28 results about "Failure assessment" patented technology

The invention relates to the technical field of offshore wind turbine generator operation and maintenance, and discloses a failure evaluation system based on an offshore wind turbine generator. Comprising a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system obtains parameters such as vibration and temperature of the wind turbine generator in real time, after preprocessing, failure features are extracted through a deep convolutional neural network, and a dynamic Bayesian network model is constructed to calculate the component failure probability. An optimal maintenance path is generated by using an improved ant colony optimization algorithm, and meanwhile, a dynamic risk assessment module, an abnormal signal detection module and a maintenance strategy optimization module are arranged. The method can accurately evaluate the unit failure risk, timely discover abnormity, optimize the maintenance decision, improve the operation reliability of the offshore wind turbine generator, reduce the operation and maintenance cost, and guarantee the stability and high efficiency of offshore wind power generation.

A multi-material gradient interface microscopic crack path prediction method comprises the following steps: S1, according to material similarity or geometric adjacency, orienting to form an edge, and generating an interface diagram; s2, extracting an energy release rate, a principal stress gradient and a temperature; s3, the three field quantities are mapped back to the graph through KD-Tree nearest neighbor and differentiable Gaussian interpolation, and node and edge embedding features are obtained; s4, the temperature-stress-energy cascade gating message passing network calculates a crack initiation probability and an edge probability, and extracts a crack path according to the maximum cumulative probability; s5, the path is gridded into an indication tensor, and an error gradient for the probability and the energy density is generated; s6, synchronously correcting the network weight and the local energy density according to the gradient, and circulating the steps S2 to S5 until the score is converged; s7, freezing the weight, screening crack initiation nodes, performing spline smoothing on a path, formatting an energy field and a reliability label, and outputting the energy field and the reliability label; according to the method, high-precision failure evaluation is obtained with less calculation amount, and the crack initiation position, the three-dimensional crack curve and the reliability level are output.

The invention discloses a crack tolerance analysis method for a saddle-shaped welding seam of an integral reinforcing connecting pipe of nuclear pressure-bearing equipment, and belongs to the technical field of reliability. The method comprises the following steps: firstly, establishing a three-dimensional model containing a crack structure and defining crack parameters; carrying out finite element analysis after applying a load; and the critical crack size is determined by iteratively adjusting the crack depth in the failure evaluation diagram. Carrying out sampling analysis by taking the load and the material parameters as random variables to obtain a critical crack size set; and finally, the failure probability reaches a target allowable value by adjusting a crack detection threshold value of nondestructive testing. According to the method, finite element simulation is carried out, the critical crack size of the saddle-shaped welding seam of the integral reinforcing connecting pipe of the pressure-bearing equipment under the complex load is checked based on a failure evaluation icon, the relation between the critical crack size and the allowable crack size is established by assuming that the crack size detected by nondestructive testing obeys normal distribution, and the test efficiency is improved while the number of experiments is reduced. And the reliability of a calculation result is ensured.

The invention relates to a safety assessment method for pressure-bearing special equipment with crack defects based on health monitoring and digital twinning. Various sensors are installed at key parts of the equipment to collect running state data in real time, a parameterized digital reconstruction technology is adopted to establish high-precision twin bodies of the equipment, and stress distribution and crack propagation trend of the structure are automatically simulated and analyzed by means of cloud computing. A two-stage Bayesian optimization algorithm is innovatively used to realize self-correction of simulation result errors, and adaptive accurate mapping of a digital model to physical equipment is realized. And in a crack defect-containing area, an instability critical dimension prediction model is constructed based on an extended finite element method and a failure evaluation diagram technology, and the crack instability critical dimension is dynamically calculated. According to the invention, real-time, efficient and automatic safety assessment and early warning can be carried out on pressure-bearing special equipment, and scientific data support and decision basis are provided for safe operation and maintenance of the equipment.

