13 results about "Fault severity" patented technology

This invention provides a method and system for handling redundant communication faults in a steer-by-wire system, relating to the field of steer-by-wire technology. The method includes: acquiring the original communication status parameters of independently configured first and second communication channels; the first and second communication channels are two independent communication channels between the main control unit and the redundant control unit in the steer-by-wire system; verifying the original communication status parameters based on a preset multi-dimensional verification strategy, and determining the current fault level of the system by combining preset fault level judgment rules and a dynamically adjustable fault judgment cycle, wherein the fault level is based on the communication channel status combination and fault severity; and executing matching fault handling, signal switching, and current regulation strategies based on the fault level, combining hierarchical signal control logic and a motor current gradient limiting mechanism to achieve precise switching between system signals and motor current.

This application proposes a method and apparatus for detecting phase loss in a power plant starting transformer, relating to the field of power fault detection technology. The method includes: acquiring the signal energy distribution of the starting transformer; constructing an energy feature vector reflecting the relative energy relationship between the three-phase current signals and inputting it into a phase loss identification model to obtain the initial probability of phase loss faults in each phase; performing state inversion calculations by combining real-time calculated zero-sequence and negative-sequence current amplitudes to infer the changes in the equivalent circuit parameters of the transformer's internal windings and leads before and after the fault, thereby correcting the initial probability to obtain a confidence level, and finally generating a phase loss detection report including the fault phase and fault severity. This application comprehensively characterizes the energy correlation characteristics of the three-phase currents through energy feature vectors and corrects the initial probability of the model using changes in equivalent circuit parameters, eliminating random biases in the output of a single model, achieving accurate matching between the fault determination result and the actual fault state of the transformer, and improving the accuracy of fault phase identification.

The application discloses a primary and secondary fusion intelligent circuit breaker on a column, which comprises a fault detection module, a protection action module, a reclosing module and an information reporting module. The fault detection module collects multi-dimensional electrical quantities through current and voltage transformers, judges the fault and identifies the type in combination with dynamic normal range and duration threshold; the protection action module calculates the fault distance based on the corrected impedance, determines whether to act in combination with the current direction and the protection range, and selects the protection level according to the fault severity; the reclosing module dynamically sets the observation time window, distinguishes the transient or permanent fault through the retest verification mechanism, and adaptively adjusts the reclosing strategy; the information reporting module uploads the fault related information. The application solves the problems of the traditional circuit breaker, such as high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like, and significantly improves the accuracy, speed and self-healing ability of the power distribution network fault processing.

This application provides a fault handling method and a vehicle control system, comprising: starting an electric swing system and acquiring fault monitoring data of the electric swing system, the fault monitoring data including a target fault code; querying a fault data table to determine the fault severity level and fault handling strategy corresponding to the target fault indicated by the target fault code, the fault data table describing the mapping relationship between faults, fault severity levels, and fault handling strategies, the fault handling strategy describing the fault alarm method and fault handling rules under the corresponding fault severity level, the fault severity level being pre-classified according to the degree of impact of the fault on the electric swing system; and alarming and handling the target fault generated by the electric swing system according to the fault severity level and fault handling strategy corresponding to the target fault. This application reduces driver reliance on experience and improves equipment operation safety by providing a handling method matching the fault severity level when a fault occurs.

This invention relates to the field of fault diagnosis technology, specifically to an intelligent fault diagnosis method and system for tunnel electromechanical equipment based on big data. It includes: acquiring power consumption data, pollutant concentration, and natural wind speed data; assessing the air supply pressure at each detection point within the tunnel; calculating the effective air supply output of the jet fan at any given detection point; calculating the energy efficiency index and energy efficiency deviation of the jet fan at that detection point; identifying faulty jet fans and calculating their fault severity, thereby performing fault diagnosis for each faulty jet fan. This invention can significantly improve the accuracy and maintenance efficiency of fault diagnosis for electromechanical equipment within tunnels.

According to an embodiment of the present disclosure, a device and a method for diagnosing faults in an electronic brake system based on a deep learning model, which may be provided, which collect and preprocess vehicle state data including sensor data and actuator state data through an in-vehicle communication network, input the preprocessed vehicle state data into a deep learning anomaly detection model to determine a normal state or a fault state of the electronic brake system, input the preprocessed vehicle state data into a deep learning detailed fault diagnosis model to determine a fault type and fault severity of the electronic brake system, if the electronic brake system is determined to be in the fault state, and independently control the electronic brake system based on the fault type and fault severity.

The application provides a power plant starting transformer open-phase detection method and device, and relates to the technical field of power failure detection. The method comprises the following steps: acquiring signal energy distribution of the starting transformer, constructing an energy feature vector reflecting the relative energy relationship among three-phase current signals and inputting the energy feature vector into an open-phase identification model to obtain initial probabilities of each-phase open-phase failure; combining real-time calculated zero sequence and negative sequence current amplitudes to perform state inversion calculation, inferring the change amount of equivalent circuit parameters of transformer internal windings and lead wires before and after the failure, correcting the initial probabilities to obtain confidence, and further generating an open-phase detection report containing fault phase and fault severity. The application completely depicts the three-phase current energy correlation characteristics through the energy feature vector, corrects the initial probabilities of the model by using the change amount of the equivalent circuit parameters, eliminates the random deviation of the single model output, realizes accurate matching between the fault determination result and the actual fault state of the transformer, and improves the fault phase identification accuracy.

