69 results about "Fault modelling" patented technology

The invention provides a dry-type transformer turn-to-turn short circuit multi-physical field simulation analysis method considering insulation aging and harmonic influence, and relates to the technical field of simulation analysis, and the method comprises the following steps: constructing a physical simulation model of a to-be-tested dry-type transformer; setting physical parameters and multi-physical field operation conditions of the physical simulation model; calculating a corresponding physical field operation calculation result under each physical field operation condition; performing post-processing on the physical field operation calculation result; by establishing a detailed simulation model and accurately assigning physical property parameters of each component, physical property changes of the turn-to-turn insulating material at different aging stages and under different temperature and frequency conditions are emphatically considered; turn-to-turn short circuit fault modeling is further introduced, magnetic-thermal-flow coupling simulation calculation is carried out under multi-harmonic excitation, the multi-physical field distribution characteristics of the dry-type transformer during different turn-to-turn short circuit faults are explored, and a reliable basis is provided for design optimization and safety evaluation.

The invention belongs to the technical field of electromechanical equipment fault diagnosis, and discloses a wind turbine generator transmission chain online fault diagnosis method, which specifically comprises the following steps: S1, adopting an SCADA (Supervisory Control and Data Acquisition) system to collect environment, electrical and vibration multi-source monitoring parameters of a wind turbine generator in real time and pre-process the parameters, thereby effectively improving the accuracy and availability of data; s2, through the design of a multi-source feature association fusion module, complementarity of environment, electrical and vibration parameters is fully utilized, and the accuracy and reliability of fault diagnosis are improved; s3, a teacher-student network framework is constructed, online fault model construction and real-time diagnosis are achieved, the real-time performance and the intelligent level of fault diagnosis are improved, due to the application of a class balance sampling mechanism and a positive and negative combined learning strategy, the influence of continuous state label offset is eliminated, pseudo label noise interference in online samples is effectively reduced, and the real-time performance of fault diagnosis is improved. And the adaptive diagnosis capability of the model in a working condition environment dynamic change scene is improved.

The invention discloses an unmanned aerial vehicle automatic test method, which comprises the steps of building a high-fidelity semi-physical simulation platform which comprises an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator, a flight control module and a ground control station, and loading a complete machine fault model and a dynamic environment model; designing an intelligent multi-fault injection scheme, and defining a single fault scene, a combined fault scene and a fault evolution scheme; configuring a dynamic environment simulation scene including static environment parameters, dynamic environment elements and environment, and a fault interaction scene; executing a self-adaptive test process, automatically generating a test case, intelligently scheduling a test sequence and dynamically adjusting test parameters; and performing multi-source data intelligent analysis and evaluation, collecting test data, performing fusion analysis on fault influence, and generating an evaluation report. By constructing a whole machine fault and dynamic environment model, the scene authenticity is improved by 40%, and for example, the motor fault yaw deviation under strong wind is closer to a real machine compared with a traditional method.

The invention discloses an automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

The invention relates to the technical field of circuit board detection, and discloses a circuit board detection method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting historical design parameters and simulation results of a circuit board, processing the design parameters and the simulation results, and forming a data set # imgabs0 #; executing a prediction model training strategy according to the data set, and generating an output prediction model of the circuit board; obtaining current design parameters of the circuit board, and applying the design parameters to the output prediction model to obtain a model result; executing the simulation dynamic adjustment strategy, and dynamically adjusting the simulation process of the simulation tool; historical fault design parameters of the circuit board are collected, a fault model training strategy is executed, and a fault prediction model of the circuit board is generated; applying the current design parameters of the circuit board to the fault prediction model to obtain a fault probability; setting a fault probability threshold; if the fault probability is not less than the fault probability threshold. And performance prediction, simulation optimization and fault early warning and correction in the circuit board design and manufacturing process are improved.

The invention provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

The invention discloses a simulation method and device for power cable fault positioning and a medium, and the method comprises the steps: collecting a power cable fault instance, and building a fault model for simulating a plurality of power cable fault types according to the power cable fault instance; determining a training stage where the user is located, and selecting a target power cable fault type matched with the training stage from a plurality of power cable fault types corresponding to the fault model; loading the fault model and a preset electromagnetic interference model to generate a simulation training scene for the fault type of the target power cable, and receiving an operation instruction sent by a user through interaction equipment in the simulation training scene; and converting the operation instruction into an operation parameter corresponding to the to-be-positioned power cable, and positioning a fault existing in the power cable according to the operation parameter and a power cable parameter corresponding to the power cable to obtain corresponding fault information, thereby realizing fault simulation positioning of the power cable.