The invention relates to optimized handling of adjacent plane interference problems, and provides a data storage device and a method. The memory blocks of the meta-block are analyzed and tested to determine whether the plurality of memory blocks have not passed the first failure assessment. If more than one memory block does not pass the first failure assessment, the memory blocks are allocated to a retry pool. When in the retry pool, a second fault assessment is performed on the memory block. If one or more of the memory blocks pass the second failure assessment, the memory blocks are placed in a pool of memory blocks and relinked to other memory blocks to form a new meta-block. However, if one or more of the memory blocks do not pass the second failure assessment, the memory blocks are withdrawn.

The application relates to the technical field of offshore wind turbine operation and maintenance, and discloses a system for evaluating the failure of an offshore wind turbine, which comprises a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system can acquire parameters such as the vibration and temperature of the wind turbine in real time, extract failure features by using a deep convolutional neural network after preprocessing, and calculate the failure probability of components by constructing a dynamic Bayesian network model. An improved ant colony optimization algorithm is used to generate an optimal maintenance path, and a dynamic risk evaluation module, an abnormal signal detection module and a maintenance strategy optimization module are simultaneously provided. The system can accurately evaluate the failure risk of the unit, timely discover abnormalities, optimize maintenance decisions, improve the operation reliability of the offshore wind turbine, reduce operation and maintenance costs, and ensure the stable and efficient operation of offshore wind power generation.

The present invention relates to a method for predicting and analyzing corrosion failure of naval equipment, and the specific steps are: 1. Analysis and judgment of corrosion failure modes; 2. Determination of statistical functions of naval equipment under typical corrosion failure modes; 3. Analysis of life function based on MATLAB: through the first and second analyses, and through test data or actual ship data, the corrosion basic parameters of the analysis object are obtained, the Weibull distribution function is analyzed, and the statistical laws of corrosion failure are obtained through numerical analysis, and then prediction and judgment are carried out. The method for predicting and analyzing corrosion failure of naval equipment provided by the present invention is based on experimental test data and software analysis. Compared with existing prediction methods, the prediction results are more accurate and can be used for systematic analysis of corrosion failure assessment under typical corrosion failure modes of key systems of naval equipment, providing key step support for life prediction, failure risk prediction, accelerated testing and process monitoring and monitoring, and long-term failure mode correction of equipment during its service period.

The present disclosure relates to a power device failure evaluation method and device, the method comprising: obtaining an operating state parameter of the power device; determining whether a stress event occurs to the power device based on whether the operating state parameter meets a first preset condition, and recording a stress parameter corresponding to the stress event; determining whether a stress failure occurs to the power device based on whether a duration of the stress event exceeds a preset time; in the case that the stress failure does not occur to the power device, calculating an aging characteristic parameter of the power device based on the operating state parameter; and determining whether an aging failure occurs to the power device based on whether one of the aging characteristic parameter and the stress parameter meets a corresponding second preset condition. The present disclosure can evaluate the stress failure and the aging failure of the power device occurring in the actual operation process.

This application discloses a method and apparatus for determining the grade of a non-volatile memory chip. The method includes: acquiring current test data of frequency, capacity, and timing of the non-volatile memory chip, as well as failure assessment test data; determining a dynamic weighting matrix based on the usage scenario information of the non-volatile memory chip; mapping the current test data to a pre-constructed first chip feature space based on the dynamic weighting matrix and calculating the corresponding likelihood probability; determining the current data score grade based on the likelihood probability; calculating a personalized degradation rate parameter based on the failure assessment test data and a pre-constructed chip degradation trajectory curve; determining the failure assessment data based on the personalized degradation rate parameter; and determining the comprehensive grade corresponding to the current data score grade and the binary grading interval to which the failure assessment data belongs as the target comprehensive grade of the non-volatile memory chip. This technical solution achieves automated, multi-dimensional, and high-precision grade classification of non-volatile memory chips.

