20 results about "Failure mode and effects analysis" patented technology

The invention, which belongs to the technical field of ocean engineering equipment risk assessment, discloses a risk assessment method for an offshore deployment and recovery device considering system toughness, and the method comprises the following steps: S1, carrying out system grading on the offshore deployment and recovery device, and identifying a potential fault mode of the offshore deployment and recovery device; s2, an expert fuzzy evaluation method is adopted to determine the severity, occurrence rate and difficult detection degree of each fault mode, and a fuzzy risk priority number is solved; and S3, constructing a system toughness quantitative index of the offshore deployment and recovery device, updating the fuzzy risk priority number of each fault mode to obtain a risk priority number considering toughness, and obtaining a key component list of the offshore deployment and recovery device through descending order arrangement. The method solves the problems that a traditional fault mode and influence analysis method cannot quantify the system toughness, is high in subjectivity and is unreasonable in static weight distribution, and is suitable for risk analysis and reliability evaluation of complex marine equipment.

This invention discloses a dynamic database platform and implementation method for wind power equipment failure mode and impact analysis (FAM). Built on a B / S architecture, it includes a server-side and a user-side. The server-side comprises a database layer and a functional processing layer. The database layer stores wind power equipment failure analysis data from expert, enterprise, and personal databases in a hierarchical manner. The functional processing layer acquires, parses, structures, and updates the status of multi-source failure-related information. By incrementally acquiring, semantically parsing, and mapping text data from different sources, candidate failure modes and impact analysis records are generated. Combined with a manual review mechanism, continuous updates and flow of failure analysis data are achieved, thereby supporting the accumulation and application of wind power equipment failure knowledge and improving the completeness and maintainability of failure analysis results.

The invention provides a failure mode and influence analysis method and system based on a graph model and electronic equipment, a failure relation network of a network data structure is used as an original carrier of data, failure elements are associated through a reference relation between nodes in the failure relation network, and when a target node is updated, the failure mode and influence analysis efficiency is improved. According to the method, the data state of the target association path referencing the target node is automatically updated by using an automatic change propagation mechanism of the failure relation network, so that the effects of one-time modification and global effectiveness are achieved, and the consistency of the associated data is ensured while the data redundancy is reduced. When a user requests to check any network node, the dynamic view is generated according to the node type and the failure relation network, a flexible dynamic multi-dimensional analysis view angle is provided, and the analysis requirements of different user roles are met. Risk assessment is carried out from a global perspective through the failure relation network and the association path, and the accuracy of risk assessment is improved.

The invention relates to the field of nuclear power simulation, in particular to an automatic analysis system and method for failure mode and influence analysis of a nuclear power plant. The method comprises the following steps: step 1, constructing a nuclear power equipment knowledge graph, and integrating equipment structures, functions, failure modes and historical data; 2, automatically identifying a potential failure mode based on a machine learning model, and associating an influence path of the potential failure mode; integrating the machine learning model with digital twinborn simulation software to realize failure deduction and verification in a simulation environment; 3, after failure classification and influence path analysis are carried out on the accessed real-time data, a multi-criterion decision algorithm is adopted to calculate the risk priority, and optimization suggestions are output. According to the method, the automatic identification of the failure mode, the dynamic reasoning of the influence path and the quantitative evaluation of the risk priority are realized by constructing an intelligent analysis framework combining the knowledge graph and the machine learning.

The application provides a model-based power system failure mode and effect analysis method, and relates to the technical field of failure mode and effect analysis, and comprises the following steps: obtaining development requirements of a power system, and constructing a system model of the power system based on the development requirements; wherein the system model comprises an activity graph, a parameter graph model, a module definition graph and an internal module graph; identifying power system failure modes based on the system model; wherein the power system failure modes comprise functional failure modes, performance deviation failure modes and structural failure modes; identifying failure transmission relationships of the power system failure modes based on the system model; modeling the failure transmission relationships to obtain a failure transmission relationship matrix; and generating power system FMEA entries based on the power system failure modes and the failure transmission relationship matrix. The application improves the accuracy and analysis efficiency of power system failure mode and effect analysis.