The invention discloses an automatic conversion analysis method and system based on an implicit common cause fault model, and relates to the technical field of power system fault analysis, and the method comprises the steps: obtaining and marking power equipment, forming a plurality of equipment sets, setting a monitoring period for operation monitoring, and storing data into an operation record library; extracting feature data of the equipment, calculating a correlation index between the equipment according to the category of the equipment, and calculating the correlation between the same category of equipment and different categories of equipment by adopting different strategies; utilizing an implicit common cause fault model to calculate the probability that the reference equipment and other equipment are abnormal at the same time under each abnormal cause, including an independent failure probability, a common cause failure probability and a comprehensive probability; and according to the comparison of the comprehensive probability and a preset threshold value, the monitoring period of the equipment exceeding the threshold value is adjusted, and according to the abnormal reason, the monitoring period of all the associated equipment is also adjusted, so that the effective monitoring and fault prevention of the power system are realized.

The invention discloses a simulation data enhanced controllable-pitch propeller small sample fault diagnosis meta-learning method, and relates to the field of fault diagnosis, and the method comprises the steps: carrying out the simulation modeling and fault injection based on the working mechanism of a controllable-pitch propeller, and building a system fault model; constructing a fault simulation enhancement data set; constructing a variable-length time series data classification network model, wherein the model is connected with a multimode encoder and a triple loss function in combination with a residual error; training the variable-length time series data classification network model by utilizing a three-stage meta-learning training process, wherein the three-stage meta-learning training process comprises a meta-learning training process by utilizing an open source data set and a fault simulation enhancement stage by utilizing a fault simulation enhancement data set; and applying the trained variable-length time series data classification network model to an actually collected fault small sample data set for small sample task training. According to the method, the model is pre-trained by using the historical related data set, so that the current task data is reduced, training samples are reduced, and higher diagnosis precision is achieved.

The invention provides a distribution network line fault detection method and system, electronic equipment and a storage medium. The method comprises the following steps: constructing a fault model library for a distribution network line; acquiring real-time magnetic field variable data of the distribution network line and performing data preprocessing; performing comparative analysis based on the real-time magnetic field variable data after data preprocessing through a fault model library to obtain a comparison result; and performing fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result. According to the distribution network line fault detection scheme provided by the invention, the operation efficiency and safety of a distribution network can be remarkably improved, the unplanned power failure time is reduced, the service life of equipment is prolonged, the maintenance cost is reduced, the maintenance plan is optimized, the power supply reliability is improved, and the development of an intelligent power grid is promoted.

The invention provides a remote fault maintenance method for a coffee machine, and relates to the technical field of coffee machine maintenance. The remote fault maintenance method for the coffee machine comprises the following steps: S1, module division and fault signal determination; s2, collecting test data, and carrying out fault analysis training by utilizing machine self-learning; s3, enabling a remote diagnosis function by the user; s4, fault signal detection is carried out on the fault model; s5, the user carries out self-recovery according to the diagnosis report; and S6, processing when the repair scheme is invalid. According to the invention, when the coffee machine of the user has a fault, the user can actively apply for a remote diagnosis service, the coffee machine can automatically upload historical whole machine operation data and a fault signal to the server, and the server rapidly analyzes the data through machine learning, generates a fault diagnosis report and provides a detailed fault reason and a repair scheme.

The invention relates to the technical field of artificial intelligence chip design, in particular to an on-chip fault-tolerant processing method and system, a chip and electronic equipment. The system comprises a fault detection module, a fault diagnosis module and a fault repair module. The method comprises the following steps: collecting monitoring data of a chip assembly through a monitoring part of a fault detection module, judging whether a chip has a fault by utilizing a preset fault model, and if the chip has the fault, obtaining fault information containing an identifier of a fault assembly and fault data; the fault diagnosis module determines the fault position according to the identifier of the fault component, evaluates the performance degradation degree of the fault component and the influence of the performance degradation degree on the chip performance based on the fault data, and generates fault evaluation data. And the fault repair module formulates a fault repair strategy based on the evaluation data and implements fault repair. Therefore, automatic detection, accurate diagnosis and efficient repair of faults are realized in the chip, system downtime and maintenance cost caused by the faults are reduced, and fault-tolerant capability and reliability of the chip are effectively improved.