The invention belongs to the technical field of riser system risk assessment, and discloses a drilling riser system failure assessment method and related device.The drilling riser system failure assessment method comprises the steps that uncertain factor sources of a riser system are obtained; data sampling is carried out on uncertain factor sources of the riser system, and a preset dynamic response index calculation value of the riser system under the storm condition is obtained through analysis; the failure state is judged, and an uncertainty factor source which has the most obvious influence on the vulnerability of the riser system under the extreme storm condition is obtained through analysis; carrying out data sampling on an uncertain factor source which has the most obvious influence on the vulnerability of the marine riser system under the extreme storm condition, and analyzing to obtain a preset dynamic response index calculation value of the marine riser system under the extreme storm condition; the failure state is judged, and uncertain factor indexes which have the most remarkable influence on the vulnerability of the riser system are obtained through analysis; the method breaks through the limitation that a traditional failure assessment method only pays attention to environmental loads, and ensures the accuracy of a failure assessment result.

This invention belongs to the field of rotating machinery condition monitoring and fault diagnosis technology. It proposes a bearing failure assessment method and system. The method involves acquiring bearing vibration signals and performing mean and absolute value removal processing to obtain the signal to be processed. Next, a lower threshold for the tail is determined, and a set of tail samples with amplitudes greater than this threshold is selected. Then, a soft-truncated power-law distribution model is constructed based on the tail sample set, and the distribution parameters, including the power-law tail exponent, truncation scale parameter, and truncation smoothing exponent, are solved using maximum likelihood estimation. Furthermore, an analytical expression for the improved Gini index is constructed based on the distribution parameters, and the bearing degradation degree index is calculated. Finally, a bearing degradation curve is constructed based on the degradation degree index for different time periods. This invention accurately characterizes the tail impact features of vibration signals using a soft-truncated power-law model and quantifies the degradation degree using an improved Gini index, effectively assessing the health status and failure trend of rolling bearings.

This invention relates to the field of flexible riser risk assessment technology, and more particularly to a method for risk assessment of marine dynamic flexible risers based on fuzzy rule inference networks. The method includes: acquiring a set of historical failure data for flexible risers; conducting a failure assessment on the historical failure data set based on a five-level risk scoring standard to obtain a first assessment result; performing feature fusion processing on the first assessment result based on a preset expert weight standard to obtain a second assessment result; obtaining fuzzy feature values ​​based on a Gaussian fuzzy mechanism and the second assessment result; inputting the fuzzy feature values ​​into a preset fuzzy rule inference network model to obtain a fuzzy set output by the fuzzy rule inference network model; and defuzzifying the fuzzy set to obtain the flexible riser risk assessment result. This invention, through the processing of a fuzzy rule inference network model, can effectively handle the multi-factor nonlinear coupling effects in flexible riser systems, thereby improving the accuracy of risk assessment.

The invention relates to the technical field of flexible riser risk assessment, in particular to an ocean dynamic flexible riser risk assessment method based on a fuzzy rule inference network, which comprises the following steps: acquiring a historical failure data set of a flexible riser, performing failure assessment on the historical failure data set based on a five-level risk scoring standard to obtain a first assessment result, and performing risk assessment on the first assessment result to obtain a second assessment result; and performing feature fusion processing on the first evaluation result based on a preset expert weight standard to obtain a second evaluation result, obtaining a fuzzy feature value based on a Gaussian fuzzy mechanism and the second evaluation result, inputting the fuzzy feature value into a preset fuzzy rule reasoning network model to obtain a fuzzy set output by the fuzzy rule reasoning network model, and obtaining a fuzzy set output by the fuzzy rule reasoning network model. And performing defuzzification processing on the fuzzy set to obtain a flexible riser risk assessment result. According to the method, the multi-factor nonlinear coupling effect in the flexible riser system can be effectively processed through fuzzy rule reasoning network model processing, so that the accuracy of risk assessment is improved.

