284 results about "Reliability model" patented technology

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

The invention relates to a hydrogen fuel cell system reliability evaluation method based on a Weibull distribution model, and the method comprises the steps: collecting the state detection quantity of a hydrogen fuel cell system in real time, and carrying out the calculation to obtain a wear index and a functional age; historical failure data of the hydrogen fuel cell system are obtained, and shape parameters and scale parameters are obtained through calculation; based on the wear index, the functional age, the shape parameter and the scale parameter, a Weibull distribution reliability model is constructed and used for calculating the cumulative failure probability and the aging failure rate; and determining the failure risk and the residual life of each component in the hydrogen fuel cell system based on the accumulated failure probability and the aging failure rate in combination with the improved risk priority. Compared with the prior art, the method has the advantages that the problems of single parameter evaluation, static analysis and inadequate individuation in the prior art are solved, dynamic, intelligent and systematic reliability evaluation is realized, and scientific support can be provided for full-life-cycle management and maintenance optimization of the hydrogen fuel cell system.

The invention relates to the field of automobile hubs. An automobile hub reliability analysis method combining fatigue failure and impact degradation failure is characterized by comprising the following steps that firstly, fatigue failure analysis is conducted on an automobile hub, and the fatigue life is predicted, specifically, load characteristic analysis is conducted on dangerous parts of the automobile hub based on a road program stress spectrum; the fatigue life of the automobile hub under the actual use condition is predicted, and related physical characteristics and characteristic parameters of the automobile hub are obtained; then, considering the mutual influence of degradation failure and impact failure of the automobile hub, establishing a time-varying failure threshold model of the degradation failure and the impact failure according to the load characteristic analysis of the dangerous part of the automobile hub, and finally establishing an impact-degradation competitive failure reliability model based on a time-varying failure threshold in combination with the time-varying failure threshold model. And a maximum likelihood estimation method is adopted to estimate residual model parameters, and an automobile hub reliability analysis result combining fatigue failure and impact degradation failure is obtained.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

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

The invention relates to the field of gears. A residual stress intensity factor is introduced into a Walker model, the influence of residual stress on the crack propagation residual life is analyzed, then a crack closing coefficient is introduced to correct the Walker model, and the bending fatigue life of the gear is obtained. The roughness and the dynamic meshing force are added into the model, the dynamic abrasion loss of the gear is calculated according to the improved Archard model, and the contact fatigue life of the gear is obtained; and finally, combining the gear bending fatigue reliability distribution function with the contact fatigue reliability distribution function by adopting a Copula function, and analyzing a competitive failure relationship among different failure forms by utilizing the combined distribution function to obtain a competitive failure reliability model so as to analyze the service life of the gear. According to the technical scheme, the technical problem of comprehensively describing the reliability of the gear is solved, accurate prediction of the reliability of the gear is achieved, and research, development and design of the gear are facilitated.

Disclosed in the present invention are an age-of-sensing analysis and optimization method for a wireless power supply sensor network. The method comprises the following steps: S1, determining a network model; S2, determining a MAC layer model; S3, determining a channel model; S4, on the basis of a multi-segment formula, fitting a nonlinear energy-receiving model, and calculating the decoding success probability of a single sensor; S5, calculating the possibility of information being successfully received in a fusion center under a sensing information requirement threshold; S6, using a hypothesis testing theory to model a fusion-sensing reliability model, so as to obtain the probability of credible fusion; S7, by means of a joint sensing-transmission success possibility, acquiring a closed-form expression of age of sensing, and formulating an information timeliness optimization problem; and S8, respectively optimizing a sensor power supply duration, a fusion information demand threshold, and sensor deployment, so as to give a minimum age of sensing. By means of the present invention, age of sensing is optimized on the basis of the analysis thereof, thereby improving the timeliness of sensing information.