The invention provides a bakelite guide rail fault monitoring method and system, and relates to the technical field of fault detection, and the method comprises the steps: building a digital connection between a monitoring platform and a distributed sensor network; collecting multi-dimensional physical quantities during operation of the guide rail, and constructing a state information base; generating an abnormal state information base; calling the visual sensor group to collect a bakelite guide rail fault section image according to the abnormal sequence value, and obtaining a guide rail part image set; analyzing the guide rail part image set to obtain a guide rail fault cause library; performing correlation analysis according to the abnormal state information base and the guide rail fault cause base to obtain a guide rail fault model; and generating a fault maintenance scheme according to the guide rail fault model, and feeding back the fault maintenance scheme to the monitoring platform. The technical problems that in the prior art, a monitoring mode is not accurate and comprehensive enough, data processing capacity is lacked, judgment is slow and errors are prone to being generated due to the fact that a system mature fault model is lacked are solved.

A method comprises creating an electronic circuit design having a plurality of electronic components, defining a fault condition imposable during a simulation of the electronic circuit design, and generating a simulation model based on the electronic circuit design. The method also comprises generating a simulation fault model representing the fault condition and executing a simulation of the simulation model to simulate operation of the electronic circuit design. During the execution of the simulation, the method comprises controlling the simulation fault model to begin an imposition of the fault condition within the simulation model, simulating circuit behavior of the simulation model in response to the imposition of the fault condition, controlling the simulation fault model to cease the imposition of the fault condition within the simulation model, and simulating circuit behavior of the simulation model in response to the cessation of the imposition of the fault condition.

The invention discloses a steam turbine high-pressure control valve fault detection method based on an improved attention mechanism, and the method comprises the steps: generating a fault data set for training through building a mathematical simulation model of a steam turbine control system and various fault models of a high-pressure control valve; the short-term data with the high sampling frequency and the long-term data with the low sampling frequency are spliced into a mixed sequence, short-term and long-term data features are extracted by adopting a multi-scale attention mechanism, and system operation features under different time scales are fused; and acquiring context representation of each time point through a multi-head attention mechanism, and outputting a fault type prediction result of the time point by using a full connection layer and a softmax function. Through the method, the abnormal state of the high control valve of the steam turbine is detected, and a new technical means is provided for fault detection and prevention of a power system.

According to the main shock and aftershock time sequence rapid construction method and device provided by the invention, the influence of similar source generation mechanisms of main shock and aftershock, similar propagation paths, the same field effect and the like is fully considered, and the main shock and aftershock time sequence is rapidly and accurately constructed. The method comprises the steps that 1, the most dangerous fault plane and the spatial position and size of the fault plane are determined according to seismic fault data, sub-events with different circle center positions are generated randomly, and the size of each sub-event is represented by a radius; 2, determining the magnitude of the aftershock according to the set main shock magnitude and the main and aftershock power law relationship; step 3, according to the magnitude of the main earthquake and the aftershock, using rectangles consistent with the size of a main earthquake source and aftershock source to perform frame selection on the fault plane, and using sub-events contained in the rectangles to form an earthquake source model so as to obtain an earthquake source finite fault model of the main earthquake and the aftershock; 4, a deterministic Green function is obtained through decoupling; and step 5, carrying out convolution on the seismic source finite fault model and the deterministic Green function to obtain a final main shock and aftershock sequence.

The invention discloses a laboratory fault monitoring system which can effectively simulate nucleation, fracture and friction behaviors of natural earthquakes and continuously monitor stress, strain and displacement space-time distribution rules of a laboratory fault contact surface. The system comprises a fault model, an optical fiber sensor unit, a data acquisition and processing unit and a control and monitoring unit, and can further improve the precision of laboratory fault monitoring. Through arrangement of the optical fiber sensors, the high spatial resolution and sensitivity requirements which are difficult to meet by a traditional experimental observation system are solved, automatic processing and model analysis of experimental data are realized in combination with data acquisition and machine learning, and the accuracy and efficiency of data analysis are improved by adopting an intelligent algorithm. According to the invention, an existing laboratory fault monitoring system is further supplemented, the precision level of laboratory seismic research can be improved, and powerful support is provided for research of physical mechanisms of natural earthquakes.

The invention provides a fault diagnosis model small sample diagnosis capability enhancing method based on a meta-learning framework, and belongs to the technical field of electric data processing. The method comprises the following steps: establishing a diagnosis model based on meta-learning; obtaining a to-be-diagnosed target small sample; obtaining the confidence coefficient of the target small sample to be diagnosed, comparing the confidence coefficient with a threshold value, rejecting diagnosis for the target small sample to be diagnosed with the confidence coefficient smaller than the threshold value, and continuing the next step for the target small sample to be diagnosed with the confidence coefficient larger than or equal to the threshold value; and inferring a fault mode label of the to-be-diagnosed target small sample by using the diagnosis model, and outputting the fault mode label as a diagnosis result of which the confidence coefficient is not lower than a threshold value. According to the method, the generalization ability of fault model small sample diagnosis of meta-learning is enhanced.