The invention discloses a fault propagation analysis and root cause identification method for an early design stage of a complex system, and the method comprises the steps: carrying out failure mode and influence analysis, and identifying a potential failure mode and fault representation of the complex system; determining a fault propagation path through fault tree analysis, and quantifying the causal strength of fault propagation by using a Bayesian network; fusing the element, the fault propagation Bayesian network and the fault representation, and developing a causal enhanced multilayer Bayesian network for quantifying a probability mapping relation between a component fault mode and the fault representation; providing two importance degree models based on the multilayer Bayesian network: cascade failure Birnbaum importance degree and element Birnbaum importance degree, which are respectively used for identifying a key root cause and a key component; based on the above analysis, a fault feature probability matrix is constructed for fault propagation analysis and root cause identification. According to the method, weak links of a complex system can be effectively identified, and potential fault propagation paths and fault root causes can be identified.

The embodiment of the application discloses a product characteristic processing method and electronic equipment, and relates to the technical field of product failure mode and effect analysis. The method comprises the following steps: acquiring a multi-source data set of product FMEA analysis; identifying product element characteristics and classifying according to the multi-source data set; marking the element characteristics according to the characteristic categories; and generating a production characteristic file set according to the marking result, so that accurate identification of product characteristics can be realized, and the technical problem of transmission faults of product characteristics from the design stage to the production execution stage can be solved.

The invention relates to an intelligent optimized marine loading and unloading arm parameter self-adaptive setting and control method, and belongs to the technical field of harbor machinery intelligent control. The method comprises the steps of establishing a unified spatio-temporal data platform, collecting time sequence, space and physical quantity multi-source heterogeneous data in a classified mode, deploying a clock synchronization mechanism to ensure time consistency, cleaning abnormal data, complementing missing values, and adding spatio-temporal labels to the data. Constructing a loading arm digital twinborn model, calculating the stress and vibration characteristics of an arm support, recording a hydraulic pressure transmission process, correcting the model by using a back propagation neural network, and adjusting parameters in real time; establishing a parameter and performance mapping model, training and optimizing, and verifying through a test set; the failure modes are sorted through failure mode and influence analysis, stress and vibration data are collected in real time, and the fatigue damage degree and the safety coefficient are calculated and updated. Unified management of multi-source data of the marine loading and unloading arm is achieved, the model precision and the parameter optimization accuracy are improved, and self-adaption, efficient and safe operation of the loading and unloading arm is supported.

The invention discloses an intelligent microfiber polishing cloth manufacturing system and method based on dynamic risk management. The system comprises a plurality of process stations, a central control unit and a quality control network which are connected in sequence. A failure mode and influence analysis database is stored in the central control unit, and a risk priority index is pre-stored for each work station. The system collects real-time data of each process station through a quality control network, and a central control unit dynamically adjusts a monitoring and control strategy according to a risk priority index and automatically starts an enhanced control measure for a high-risk station. A traditional static failure mode and influence analysis management are converted into a dynamic and online intelligent control system, active prevention, real-time early warning and closed-loop control of quality risks in the production process are achieved, and the quality stability and the first-pass yield of microfiber polishing cloth production are remarkably improved.

This invention discloses an integrated method for early warning and quality improvement of critical elevator components under harsh environments. The method comprises the following steps: S1, collecting full lifecycle data of critical elevator components; S2, employing a composite analysis method combining Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FMA) to deeply explore failure causes and construct a quantitative failure mechanism model; S3, based on the failure mechanism model, selecting sensitive characteristic parameters and integrating machine learning algorithms to construct an adaptive early warning model, achieving real-time monitoring, risk classification, and accurate early warning of early component degradation under harsh environments; S4, based on the results of the failure mechanism model and the early warning model, formulating a multi-dimensional quality improvement plan. This invention can achieve accurate early warning of critical elevator components under harsh environments, prediction of remaining life, and tracing of quality problems, upgrading from "post-event maintenance" to "predictive maintenance + continuous quality improvement."

