47 results about "Multiple fault" patented technology

This invention relates to the field of power distribution fault location technology, specifically to an AI-driven power distribution fault location system and method. The system includes: a state projection construction unit that constructs multiple fault state projection surfaces; a state collision elimination unit that uses a topology response collaborative constraint model to identify structural logic conflicts and uses a structural collision-driven multi-path filtering mechanism to eliminate fault state projection surfaces with structural logic conflicts; a differential comparison scoring unit that uses an AI response scoring model to generate confidence scores for each candidate fault state projection surface and uses a working condition semantic embedding model to correct the confidence scores; a structural feedback location unit that uses the AI ​​response scoring model to update and sort the confidence score sequence to determine the fault point; and a fault terminal processing and display unit that displays the fault points of each power distribution station and generates inspection tasks and maintenance work orders. This invention combines AI-driven intelligent judgment and location of power distribution faults and constructs a fault identification method for multi-site maintenance scenarios.

The present application relates to the technical field of fault type identification acceleration, in particular to a kind of multi-modal sound feature fusion's fluid transmission equipment fault classification acceleration method and system;After sound sensor collects fluid transmission component operation sound signal, pre-processing is removed interference noise;Time domain feature extraction module extracts multi-scale space-time feature by multi-scale time convolution fusion unit;Frequency domain feature extraction module is converted by FFT, amplitude and phase separation processing and IFFT restoration, and time domain feature after frequency domain processing is obtained;Dual-domain feature fusion module fuses dual-domain feature by real Gabor wavelet function, and then excavates deep feature by Transform module;Fault diagnosis module outputs fault classification result by convolution, fully connected and Softmax function, and hardware platform outputs recognition accuracy by UART serial communication.This application detects comprehensively, responds quickly, has strong stability, low power consumption, good portability, can be adapted to different environments, identifies multiple fault types, and can also be extended to other related fields.

This application provides a chaos testing method, apparatus, device, storage medium, and program product, which can be applied to the big data field. The method includes: modifying the business data of key nodes based on fault factors in a business knowledge graph to obtain multiple fault nodes. The business knowledge graph is obtained from historical business data, and the key nodes are determined based on their impact on the business. Fault factors include adjustment values ​​for logical judgment conditions and timing delay values. The method also involves extracting the relationships between nodes in the business knowledge graph, and using multiple fault nodes as starting points, traversing the business knowledge graph based on these relationships to obtain multiple fault chains. Finally, the method injects the faults in each of the multiple fault chains into a test system to obtain test results.

The present application relates to a kind of solving method for the design of nonlinear system multiple small fault estimator, first establish the discrete-time system model containing actuator and sensor multiple fault and nonlinear term, then introduce nonsingular transformation and decompose the original system into two subsystems: subsystem 1 only contains external disturbance, subsystem 2 contains both actuator fault and sensor fault. Based on two subsystems, two iterative learning estimators 1 and 2 are designed, and an integrated solution method for designing two estimator parameters is applied using optimization algorithms. Finally, a multiple small fault estimation strategy for nonlinear systems is presented, and accurate estimation is achieved when multiple faults occur concurrently. The present application can accurately estimate the actuator and sensor faults of nonlinear discrete-time systems, while simultaneously counteracting the influence of external disturbances on fault estimation results, effectively estimating small faults in the system, and improving the system's fault handling capability and fault tolerance performance. The solving method of the present application is simple and easy to implement in practical engineering systems.

This application discloses a vehicle fault determination system, method, device, and storage medium, belonging to the field of vehicle technology. In this system, a diagnostic device first determines the target controller based on multiple fault codes, then sends an acquisition request to the target controller. The target controller acquires signal values ​​from multiple functional modules within multiple cycles, generates a first data stream based on the signal values ​​of the multiple functional modules within each cycle, and sends the first data stream corresponding to the multiple cycles to the diagnostic device. The diagnostic device generates a second data stream based on the first data stream corresponding to the multiple cycles and sends the second data stream to the terminal. The terminal processes the second data stream using a processing script to obtain a first file, and determines the cause of the fault based on the first file. Therefore, when multiple fault codes occur, this system can directly acquire the signal values ​​of multiple functional modules corresponding to the target controller at once, eliminating the need for multiple acquisitions, saving time, and thus improving the efficiency of determining the cause of the fault.

This application relates to a method and system for fault monitoring of thermal flow sensors based on multi-source data fusion. The method includes: dividing historical operating status data at various time points into multiple data subsets according to a preset time window; updating a first graph object based on the features of each graph node and the correlation strength of edge features corresponding to the data subsets to construct a time-series subgraph corresponding to multiple fault category labels and training a first defect detection model; dividing current operating status data into a time-series subgraph to be detected corresponding to multiple data subsets to be detected according to a preset time window; inputting the time-series subgraph to be detected into the first defect detection model to determine the preliminary fault detection result of the thermal flow sensor to be detected; and correcting the first fault category based on domain knowledge graph and text feature data to determine a second fault category and its corresponding natural language description. This application can accurately identify complex faults caused by multi-parameter collaboration.