The invention provides a traction control unit fault diagnosis and processing test system. The traction control unit fault diagnosis and processing test system comprises a high-speed simulation machine, a sensor simulation unit, a photoelectric conversion unit, a fault injection and signal observation unit and a traction control unit. According to the traction control unit fault diagnosis and processing test system provided by the invention, through fault modeling and semi-physical simulation test platform construction, simulation of each stage of a motor interphase short circuit fault in a laboratory low-voltage environment is realized; the irreversible damage to test equipment such as a motor caused by direct ground test simulation of an interphase short circuit fault is avoided, and the product development test cost and the safety risk are reduced.

The invention discloses a fault diagnosis method and device based on a vibration spectrum and an envelope spectrum, computer equipment and a storage medium, the method is mainly composed of characteristic value diagnosis, speed spectrum diagnosis and envelope spectrum diagnosis, and a fault model based on a water pump mechanism and analysis experience summarization are implanted into a software system. A software system is utilized to perform automatic diagnosis and analysis through a series of signal processing and analysis on collected signals, and equipment fault points are automatically judged through a computer. The method can automatically diagnose common unbalance faults, coupling misalignment faults, foundation loosening faults, water pump resonance faults, blade friction faults, bearing inner ring faults, bearing outer ring faults, poor bearing lubrication faults and the like of the water pump.

The invention discloses an on-line monitoring method of an electromagnetic valve and electronic equipment, and relates to an electromagnetic valve monitoring technology, and the method comprises the steps: a fault model construction step: collecting a historical abnormal data set of the electromagnetic valve, carrying out the weight analysis of the historical abnormal data set, and constructing an abnormal feature track set according to a weight analysis result; the online monitoring step is used for collecting real-time operation data of the electromagnetic valve, judging whether the real-time operation data is abnormal or not according to preset standard operation data, if yes, extracting abnormal parameters in the real-time operation data, and sending the abnormal parameters to a server; and according to the abnormal parameters, finding out an abnormal characteristic track corresponding to the abnormal parameters from an abnormal characteristic track set as a target abnormal characteristic track, predicting predicted operation data of the solenoid valve according to the target abnormal characteristic track, and generating abnormal processing information at the same time. According to the invention, the monitoring efficiency and the feedback speed are improved, so that a user can respond to and handle exceptions in time.

The invention relates to the technical field of distributed power supply fault judgment, in particular to a distributed power supply access unit fault analysis method, which comprises a working condition comparison module, a fault analysis module and a data processing module. The working condition comparison module periodically obtains working condition parameters of the distributed power supply access unit and updates the obtained data in real time; the fault analysis module comprises a plurality of fault models, a parameter fault threshold value and a duration out-of-limit threshold value of a corresponding working condition are set in each fault model, and when the equipment working condition exceeds the parameter fault threshold value and the duration exceeds the out-of-limit duration threshold value, fault occurrence is judged, a fault occurrence event is recorded, and the event is added into a processing queue of the data processing module; and the data processing module packages, stores and frames the fault event, and uploads the fault event to uplink equipment for processing. Only whether the working condition parameters are out of limit or not is judged in each period, the continuous out-of-limit time is recorded on the premise that the working condition parameters are out of limit, and the fault occurrence event can be accurately judged under the condition of low occupancy rate.

System (1) for predictive diagnosis of fault conditions of components (5) of the powertrain of a fleet (3) of electrically powered mobile working machines (4), wherein the system comprises: at least one working machine (4) with a communication module for sending operating data (BG); and a central unit (2) spatially separate from the working machine (4), which is designed to receive the operating data (BG) from the working machine (4) via a communication network, characterized by a data processing unit implemented in the central unit (2) with: a) an anomaly detection module configured to detect deviations from normal behavior in the operating data (BG) received from the working machine (4) that indicate an impending fault; and b) at least one hybrid fault model (24) configured to analyze the relevant operational data (OD) after detection of a deviation by the anomaly detection module, wherein the hybrid fault model (24) combines physical domain knowledge about the component (5) and data-driven models to specify a fault type and determine a fault severity.

The application discloses a camera fault determination method, device and computer readable storage medium. The camera fault determination method comprises the following steps: obtaining script data of a to-be-identified object; determining a fault identification threshold of the to-be-identified object according to a pre-trained fault model; and determining a fault determination result corresponding to the to-be-identified object according to the fault identification threshold and the script data. The to-be-identified object comprises a camera, a client and / or a video monitoring platform. The fault identification threshold of the to-be-identified object is determined through the pre-trained fault model, and the fault determination result of the to-be-identified object is determined based on the fault identification threshold and the script data. In this way, the offline troubleshooting efficiency of the camera and the accuracy of the fault determination are improved.