The application discloses a power station boiler high-temperature component with defects remaining life calculation method, device, equipment and medium, relates to the field of boiler risk analysis, and the method comprises the following steps: according to the basic information of the power station boiler component with defects, determining the initial material yield strength, the reference stress at the defect position, the load ratio, the fracture ratio, the creep damage before the defect is generated, the load ratio cutoff line and the failure evaluation curve; when the initial state of the defect is safe, the entire service cycle of the component with defects is divided into multiple time load steps, the creep crack propagation damage, the load ratio and the fracture ratio of each time load step are calculated; and when a certain time load step does not pass the safety assessment, the remaining service life of the component with defects is determined according to the cracking time corresponding to the time load step and the running time of the component with defects. The application improves the calculation accuracy and efficiency of the remaining service life of the power station boiler high-temperature component with defects.

This invention discloses a failure assessment method, system, and device for spiking neural network circuits, relating to the field of spiking neural network circuit technology. The method includes: injecting a pre-set fault into the spiking neural network unit of the spiking neural network circuit to change the stability of the spiking neural network circuit, obtaining a faulty spiking neural network circuit; then, based on a pre-set sample set, calculating the first accuracy of the spiking neural network deployed in the spiking neural network circuit before the fault injection, and the second accuracy of the spiking neural network in the faulty spiking neural network circuit after the fault injection; finally, optimizing the spiking neural network circuit based on the first accuracy, the second accuracy, and a pre-set optimization strategy. This solves the technical problem that existing neural network circuit evaluation and optimization methods cannot effectively improve the reliability of the spiking neural network circuit itself.

The invention relates to the technical field of bridge steel structure anti-breaking design, and particularly discloses a method for calculating the minimum acceptable CTOD value of high-strength bridge steel, which comprises the following steps: carrying out a steel tensile test at the minimum service environment temperature of a bridge structure to obtain the mechanical property of the steel at the corresponding temperature, and drawing a failure assessment diagram FAD; assuming the possible defect form and size of the bridge steel member, considering the maximum service load of the bridge steel member, determining the design stress, calculating the abscissa value Lr'of the failure assessment graph FAD, calculating the ordinate value Kr 'corresponding to the Lr', and regarding the points (Lr 'and Kr') as the critical points of the fracture failure of the bridge steel member; according to the method, the minimum acceptable CTOD value of the steel under the corresponding service condition can be calculated under different working conditions, the fracture toughness level of the steel can be evaluated more visually and conveniently, and anti-fracture design of a bridge steel structure is facilitated.

This invention provides a method for assessing internal failure of orthotropic laminates based on strain measurement, relating to the field of orthotropic composite materials technology. The method includes the following steps: S1, fabricating a strain gauge test model of the laminate; S2, connecting the strain gauge test model to a data acquisition instrument, conducting a working condition test on the structure under test, and using a host computer to calculate the stress in each layer and principal direction of each angle within the laminate; S3, acquiring the strength limit of the orthotropic single-layer laminate material, and based on the strength limit of the orthotropic single-layer laminate material and the stress in each layer and principal direction of each angle within the laminate, performing failure assessment for each ply at different angles. Due to the special characteristics of orthotropic laminates, different ply failures can occur under different stress states. This invention provides a method that can calculate the failure assessment of ply in different directions within the laminate structure based on the surface strain measurement values ​​of the composite laminate.

The embodiment of the invention provides a vehicle function failure assessment method and system and a vehicle, and relates to the technical field of function failure probability assessment and automatic driving. The method comprises the steps that initial scene data corresponding to a vehicle are acquired, and the initial scene data are used for representing a real-time scene where the vehicle is located; performing parameter dimension reduction processing on the initial scene data to obtain target scene data; based on target failure probability distribution, adaptive sampling is carried out on the target scene data, a target sampling result is obtained, and the target failure probability distribution is used for representing scene information corresponding to a function failure event of the vehicle; and performing evaluation calculation according to the target sampling result to obtain the function failure probability of the vehicle in the real-time scene. According to the method and the device, the technical problems of low efficiency and low accuracy of a vehicle function failure evaluation method caused by dependence on a Monte Carlo method in related technologies are solved.