A method for vehicle fault management includes generating, for a vehicle system, a fault model including a plurality of faults using design failure mode and effect analysis information and parsing each of the faults of the plurality of faults by subsystem of the vehicle system. The method also includes determining, for a respective fault of the plurality of faults, whether the respective fault is associated with at least one existing diagnostic trouble code based on fault code requirement information. The method also includes, in response to a determination that the respective fault is associated with at least one existing diagnostic trouble code, associating, in a fault database, the at least one existing diagnostic trouble code with the respective fault.

The embodiment of the application discloses a kind of development method and system of aircraft fault isolation strategy, method includes: by the processing of failure mode and effect analysis data, the associated relationship of each fault reason is formed with fault phenomenon, troubleshooting scheme and test scheme;According to fault phenomenon, merge failure mode;According to maintenance level, merge troubleshooting scheme;For each failure mode group, determine the troubleshooting step group corresponding to each fault reason in it, and each troubleshooting step group is described to form troubleshooting step group file;In each failure mode group, according to fault code and maintenance level, each troubleshooting step group corresponding to each fault reason is merged;Each troubleshooting step group file is sorted, and fault isolation program file is generated;According to failure mode group, generate fault information list;According to maintenance level and user demand, select the data module of required fault list and fault isolation program, to generate fault isolation strategy.

The invention discloses a transformer risk assessment method and system, and belongs to the technical field of power grid risk assessment, and the method comprises the steps: calculating the fault rate of equipment of the same model of each manufacturer according to the model, manufacturer, number and fault type information of in-operation equipment of a power grid company obtained through statistics; according to the corresponding relation between the equipment fault rate and the fault occurrence degree, the equipment fault rate is converted into the fault occurrence degree; analyzing the structure, the function and the failure of the equipment to obtain fault types and the severity and the undetectability of each fault type; and calculating the risk index of each fault type according to the fault occurrence degree, the severity and the undetectability, and taking the maximum value of the risk index of each fault type as the equipment risk index. Furthermore, the method also comprises the step of determining the maintenance priority of the equipment according to the equipment risk index. According to the method, the statistical rule and the fault mode and effect analysis technology are fused, the fault frequency and the risk index of the transformer are accurately quantified, and distribution of operation and inspection resources is optimized.

The application discloses a kind of power grid frequency safety promotion methods based on virtual power plant, including in hierarchical virtual power plant architecture, using the minimum probability scene method based on Lyapunov criterion and failure mode and effects analysis method, considering information-physical uncertainty virtual power plant reliable capacity mathematical model is established for selected area;With investment minimization as target, with information-physical uncertain scene normal capacity, reliable capacity and delay as constraint, the virtual power plant terminal-communication system reconstruction planning model is constructed;Based on mathematical transformation, the planning model is converted into mixed integer linear programming model, and the reliable frequency modulation capacity of virtual power plant and terminal-communication system reconstruction upgrade scheme are obtained by using CPLEX software solution;Whether the reliable capacity supply and climbing rate obtained meet the needs of power grid frequency safety is verified.The application helps virtual power plant to provide reliable frequency modulation capacity economically and efficiently, assists power grid frequency regulation and improves power grid frequency safety.

The embodiment of the invention relates to the technical field of information processing, and discloses a software security analysis method and device based on a software architecture diagram, a storage medium and a product. The software architecture diagram comprises a plurality of software components and an interface relationship for connecting the software components; identifying all candidate starting points and candidate end points in the software architecture diagram so as to automatically split the software architecture diagram into a plurality of independent control flows; automatically converting each control flow into a corresponding software sequence diagram; performing security analysis on the software according to the software time sequence diagram; the safety analysis comprises failure mode and influence analysis or automobile safety integrity level decomposition rationality analysis, so that the software architecture diagram is directly converted into the software sequence diagram after being split, the design efficiency of an engineer is improved, and the split software sequence diagram is used for subsequent software safety analysis and software safety judgment, so that the design efficiency of the engineer is improved. Potential safety risk points and weak links can be found in the early stage of architecture design.