The invention discloses a hydropower station equipment fault detection system and method based on multi-source information fusion. The system comprises a central control host, a fault management server, an inspection robot, an environment monitoring sensor and a vibration sensor. The fault management server comprises a historical record database and a fault detection module; the fault detection module is used for performing fault diagnosis on hydropower station equipment; the equipment classification module is used for classifying the hydropower station equipment according to the fault phenomenon; and the inspection frequency setting module is used for setting the inspection frequency of the inspection robot. Different detection modes are set for different types of equipment, and the pertinence of fault detection is improved. The inspection frequency is adjusted according to the historical fault condition of the equipment, high-frequency faults can be found more easily, repeated inspection of low-frequency faults is avoided, and resources are saved. According to the method, the consistency of the frequencies of the multiple input data of the associated equipment fault model can be effectively ensured, and the normal operation of the fault model is ensured.

The present invention relates to a method for monitoring the operating conditions of a surge tank of a hydropower unit based on MFCC and a fault model library. A soundprint sensor is used to collect the soundprint signal of the surge tank. Based on historical data of the soundprint signal, liquid level signal, vibration signal, and pressure pulsation signal of known surge tank faults, a soundprint model library containing typical soundprint features is established for quickly comparing and identifying whether common faults are present under the current operating condition based on the soundprint signal of the current surge tank. Furthermore, a fault model library containing soundprint, liquid level, vibration, and pressure pulsation feature data is constructed for comparing and identifying whether known faults are present under the current operating condition based on the soundprint, liquid level, vibration, and pressure pulsation signals of the current unit. The present invention achieves real-time, online, high-precision, and automatic identification of surge tank faults, significantly improving the efficiency and accuracy of fault diagnosis and reducing downtime and maintenance costs caused by misdiagnosis or missed diagnosis.

Methods, computing systems, and computer-readable media for a machine learning method of modeling fault-related properties of a geological region are presented. The techniques include: obtaining seismic geological data for a geological region; obtaining from a user identifications of a plurality of faults in the geological region; automatically generating values for descriptors of respective faults of the plurality of faults; automatically partitioning faults of the plurality of faults into a plurality of groups according to the values for the descriptors; obtaining a mapping of respective groups of the plurality of groups to modeling parameter values; applying the mapping to a fault in the geological region outside of the plurality of faults to obtain a modeling parameter value for the fault outside of the plurality of faults; and modeling a fault-related property of the geological region based on the modeling parameter value for the fault outside of the plurality of faults.

The invention belongs to the field of power fault analysis, and discloses an MMC-based DC system fault analysis method, which comprises the following steps of: constructing corresponding fault models of different types at different positions of a DC power distribution network on the basis of analysis on various fault characteristics of the DC power distribution network of an MMC converter when a fault analysis model is carried out; the method can significantly accelerate the discrimination of fault lines, can greatly improve the fault processing efficiency, can greatly improve the power supply reliability of the operation of a power system, actively promotes the state diagnosis technology of the power system, and improves the operation level of a power grid.

The invention discloses a fault prediction method for a combined heat and power system of a solid oxide fuel cell, belonging to the technical field of solid oxide fuel cells. The method includes the steps of: establishing finite element sub-models of each component according to the structural composition of the combined heat and power system of the solid fuel cell, and integrating them to form the combined heat and power system of the solid fuel cell, and simulating the system according to the normal operating conditions to obtain the output parameters under normal conditions; then establishing a fault model, simulating to obtain the output parameters of the system under different faults, comparing the output parameters under normal conditions with the output parameters under different fault conditions, and analyzing the changes generated by the system after a fault occurs; finally, predicting whether the system has a fault and the type of the fault according to the output parameters of the actual system. The invention uses a simulation method to simulate the relevant characteristics of the SOFC system after different faults occur, and can predict whether a fault occurs and the type of the fault according to the output parameters of the actual system.

The fault processing method, device, network equipment and storage medium provided by the embodiment of the application analyze the historical data of each optical module according to the model of the optical module, determine the historical data that mutates when the optical module of the corresponding model is in a fault condition, extract the corresponding original fault data from the historical data that mutates, and train a fault model according to the original fault data and the model of the optical module. Thus, by dividing according to the model of the optical module, the flexibility of collecting the historical data of the fault optical module is improved, and the application range of fault detection is further improved. In actual use, automatic updating can be achieved, the detection accuracy is continuously improved with the accumulation of fault data, and the operation and maintenance cost of the optical module is effectively reduced.