This invention relates to the field of grid-connected control of new energy power generation and power system fault recovery, and discloses a collaborative fault recovery method for energy storage and AC / DC hybrid transformers in a wind-solar-storage complementary system. This method identifies abnormal operating conditions and constructs a fault severity index by monitoring the voltage, frequency, and power status of the point of common coupling (PCC). During a fault, the energy storage system provides rapid active power support, and when the energy storage response is limited, the AC / DC hybrid transformer provides compensation and adjustment. After the fault is cleared, the collaborative optimization weights are adaptively updated based on power balance constraints and the grid stability index, completing the full-process coordinated control of the energy storage system and the AC / DC hybrid transformer, thereby improving the system's fault ride-through capability, recovery smoothness, and operational stability.

The application relates to a method and device for scanning a predicted fault applied to an element of a power system, a computer device, a storage medium and a computer program product, and relates to the technical field of power grid safety monitoring. The application can shorten the time of detailed power flow calculation and improve the efficiency of the predicted fault scanning. The method comprises the following steps: obtaining a node admittance matrix and an active power flow total increment of an element to be detected under a predicted fault condition; obtaining a voltage phase angle increment corresponding to each bus in the element to be detected according to the node admittance matrix and the active power flow total increment; obtaining an active power flow increment of a current branch according to a first voltage phase angle increment, a second voltage phase angle increment and a current reactance value of the current branch for each branch in the element to be detected; determining a weight coefficient and a power flow limit value of the current branch; and inputting the active power flow, the weight coefficient and the power flow limit value into a preset severity index operation model to obtain a predicted fault severity index when the current branch is disconnected.

This invention relates to an adaptive diagnostic method for inter-turn short-circuit faults in a dual-phase three-phase permanent magnet synchronous motor, belonging to the field of motor fault diagnosis technology. By establishing a mathematical model and an adjustable model for inter-turn short-circuit faults in the dual-phase three-phase motor, and combining multi-phase motor space vector decoupling transformation, a short-circuit fault component is introduced into the harmonic subplane of the stator voltage equation. Fault characteristic values ​​are designed using the x-y current residual between the actual motor and the adjustable model. The fundamental amplitude is extracted as a fault severity feature through orthogonal Fourier transform. An observer based on the mapping relationship between the short-circuit current residual and the fault turns ratio is constructed. A model reference adaptive law for the piecewise PI regulator is used to dynamically adjust the PI parameters according to speed and load conditions, achieving adaptive feedback estimation of the short-circuit turns ratio. This improves the accuracy and real-time performance of fault feature extraction, simplifies the fault-tolerant algorithm and optimizes the disturbance observer parameter design, solves the diagnostic distortion problem caused by changes in operating conditions, and provides an optimized solution for motor monitoring.

The application discloses a vehicle fault warning method and device, electronic equipment and computer program product, and relates to the technical field of vehicle control. The method comprises the following steps: acquiring real-time running parameters of a current vehicle, identifying a fault type and a fault severity of the current vehicle according to the real-time running parameters; matching corresponding warning content in a preset warning template library according to the fault type and the fault severity; determining a target warning distance according to the fault severity, and generating a dynamic electronic fence according to the target warning distance; and broadcasting the warning content to target vehicles in the dynamic electronic fence through the current vehicle, so that the target vehicles can play the warning content in the vehicles. The application realizes real-time and accurate transmission of vehicle fault information, enables the target vehicles to take precautions in time, improves the safety of vehicle driving, and can be applied to the technical field of vehicle control.

This invention discloses an autonomous driving control method and control system for intelligent connected vehicles. The control method includes: collecting multi-source onboard data, extracting continuity features, consistency features, and temporal trend features from the multi-source data, and identifying fault types by combining them with preset joint judgment rules; constructing a two-dimensional hierarchical system of fault severity index and scenario risk index, calculating a hierarchical score based on the fault type, and determining a downgrade level in the two-dimensional hierarchical system based on the hierarchical score; setting dynamic constraints according to the downgrade level, searching for safe parking domains in real time, and generating parking paths that meet the dynamic constraints and obstacle avoidance requirements; controlling the vehicle to travel along the parking path to the safe parking domain, and performing safe parking and warning operations. The technical solution of this application can effectively control the vehicle and improve safety when a fault occurs.

This application relates to a fault identification method, system, equipment, and medium for belt conveyors. The method includes: acquiring multi-channel vibration signals from the belt conveyor; mapping the multi-channel vibration signals into a two-dimensional spatiotemporal grayscale image; performing texture analysis on the two-dimensional spatiotemporal grayscale image to extract anisotropic texture features; analyzing the anisotropic texture features using a pre-trained classification model to obtain the fault type, the pre-trained classification model including a first classification model and a second classification model; determining the fault start position according to the fault type using a corresponding texture start position identification rule, and determining the fault severity level based on the anisotropic texture features; and outputting the fault identification result composed of the fault type, fault start position, and fault severity level to drive alarm prompts. This application improves the accuracy of fault identification for belt conveyors.