The invention provides a reliability evaluation method and system considering that a micro-grid supports a power distribution network, and the method comprises the steps: employing a Monte Carlo simulation framework, building a probability reliability model through the development of a modeling process of a self-control grid-connected micro-grid, describing the micro-grid suitable for various standard scenes, and evaluating the reliability of a multi-MG grid-connected power distribution network. Scheduling and resource management are optimized by using a mixed integer linear programming technology; a micro-grid component is simplified into an equivalent distributed power generation unit, and the equivalent distributed power generation unit is integrated into an overall analysis framework, so that the calculation complexity is effectively reduced, the reliability evaluation efficiency of the power distribution system is remarkably improved, and the function of performing reliability evaluation on the power distribution network is realized. The framework and the model provided by the invention are adaptive to economic and technical research, and the reliability of the multi-MG power distribution system is enhanced; the method is an accurate and simple reliability evaluation method, reduces the operation burden, and improves the reliability evaluation efficiency of the power distribution network.

The invention provides a system reliability modeling method based on multi-source data fusion. The method comprises the following steps: collecting data in a ship complex system; preprocessing the collected data to obtain multi-source data; performing reliability modeling based on the multi-source data to obtain a basic model; performing simulation on the basis of the multi-source data and the data of the basic model to evaluate the reliability level of the system, and performing statistical analysis on the data obtained by system failure simulation to obtain a system reliability evaluation index; the reliability evaluation indexes comprise the reliability and the average life of the system. According to the method, data processing in each stage is developed and developed according to reliability test identification requirements of a complex system, on this basis, a system reliability model is constructed and fault and failure characteristics of the system are analyzed by combining the structure and functional characteristics of the system, and reliability simulation evaluation of typical application scenarios is developed.

The invention provides a method for predicting a single event effect induced failure rate of an FPGA (Field Programmable Gate Array) system in radiation. The method comprises the following specific steps: representing a radiation environment of the FPGA system; determining the sensitivity of the single particle effect of the FPGA system in the radiation environment; and establishing a fluence domain reliability model of the FPGA system in the radiation environment: converting the characteristics of the single event effect cross section sigma SEE into a corresponding average fault-free fluence MFTF, and establishing an FPGA system reliability model of each energy spectrum band or LET spectrum band in a given radiation fluence according to the average fault-free fluence MFTF and the total number phi of particles passing through a unit area in a radiation field. According to the method, physical process analysis and data driving means are combined, and the fluence domain reliability model is established, so that the reliability of related equipment in a special physical environment is improved.

The invention discloses a virtual power plant adjustable potential comprehensive evaluation method, system and equipment giving consideration to communication and action time delay. According to the method, a communication system time delay model, an equipment action time model and a communication reliability model are constructed by constructing a cloud edge collaborative network system framework for regulating and controlling a virtual power plant. And then, establishing a virtual power plant maximum adjustable potential evaluation model based on timing delay step length search by using a k-shortest path algorithm. And finally, providing a method based on a near-end strategy optimization algorithm to solve the optimization model. According to the method, comprehensive evaluation of the adjustable potential of the virtual power plant considering the influence of the communication system and the resource action time is accurately described, a decision basis is provided for the virtual power plant to participate in primary and secondary frequency modulation and peak regulation of the power system, and frequency stability and power and electric quantity balance of the power system can be supported.

The invention discloses a mixed edge cloud-oriented satellite-ground convergence network service function chain (SFC) arrangement method. The method comprises the following steps: firstly, establishing a mixed edge cloud-oriented satellite-ground network model and a service function chain model; secondly, designing a service cost model and a link reliability model for the scene; thirdly, establishing a service function chain arrangement optimization problem model; then, converting the problem model by using a Markov decision process, performing feature extraction on the current network and service state, and inputting the features into a strategy generation network to obtain a deployment strategy; carrying out link mapping by adopting a Dijkstra algorithm based on reliability; and finally, optimizing an arrangement strategy by using deep reinforcement learning to realize an optimization target. According to the invention, the arrangement of the service function chain is carried out under the assistance of the cloud computing center in the satellite-ground convergence network environment, and efficient resource allocation and management are realized by taking the maximization of the service acceptance rate and the minimization of the service cost as targets.

