13 results about "Fault tree analysis" patented technology

This invention provides a method and system for assessing the reliability of wind turbine generator sets based on a directed fault graph. Addressing the challenges of complex structures and multiple failure modes in wind turbine generator sets, it employs fault tree analysis to establish a directed fault graph model. To address the difficulty of polymorphism at the root nodes of the directed fault graph, it considers the uncertain logical relationships between components and the system within the wind turbine generator set and uses probability values ​​to measure these relationships in a conditional confidence quality table. Then, it takes all importance rankings into account and performs directed fault graph reasoning according to the same importance ranking, selecting the maximum value to complete the multi-fault state reliability assessment of the wind turbine generator set. This invention effectively assesses the reliability of wind turbine generator sets while avoiding the low efficiency and low accuracy problems of the Monte Carlo method and confidence interval method, respectively.

This invention provides an intelligent CNC hydraulic system and control method, including a sensing and monitoring unit comprising a multi-source heterogeneous sensor network deployed at key nodes throughout the system. The invention is rationally designed, using a multi-source heterogeneous sensor network deployed throughout the system to simultaneously collect multi-dimensional operating parameters such as pressure, flow rate, temperature, and vibration. Edge computing is used to complete data preprocessing and fault-sensitive feature extraction. Relying on an SVM-LSTM dual-model fusion fault intelligent diagnosis module and integrating a fault tree FTA analysis submodule, it achieves accurate classification of fault types, rapid location of fault points, and root cause tracing. It also provides early warning of early system faults, significantly improving fault diagnosis accuracy compared to traditional threshold alarm methods. Furthermore, it can identify fault types that traditional methods cannot detect, such as directional valve sticking, predicting fault development trends in advance and effectively preventing equipment damage caused by fault escalation.

This application discloses a method and electronic device for processing vehicle functional safety standard data. The method includes: generating standardized hazard scenario information based on vehicle functional data, and assessing the hazard scenario information using risk parameters to obtain preliminary safety level information, whereby risk parameters include exposure rate, severity, and controllability; performing dual-benchmark cross-validation on the preliminary safety level information to obtain validated target safety level information and corresponding functional safety objectives; extracting vehicle system architecture information based on the functional safety objectives, and generating fault tree analysis information by combining the functional safety objectives and vehicle system architecture information; calling a functional safety requirement generation model, generating functional safety requirement information based on the fault tree analysis information, and performing compliance verification on the functional safety requirement information to obtain the vehicle's target functional safety requirement information. The technical solution provided in this application can improve the processing efficiency of vehicle functional safety standard data.

The application discloses a vehicle shock absorber abnormal sound prediction method and relates to the technical field of shock absorbers. The method comprises the following steps: performing bench whole vehicle cooperative testing on a vehicle shock absorber, and collecting vibration acceleration signals and key structural parameters of the vehicle shock absorber; scoring the abnormal sound of the cooperative testing by means of expert scoring, to obtain a subjective evaluation score; screening abnormal sound key parameters by combining fault tree analysis and sensitivity analysis; constructing an abnormal sound feature vector by means of wavelet packet decomposition and sample entropy; calculating multi-road surface noise transfer function values based on a whole vehicle structure finite element model; taking the abnormal sound key parameters, the abnormal sound feature vector, the multi-road surface noise transfer function values and the subjective evaluation score as a training set, training an abnormal sound prediction model, and obtaining a trained abnormal sound prediction model; obtaining a predicted subjective evaluation score based on the abnormal sound prediction model; and performing hierarchical early warning based on the predicted subjective evaluation score, so that the abnormal sound prediction of the vehicle shock absorber is realized.

This invention relates to the field of capacity measurement technology, specifically to a method and system for measuring the openable capacity of a novel distribution network under uncertain source load scenarios. It includes the following steps: real-time acquisition of operational data from distributed photovoltaic (PV), electric vehicle (EV), and novel loads in the new distribution network; generation of a typical operational scenario set using K-means clustering algorithm; identification of safe operating boundaries using fault tree analysis and Petri net modeling; construction of an objective function and constraints with the goal of maximizing openable capacity; and output of the optimal openable capacity sequence by solving the problem using a bibliometric optimization method; and realization of coordinated control of the distribution network and microgrid, and dynamic updating of the safe operating boundaries. This invention fully considers the complex scenario characteristics such as fluctuations in distributed PV output and random electric vehicle charging loads, solving the problems of traditional static capacity measurement methods ignoring source load uncertainties and large deviations between the measured results and actual operating states.