This invention discloses a reliability testing risk management method and system that considers differences in test types. The method includes the following steps: S1: Constructing a risk event classification system and a test type classification system; S2: Using Failure Mode and Effects Analysis (FMEA) to calculate the baseline risk priority number for each risk event under all test types; S3: Calculating correction coefficients and constructing a two-dimensional evaluation matrix of risk events and test types based on the correction coefficients; S4: According to the current test type, reading the risk priority number and risk level corresponding to each risk event from the two-dimensional evaluation matrix, and formulating control measures according to preset control rules. This invention, by constructing a systematic method based on FMEA, with hierarchical control as the core and closed-loop improvement as the guarantee, achieves a fundamental shift from experience-based response to systematic prevention, significantly improving quality level and risk management efficiency.

The invention provides a seamless track service state evaluation method based on multi-source monitoring data fusion. The method comprises the steps of collecting monitoring data of the special section of the seamless track through various devices, constructing a safety index system of the special section of the seamless track based on the monitoring data by adopting a fault mode and an FMEA (Failure Model and Effect Analysis) method, and classifying safety indexes of the special section of the seamless track, and determining the weight of each safety index by adopting an analytic hierarchy process (AHP) according to the classification of each safety index, and evaluating the service state of the special section of the seamless track through an evaluation mechanism according to the weight of each safety index. According to the method, monitoring data acquisition, systematized failure analysis, hierarchical weight determination and fuzzy comprehensive evaluation are integrated, a set of efficient and accurate seamless track service state evaluation system is constructed, the evaluation precision and the intelligent level are remarkably improved, and reliable safety guarantee is provided for railway operation in plateau regions.

The invention provides a security simulation test method and device, computer equipment and a storage medium, and the method comprises the steps: analyzing a software failure mode of a tested system through a failure mode and influence analysis FMEA method and a software failure mode and influence analysis SFMEA method, and building a fault tree based on the software failure mode; constructing a fault type library based on the bottom event of the fault tree, wherein the fault type library comprises a plurality of fault injection cases; and in a simulation test environment provided by the simulation test software, performing fault injection on the tested system based on the fault injection case, monitoring a fault injection feedback result, and updating an exception handling mechanism based on the fault injection feedback result. According to the technical scheme provided by the invention, the fault injection operation can be conveniently realized, the coverage degree of the fault injection use case is ensured, and the flexible expansion of the fault injection use case is facilitated.

The invention relates to a food safety risk assessment method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring risk monitoring data related to each risk assessment index in a food production process; the risk assessment index is determined by performing failure mode and influence FMEA analysis on each management and control object in the food production process; determining an index score corresponding to each risk assessment index based on the risk monitoring data; calculating a food safety risk index according to the index score corresponding to each risk assessment index; the food safety risk index is used for indicating the risk degree of the food safety accident. According to the method, the FMEA analysis is introduced into the food safety risk assessment process, so that the risk assessment indexes can more accurately point to real and key risk points in the production process, thereby realizing more timely risk state quantification and early warning, improving the timeliness of food safety risk early warning, and improving the risk assessment efficiency. Food safety accidents are effectively avoided, and food safety is guaranteed.

The invention provides a single-point fault equipment identification method and device fused with human factors, equipment and a medium, and relates to the technical field of nuclear power plants. The method comprises the following steps: acquiring an operation specification file and a control logic file of a nuclear power plant, analyzing the influence of equipment failure on unit operation, and classifying to obtain a key equipment list; through failure mode and influence analysis, an equipment failure list containing single and multiple equipment failure combinations is obtained; abstracting a personnel operation behavior into a functional unit, defining a logical relationship according to a system operation mode, and determining a human factor and equipment coupling relationship model by combining the functional unit, the logical relationship and various human factor error types; based on the equipment failure list and the model, constructing a system unavailability fault tree fusing equipment failure logic and personnel operation logic; based on the fault tree, a single-point fault identification result is output, and human factor reliability analysis and fault tree analysis are organically integrated, so that the comprehensiveness of single-point fault equipment identification is remarkably improved.