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

The invention discloses an air cushion belt dynamic balance control system and a management and control method, and belongs to the field of air cushion belt conveyors. Original signals of a sensor are obtained, a Fluent noise filtering algorithm is used for eliminating airflow interference, synchronous tension and air pressure data are aligned through timestamps, a reliability model is used for filling temperature field missing values, and standardized data streams are formed; based on the standardized data flow, analyzing an air pressure gradient by means of a fluid mechanics equation, extracting abnormal vibration modal characteristics of the carrier roller, calculating a fluctuation coefficient in combination with tension simulation, and obtaining a multi-dimensional parameter set containing dynamic distribution of a pressure field and a belt deviation angle; inputting the multi-dimensional parameter set into the digital twin, updating the fatigue damage parameters of the belt through Fluent simulation driving, rendering the visualization result of the flow field of the air chamber in real time, and outputting a simulation result containing the dynamic cloud picture of the pressure field of the air chamber and the fatigue damage distribution of the belt; and calculating the pressure balance degree of the air chamber according to a simulation result.

The invention discloses an efficient sampling adaptive data fusion method. The method comprises the following steps: S100, generating an initial low-precision sample set; s200, obtaining a high-precision sample set by an infinitesimal method; s300, mixing the low-precision and high-precision sample sets to obtain a new sample set; s400, calculating the real distance between the low-precision sample set and the nearest high-precision sample; s500, establishing an adaptive RBF neural network model, and processing the new sample set; s600, substituting the real distance into an adaptive RBF neural network model to update parameters and search an optimal function value; according to the method, a mixed precision pneumatic data fusion model of different precision data is associated, and an infinitesimal division sampling method and a self-adaptive data fusion method are provided, so that efficient initialization and high-precision prediction of an RBF variable credibility model are realized.

The invention discloses a railway signal equipment station level reliability analysis method, which belongs to the technical field of railway signal system operation and maintenance, and comprises the following steps of: calculating to obtain annual average temperature data and annual average humidity data of a region where a railway line is located; constructing an action coefficient of the environment on railway signal equipment degradation; constructing a degradation process model of the railway signal equipment; calculating to obtain a reliability value of the railway signal equipment; establishing a topological structure of the railway signal equipment system according to the railway signal station layout diagram and the interlocking table; equidistant discretization based on time scale is carried out on the reliability state of the railway signal equipment, and a station level reliability model of the railway signal equipment is constructed; and representing the station-level reliability of the railway signal equipment by using an indicative function, and calculating to obtain a station-level reliability value of the railway signal equipment. According to the invention, the problem that the equipment-level and station-level reliability evaluation of the outdoor railway signal equipment is difficult to carry out in a complex operation environment on a railway site is solved.

The invention discloses an electrical coupling system operation reliability evaluation method, which comprises the steps of analyzing a network topology, an energy flow direction and an element logic relationship of an electrical coupling system, and constructing a Bayesian network model of the system from a power supply source to a load point; establishing an element reliability model, and obtaining different failure rate information of the non-key element by changing different age limits and load rates; taking different failure rate information of the non-key elements as input data of nodes of the Bayesian network model, outputting an analytical model coefficient, and obtaining a reliability index in combination with a key element combination state probability; training an LLSVM model by taking different failure rate information of the non-critical elements as input and taking an analytical model coefficient as output; and during online application prediction, obtaining different failure rates of corresponding non-key elements by combining element long-term and short-term prediction results with the element reliability model, inputting the different failure rates into the trained LLSVM model, and outputting a reliability index. According to the method, effective, rapid and efficient reliability evaluation of the electrical coupling system is realized.

A computerized method for determining the reliability of a prediction output comprises a prediction module implementing at least one first prediction model to obtain a first prediction output and at least one other prediction model to obtain another prediction output where the method further comprises a similarity module implementing a similarity scoring method to determine a similarity score between the first prediction output and the other prediction output of the at least one other prediction model, the method further comprising a reliability module implementing a reliability model which determines the reliability of the first prediction output based on the determined similarity score.