This invention discloses a method, system, and medium for predicting and managing the health status of locomotive brake systems based on fault tree analysis and data fusion. The method involves constructing a digital fault prediction model for the locomotive brake system based on fault tree principles; collecting real-time status data from each subsystem of the locomotive and extracting quantitative features to determine the status of each basic event in the model; dynamically calculating the probability of each event using these quantitative features and the digital fault prediction model, and then conducting graded assessments and early warnings accordingly; if an early warning is not triggered, the method continues to collect status data from each subsystem of the locomotive; if an early warning is triggered, an intelligent maintenance work order is generated; maintenance personnel perform on-site repairs on the locomotive brake system according to the intelligent maintenance work order, and the repair results are fed back to the digital fault prediction model to optimize the model and algorithm parameters. This invention, by constructing a fault tree model and fusing multi-source real-time data, achieves dynamic assessment of the brake system's health status, early prediction of fault risks, and intelligent support for maintenance decisions.

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."

This invention relates to the field of gas turbine technology, and more particularly to a method, apparatus, equipment, and medium for determining the manufacturing qualification information of gas turbine blades based on fault tree analysis. The method includes: obtaining a parameter set corresponding to each gas turbine blade in a gas turbine blade set; analyzing the parameter set using a fault tree corresponding to the gas turbine blade set to obtain qualification information for each gas turbine blade; determining the production problem corresponding to the gas turbine blade set based on the qualification information; and generating manufacturing improvement measures corresponding to the gas turbine blade set based on the production problem. This invention can provide improvement measures for subsequent turbine blade manufacturing, point out specific research directions for scientific research, and improve the accuracy of turbine blade production.

This invention provides a method for calculating the frequency of administrative shutdowns and reactor stoppages in nuclear power plants using fault tree analysis, comprising the following steps: S1, Screening and equipment failure impact analysis based on administrative requirements: Based on operational constraints or power plant requirement documents, identify potential administrative shutdowns and reactor stoppages that may occur in the system, and perform failure mode and impact analysis on the system; S2, Reliability data collection: Collect reliability data for the equipment analyzed in step S1; S3, Establishing a fault tree model: Improve the PSA fault tree modeling method to make it applicable to the calculation of administrative shutdown and reactor stoppage frequencies; S4, Quantitative calculation: Combine the reliability data collected in step S2 and the formula given in step S3 to calculate the equipment failure probability and failure frequency. This invention, based on the power plant's own equipment reliability and repair capabilities, provides a more scientific calculation method that better suits the characteristics of the power plant, reduces judgment errors from different personnel, and the calculation results are more consistent with the actual situation of the power plant.

This invention discloses a method, system, equipment, and storage medium for constructing a distribution network operation architecture, relating to the field of distribution network operation architecture. The method includes: extracting monitoring data from different equipment operations to establish a distribution network dataset with the same time-stamp; resampling all equipment data to the same timestamp sequence based on the kernel density formula; determining and performing alignment operations between the current equipment operation monitoring data and the same timestamp sequence based on the equipment's sampling frequency and timestamp; obtaining the quality assessment score and weight coefficient of the equipment operation monitoring data, and updating and optimizing it; constructing a distribution network operation architecture evaluation model and outputting the current operation status labels of the distribution network equipment; and dynamically adjusting the equipment operation parameters in the distribution network operation architecture based on fault tree analysis and expert experience rules. This achieves dynamic sampling of equipment operation data and applies the distribution network operation architecture evaluation model to solve the problem of synergistic operation of distribution network economy and reliability under multiple constraints.

This application discloses a fault diagnosis assistance method, system, and electronic device for offline equipment. The method includes: acquiring at least one fault data from the offline equipment in the form of voice, text, or images; performing modality-specific content recognition and feature extraction on the data, mapping it to a unified public semantic space to generate a comprehensive feature vector; inputting it into a fault tree analysis model, and combining it with dynamic weight adjustment based on operating conditions to output a set of high-confidence candidate fault paths; inputting this set into a retrieval enhancement generation model, and performing a dual-path hybrid semantic retrieval matching of historical maintenance cases and technical specifications in a local multimodal knowledge base; correcting the reasoning results through a knowledge graph constraint decoding strategy, and generating a diagnostic result containing fault root cause location and maintenance suggestions. This application integrates multimodal adaptation, cross-modal fusion, logical reasoning, knowledge retrieval, and logical constraints, realizing intelligent fault diagnosis of offline equipment on edge devices, and improving the accuracy and flexibility of offline fault diagnosis.