This invention belongs to the field of power equipment condition monitoring and fault diagnosis technology, and discloses a method and system for online monitoring and failure assessment of the pressure of switchgear louver contacts. The method includes: real-time acquisition of temperature data and raw pressure data of the louver contacts; temperature compensation of the raw pressure data based on a temperature-pressure compensation model to obtain compensated pressure data; inputting the compensated pressure data into a KNN regression intelligent diagnostic model to calculate a comprehensive three-dimensional index H and determine the health status level. The system includes a sensing unit, a data transmission unit, and a monitoring and evaluation unit. This invention eliminates the interference of temperature on pressure measurement through temperature compensation, and combines multi-sensor three-dimensional pressure sensing with a KNN regression model to achieve accurate classification and early warning of the health status of the louver contacts, improving the accuracy and reliability of online monitoring.

The invention discloses a buried oil and gas pipeline damage failure assessment method, system, equipment and medium under an explosion load, and belongs to the technical field of pipeline damage detection. The maximum diameter and the minimum diameter of the sunken section of a pipeline within a safety range threshold after a simulation buried oil and gas pipeline explosion experiment are acquired; calculating a safety critical value of a pipeline section critical safety deformation ratio; establishing a mapping relation between the safety deformation ratio and the overpressure P and the impulse I of the explosive shock wave; based on the safety critical value, multiple sets of different overpressures P are applied to the pipeline in a simulation mode, multiple sets of impulses I are calculated, then a P-I curve is obtained through fitting, and a pipeline damage failure evaluation model is established according to the P-I curve; according to the method, the overpressure P1 under the actual explosion load is obtained, the corresponding impulse I1 is calculated, the coordinate positions of the P1 and the I1 in the pipeline damage failure evaluation model are positioned, the failure condition of the current buried oil and gas pipeline is rapidly judged, precious time is gained for timely countermeasures, and safety risks and economic losses possibly caused by pipeline failure can be effectively reduced.

The application discloses a kind of drilling tool structural member downhole load real-time acquisition method, belong to petroleum drilling technical field, comprising the following steps: a, according to different kinds of working condition to time series data working condition annotation;B, calculate the full angle change rate, form time series table;C, the working condition is divided into a kind of working condition and two kinds of working condition, to a kind of working condition acquisition data, fusion forms well load a kind of working condition time series table, to two kinds of working condition acquisition data, fusion forms well load two kinds of working condition time series table;D, fusion well load a kind of working condition time series table and well load two kinds of working condition time series table, form well load working condition time series table, obtain the downhole load of drilling tool structural member by well load working condition time series table.The application can accurately identify the downhole actual load of drilling tool structural member in real time, provide research basis and analysis foundation for carrying out drilling tool structural member failure assessment, beneficial to improve drilling stability.

The invention relates to a gear gluing failure real-time evaluation system and method based on a star sensor network, and belongs to the technical field of gear gluing failure evaluation. Comprising a test gear box, a temperature sensor, a vibration sensor, a box body monitoring node, a gear monitoring node and a test gear which are arranged on a shaft of the test gear box, a relay node and an upper computer carrying Qt design software, the system is used for converging and forwarding data collected by the gear monitoring node and the box monitoring node, a star-shaped wireless sensor network topological structure is formed through Wi-Fi modules configured by the gear monitoring node, the box monitoring node, the relay node and the upper computer, signals are collected synchronously, the service states of gears, bearings and gear boxes can be reflected accurately, and the service quality of the gears, the bearings and the gear boxes is improved. The star-shaped wireless sensor network has flexible networking capability, non-unpacking judgment of the gear gluing bearing capability test is realized by observing whether the change of a multi-dimensional signal characteristic value exceeds a specific threshold value or not, and the test efficiency is improved.

The invention provides a method and system for evaluating air hole defects in a ship welding structure, and the method comprises the steps: obtaining the feature information of the air hole defects in an actual ship welding structure; according to an actual ship welding structure, constructing a three-dimensional welding structure model comprising pore defects; calculating a fatigue stress range of the pore defect; according to the fatigue stress range and the material performance parameters of the ship welding structure, the crack propagation behavior of the pore defect is calculated through a crack propagation model; generating a failure evaluation curve according to the material performance parameters; based on the crack propagation behavior, calculating evaluation parameters under different cycle times; and comparing the evaluation parameters under different cycle times with the failure evaluation curve, and judging the influence of the pore defect on the structural safety in combination with the designed life.