The invention discloses a reliability evaluation method and system for a flexible interconnected power distribution network containing a three-terminal SOP, and the method comprises the steps: determining a topological structure of an MMC converter, constructing a sub-module reliability model based on the topological structure, and constructing a bridge arm reliability model considering the correlation between redundant configuration and sub-modules through the sub-module reliability model; constructing a reliability model of the MMC according to the topological structure of the MMC converter and the bridge arm reliability model; analyzing various running states of the three-terminal SOP, and constructing an SOP reliability model through the various running states and the reliability model of the MMC; and based on the SOP reliability model, through random simulation of an operation state and load transfer judgment, reliability evaluation of the flexible interconnected power distribution network is carried out, and the influence of SOP access on the reliability of the flexible interconnected power distribution network is analyzed. According to the method, the reliability change after the multi-terminal SOP is accessed to the power distribution network is quantitatively analyzed, events of different sequential relationships between fault occurrence, recovery and the like in the power distribution network system are considered, and the reliability evaluation level of the system can be improved.

The invention provides an unmanned aerial vehicle airborne time reliability evaluation method based on multi-source uncertainty analysis, and the method comprises the steps: constructing an unmanned aerial vehicle aerodynamic model, carrying out the numerical simulation of the aerodynamic parameters of an unmanned aerial vehicle, obtaining the change conditions of the aerodynamic parameters, such as a lift coefficient and a resistance coefficient, along with the flight state, constructing an unmanned aerial vehicle airborne time model, the method comprises the following steps of: deducing a control equation of the space-remaining time, analyzing the change conditions of the space-remaining time under parameter uncertainty, environment uncertainty and flight state uncertainty, performing a space-remaining time simulation test of the unmanned aerial vehicle under multi-source uncertainty, quantifying the space-remaining time uncertainty of the unmanned aerial vehicle and calculating the distribution of the space-remaining time; and constructing an unmanned aerial vehicle airborne time reliability model, and evaluating the reliability. Based on the influence of the complex environment on the space-leaving time in the flight process of the unmanned aerial vehicle, the reliability model of the space-leaving time is constructed, the reliability of the space-leaving time of the unmanned aerial vehicle is quantitatively evaluated in actual use, and the method has practicability, high accuracy and high engineering applicability.

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

The invention relates to the technical field of stress testing, in particular to a constant thermal stress sequential electric stress testing method, which comprises the following steps of: preparing a sample, calibrating equipment, applying linear increasing electric stress under constant thermal stress and monitoring failure in real time; calculating an electric stress acceleration factor, equivalent time and failure time; combined with the test junction temperature and the normal working condition, a comprehensive acceleration factor is calculated, and the failure time is converted; and constructing a three-parameter Weibull distribution reliability model based on the converted data, and estimating parameters by using maximum likelihood estimation and a Newton-Raphson algorithm to obtain a reliability index. According to the method, the failure can be efficiently induced under the condition of constant thermal stress, and the modeling reliability index is accurate, so that the efficiency, accuracy and engineering practicability of reliability evaluation are improved.

A deep foundation pit supporting structure design method based on system reliability analysis relates to the technical field of foundation pit structures, and obtains a supporting structure design method with high safety coefficient and low supporting cost through calculation and analysis, and comprises the following steps: S1, obtaining an engineering exploration report and a preliminary design scheme corresponding to a deep foundation pit; s2, determining a value range of a design variable x and statistical characteristics of a random variable y; s3, determining a failure mode of a deep foundation pit support design system according to engineering characteristics of the support corresponding to the deep foundation pit; s4, constructing a limit state function of each failure mode; s5, constructing a system reliability model through the system failure probability of the series system in the step S4, fusing a multi-objective optimization model into the system reliability model, and setting an objective function and a constraint condition; and S6, setting a multi-objective optimization algorithm in the deep foundation pit support design system, calculating the safety and cost of different support structures by using the algorithm, obtaining a Pareto solution set, and obtaining the corresponding deep foundation pit support structure design through comparison and selection.

