prediction result; and optimizing the initial prediction result to form a target prediction result. This invention dynamically adjusts the predicted emergency material demand for gas pipelines based on the production management foundation of gas operating enterprises, meeting the needs of different gas operating enterprises and possessing strong practical applicability.

This invention discloses a maintenance and management system for gas facilities and equipment, relating to the field of gas equipment management technology. It includes: an information acquisition module for real-time acquisition of operational data from gas facilities and equipment; a fault-prone equipment identification module for cluster analysis of historical maintenance records to identify fault-prone equipment; an equipment status assessment module for constructing an equipment status assessment model, inputting the real-time acquired operational data from the gas facilities and equipment into the model to obtain the current failure probability of the equipment; a maintenance management module for calculating the maintenance priority of each gas facility and equipment, sorting maintenance tasks from highest to lowest priority, and executing them in the sorted order; and a path planning module for using a path planning algorithm to output optimal path information and executing tasks according to the planned optimal path, thus achieving comprehensive and precise management of gas facilities and equipment.

This invention provides a method and system for optimizing the layout of petrochemical plants based on domino accident analysis. The method identifies all objects requiring domino accident analysis based on influencing factors; it determines the cumulative escalation probability of domino accidents by considering the synergistic coupling effect of equipment analysis on accident escalation probability, combined with the reliability and failure probability of safety barriers; it forms domino accident correlation evolution paths under multiple accident escalation vectors, and establishes a domino accident evolution physical model using node graph technology; it then dynamically simulates and calculates the escalation state and impact range of domino accidents; and it optimizes the layout based on a comprehensive analysis of the cumulative escalation probability, domino accident escalation state, and impact range data, identifying applicable safeguards. This approach overcomes the problems of unclear research criteria, incomplete analysis factors, and limited application scenarios in existing technologies. Based on domino accident consequence assessment modeling technology, it proposes plant facility layout optimization and blocking strategies.

This invention belongs to the field of axial flow pump control technology, specifically relating to an intelligent optimization control method and system for axial flow pump operation. The method includes: constructing and fusing a Gaussian process regression surrogate model corresponding to the physical mechanism and operating data; the model synchronously outputting performance prediction values ​​and prediction uncertainties; calculating the state entropy corresponding to the current operating condition based on the surrogate model; performing heuristic pruning by using the surrogate model to estimate the change in state entropy; the system selecting a subset of candidate rules and calling the surrogate model to infer and calculate the comprehensive gain value; the inference depth decreasing as the prediction uncertainty increases; establishing a penalty mechanism that applies penalties to the gain value based on historical failure probabilities and the current state entropy value; and the system determining the optimal control rule and issuing commands based on the adjusted gain value. This invention can reduce the underlying computing power consumption, prevent long-term error accumulation, prevent repeated equipment oscillations, and ensure the long-term efficient operation of the axial flow pump.

The application discloses a kind of air-ground coordinated unmanned aerial vehicle operation risk assessment method, facing unmanned aerial vehicle city operation scene, belong to unmanned aerial vehicle risk assessment field.First, based on system failure and track error probability density distribution, respectively calculate unmanned aerial vehicle operation system failure and collision failure probability;Second, based on kinematic model, solve the probability density distribution function of the predicted crash point of unmanned aerial vehicle under ballistic descent condition;Finally, use personnel damage, economic loss and noise influence multiple indexes to evaluate the operation risk of unmanned aerial vehicle.The application faces the city operation risk scene of fine modeling, can calculate the instantaneous operation risk of unmanned aerial vehicle, to provide corresponding support to its operation planning in city operation scene.

The invention relates to an adaptive method for the preventive maintenance of a vehicle comprising a part with a plurality of sensors, the method comprising the following steps: - a measurement step (E11) of a plurality of parameters during an observation period by the plurality of sensors; - a calculation step (E12) of a damage estimation value for the part from the plurality of parameters measured during the observation period; - a calculation step (E13) of a failure probability value for the part from the calculated damage estimation value; - a signaling step (E14) of the part when the calculated failure probability value exceeds a predetermined probability threshold value in order to perform preventive maintenance on the vehicle. Figure 1

The application discloses a failure probability prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining product parameters corresponding to a target product, constructing a product simulation model corresponding to the target product according to the product parameters, the target product comprising a screen module, and the screen module comprising one or more components; performing drop simulation testing on the product simulation model based on one or more drop directions to obtain simulation test results corresponding to each drop direction; the simulation test results comprising maximum strain data corresponding to each component and a target component area corresponding thereto; analyzing the simulation test results corresponding to each drop direction by using a constructed failure probability prediction model to obtain a target failure probability corresponding to the target product. Through the failure probability prediction method, device, electronic equipment and storage medium, the probability of display screen breakage of the electronic equipment in the case of drop can be accurately evaluated.

A reservoir safety monitoring and early warning method based on multi-source heterogeneous data fusion is characterized by the following steps: constructing a multi-dimensional reservoir safety risk assessment index system to comprehensively assess reservoir safety; setting a quantitative reservoir safety risk level classification formula based on safety status score, failure probability, and severity of consequences; collecting and fusing multi-source heterogeneous data, and preprocessing the collected raw data; constructing a dynamic prediction model that integrates linear regression and machine learning to predict future values ​​of key safety indicators; assessing comprehensive safety risks; and issuing dynamic graded early warnings to achieve real-time, accurate, and comprehensive perception, intelligent diagnosis, and graded early warning of reservoir safety status.

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 application relates to a risk assessment method and device for an urban lifeline pipe network, and relates to the field of urban pipe network system safety monitoring; the method can accurately quantify pipe network risks and provide data support for urban risk decision-making. The method comprises the following steps: acquiring attribute parameters of each pipe section in a pipe network system and soil body attributes, and constructing a finite element model of the pipe network system; acquiring seismic motion records of a region where the pipe network system is located, performing dynamic analysis based on amplitude-modulated acceleration time histories, and obtaining stress data of the pipe sections under different seismic motion peak accelerations; determining the damage levels of the pipe sections through the stress data, and counting the number of times when the damage levels reach preset levels to obtain failure probabilities of the pipe sections; training a machine learning agent model through the seismic motion records and the failure probabilities, and determining the failure probabilities of to-be-measured pipe sections through the model; determining importance indexes of the to-be-measured pipe sections through a weighted graph theory model; and determining risk assessment results of the to-be-measured pipe sections based on the failure probabilities and the importance indexes.