The present invention discloses a method and system for testing the withstand voltage performance of LCD logic board substrates. The method comprises the following steps: pre-processing a sample of the LCD logic board substrate; configuring a multi-stage stepped voltage loading sequence; installing a distributed electrode array; performing a dynamic withstand voltage test and recording test parameters, including inter-electrode leakage current and current fluctuation characteristic parameters, local temperature rise distribution data of the LCD logic board substrate sample, and surface deformation of the LCD logic board substrate sample; constructing a composite failure assessment model based on the test parameters and outputting a withstand voltage stability coefficient; and grading withstand voltage performance based on the withstand voltage stability coefficient. The present invention has the following advantages and effects: it can simultaneously monitor multiple electrical, thermal, and mechanical parameters and establish a core detection method for a dynamic failure quantification model, ultimately improving the accuracy and reliability of the withstand voltage performance test results of LCD logic board substrates.

This invention discloses a method and system for urban planning optimization based on multi-agent simulation, relating to the field of urban planning technology. By continuously tracking changes in the distribution structure of multi-agent behavior during simulation, if the discreteness sequence Div shows a sudden increase or continuous expansion trend at a certain time point, it is accurately located using the consistency collapse candidate point Cbr. Furthermore, the consistency collapse criterion Brk is used to determine whether the planning scheme has entered an uncontrollable state. Thus, the failure assessment result Evl is output before the actual implementation of the plan. This can guide planners to make targeted adjustments to the planning scheme without changing hardware conditions, avoiding real-world problems such as localized congestion, functional imbalance, or fragmented group behavior after the plan is implemented. This significantly improves the stability, controllability, and long-term adaptability of urban planning schemes in real-world operating environments.

The invention provides a coal wharf intelligent control system failure assessment method and device, and the method comprises the steps: setting a basic window length according to an equipment type, calculating a window length offset through combining historical fault data, and obtaining an optimized window length; extracting time sequence characteristics such as an autocorrelation coefficient and a partial autocorrelation coefficient from the standardized time sequence data corresponding to the optimized window length; and determining a health index and a residual service life prediction value according to the time sequence characteristics by utilizing a long-short-term memory network and a prediction model of a Transform structure. According to the method, the window length is dynamically calculated and optimized, the key time sequence characteristics are extracted, and the specific prediction model is adopted, so that the real-time and accurate evaluation on the failure of the intelligent control system of the coal wharf is realized, and the defects of the existing method in the aspects of processing the dynamic evolution and prediction splitting of multi-source heterogeneous data are effectively overcome.

The present invention belongs to the field of pipeline failure assessment and relates to a dual-competition failure assessment method based on pipe body strain and girth weld fracture toughness. The present invention is based on a high-grade steel pipeline with girth weld cracks under the action of landslide. The critical index of the fracture toughness J-integral of the girth weld material of the high-grade steel pipeline is determined through a three-point bending test. A calculation model of the structural strain and fracture toughness of the high-grade steel pipeline with girth weld cracks under the action of landslide is established. The influence of pipeline, crack and landslide parameters on strain and J-integral is determined. The particle swarm optimization (PSO) and long short-term memory neural network (LSTM) mosaic algorithm (PSO-LSTM) are used to respectively establish pipe body strain and girth weld crack J-integral prediction models. Finally, a dual-competition failure assessment method based on pipe body strain and girth weld fracture toughness is established, and a series of pipeline safety evaluation maps are formed. The present invention forms a landslide pipeline failure assessment method integrating pipe body strain and girth weld fracture toughness, optimizes the dual-competition failure assessment method for landslide pipelines, and is field-verified to be able to scientifically and accurately assess the safety status of landslide pipelines, with strong practicality.