The invention provides an aero-engine washing effect evaluation and complete machine decline prediction method and device, and the method comprises the steps: selecting an EGTM of an aero-engine as a washing effect evaluation and engine state monitoring indication quantity, and carrying out the preprocessing of the EGTM data; based on the preprocessed EGTM data, analyzing an EGTM recession rule by using an index model, and establishing a non-intervened EGTM operation recession model, a recoverable recession model and an unrecoverable recession model; according to the recoverable decline model and the washing performance recovery reliability model, a prediction method under the EGTM flight cycle number is established, and evaluation of the washing effect of the aero-engine and prediction of complete machine decline are achieved. According to the method, the washing effect of the aero-engine after washing can be evaluated, the change trend of the EGTM of the aero-engine can be predicted, and help is provided for reasonably arranging the washing nodes of the aero-engine.

The invention relates to the technical field of network communication, and discloses a network node reliability evaluation method based on spatio-temporal reliability, which comprises the following steps: S1, collecting operation state information and topological structure information of a plurality of nodes in a network; s2, based on the operation state information, establishing a time-varying reliability model of each node, and dynamically adjusting model parameters according to a recent operation state to obtain a time dimension reliability index of each node; s3, on the basis of the topological structure information, analyzing the incidence relation between the nodes, and obtaining the spatial dimension reliability index of each node in combination with the operation state information; and S4, synthesizing the time dimension reliability index and the space dimension reliability index to generate a comprehensive reliability evaluation result of each node. The limitation that a traditional static evaluation method cannot reflect the dynamic change of node states is overcome; comprehensive evaluation of node reliability in NFV and multi-domain network environments is realized, and the actual reliability condition of the node is accurately reflected through comprehensive analysis of two dimensions of time and space.

The invention discloses a compressor impeller fault life reliability analysis method, device and equipment and a medium. The method is characterized by comprising the steps that three-dimensional point cloud data of a target compressor are collected, three-dimensional modeling is conducted on the target compressor according to the three-dimensional point cloud data, and a compressor three-dimensional model is determined; performing particle erosion simulation and compressor crack propagation simulation on the compressor three-dimensional model, and determining an erosion reliability model; impeller fatigue simulation is conducted on the compressor three-dimensional model, and a fatigue reliability model is determined; and a compressor impeller reliability model is established according to the erosion reliability model and the fatigue reliability model, fault life reliability analysis is conducted on the target compressor based on the compressor impeller reliability model, and the impeller fault life reliability of the target compressor is determined. A comprehensive fault life reliability prediction model can be established for the compressor impeller, the reliability of the compressor impeller can be accurately predicted, and a reference function is provided for health management and maintenance of the compressor.

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

The invention discloses a converter reliability evaluation method and system and a computer program product. The method comprises the following steps: determining voltage data respectively corresponding to each sub-module in a plurality of module groups according to working condition information of a converter; based on the voltage data, establishing a loss model corresponding to each sub-module in the plurality of module groups, the loss model being used for determining operation loss of a plurality of components corresponding to each sub-module in the plurality of module groups; based on a loss model, calculating the steady-state junction temperature corresponding to each sub-module in the plurality of module groups; performing component failure rate analysis by using the steady-state junction temperature to obtain an analysis result; according to an analysis result, constructing a module reliability model corresponding to each sub-module in the plurality of module groups; and constructing a system reliability model based on the redundancy configuration strategy and the module reliability model. According to the invention, the technical problems of low accuracy and poor reliability of a converter reliability evaluation method in the prior art are solved.

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

The invention provides a data-physics combined drive system layer reliability modeling method. Damage parameter data are obtained through an external load working condition of a mechanical system and a finite element model; selecting a reference variable, and respectively establishing a system external load-reference variable proxy model and a reference variable-part damage parameter proxy model by applying a machine learning model based on the damage parameter data set; acquiring external load data information, and acquiring probability distribution of reference variables through an external load-reference variable machine learning model; reflecting a failure correlation relationship among parts in the system through a reference variable-part damage parameter machine learning model; based on the probability distribution of the reference variables and the failure correlation between the parts, a system layer reliability modeling method is applied to establish a data-physics combined drive system layer reliability model so as to solve the system reliability under the complex load working condition, and the accuracy and rationality of system reliability evaluation are improved.