This invention relates to the field of submersible electric pump (FET) fault diagnosis technology, and particularly to a method and system for FET fault diagnosis based on CFD and deep learning. The method includes constructing a full-channel 3D model, simulating FET faults by modifying key geometric feature parameters, and using a hybrid mesh strategy to divide the mesh; extracting time-domain and frequency-domain features from the simulation data, forming feature vectors from these features, and constructing multi-dimensional labeled simulation data using these feature vectors and multi-dimensional label vectors; constructing a multi-channel deep neural network, training the multi-channel deep neural network using the multi-dimensional labeled simulation data; and deploying the trained multi-channel deep neural network to a well site edge computing gateway to diagnose FET sensor data. This invention utilizes CFD to generate high-quality data to drive deep learning model training, achieving rapid and accurate collaborative diagnosis of multiple faults in FETs.

The utility model belongs to railway signal system technical field discloses a 6502 electric concentration voice alarm system mainly used to solve the problem that the existing bell fault alarm mode cannot quickly distinguish fault types and has low fault processing efficiency. The utility model prestores a plurality of customized voice alarm data based on different fault types in the voice control module. When the specific alarm relay in the 6502 electric concentration console is excited, the corresponding control relay of the alarm relay can be triggered to be excited synchronously, and then the corresponding control port in the voice control module is connected, so that the corresponding alarm voice broadcast is carried out through the voice power amplifier module and the loudspeaker. Compared with the traditional bell alarm mode, the system does not need technical personnel to check the console indicator light on site, and the voice directly broadcast fault type can effectively improve the fault identification efficiency, and also enable the technical personnel to instantly identify the core problem in the multiple fault concurrent scene, and significantly improve the emergency disposal efficiency.

This disclosure provides a fault detection method, a fault detection model training method, an apparatus, and a railcar, which can be applied to public transportation and fault detection technology. The fault detection method includes: determining vibration characteristics based on vibration trend data obtained from multi-dimensional vibration signals during equipment operation; constructing a hierarchical structure based on the vibration characteristics, wherein each level in the hierarchical structure includes at least one node, and the node represents fault information of a fault existing in the equipment; analyzing the temporal characteristics of the hierarchical structure to obtain fault detection results, wherein the fault detection results include multiple fault information items arranged in priority order, and the priority order represents the degree of impact of the fault on the equipment.

The application discloses a kind of based on small sample fault diagnosis method of super-dimensional calculation, belong to mechanical equipment fault diagnosis technical field, including collecting the vibration signal original data of multiple mechanical equipment, design signal efficient pretreatment module, the calculation of local feature information is carried out to original vibration signal;Design the time series sample coding mode of super-dimensional calculation, i.e. multivariate sample coding, for the vibration signal local feature extracted is coded into a series of super-dimensional vector, forms a super-dimensional space;Determine the similarity measure standard, design similarity measure fusion decision, the local feature information of vibration signal is input into super-dimensional calculation model and is trained, and training result is called query super vector;With different proportion test set, the model obtained by training is tested and classified, obtain the final result of model training, and utilize multiple fault data to analyze and test the model.The application can efficiently extract fault feature, improve the efficiency and precision of fault diagnosis.

This application discloses a fault processing method and apparatus, and a computer-readable storage medium, and relates to the field of electric power technologies, so as to improve the flexibility of battery fault processing. The method includes: obtaining a first parameter value of a target parameter of a battery at a first moment; determining a target fault level among multiple fault levels of the battery based on the first parameter value, where the target fault level is used to indicate a risk level of the battery at the first moment; and executing a target fault processing policy corresponding to the target fault level.

The application discloses a kind of based on transfer learning's electric energy meter fault classification method and device.Therein, method includes: collecting the historical fault data sample set of electric energy meter;Respectively traverse each fault category sample in historical fault data sample set, all samples under this fault category are as minority class sample set, the sample of remaining each fault category is as majority class sample set, generates multiple two-class data sets;According to the pre-trained transfer data selector and transfer task supervisor, respectively to multiple two-class data sets are iterated against, generates multiple transfer data sets;Respectively multiple transfer data sets are input in at least number class sample generation model, generates multiple balanced sample sets respectively training classifier, generates multiple fault category classifier;Real-time acquisition is measured fault data respectively input to multiple fault category classifiers, outputs multiple fault category probability, and selects maximum value in multiple fault category probability as the fault category of measured fault data.

This invention relates to a parameter identification method for reactive power compensation devices (RPCs) in power system averaged power flow ...

This application discloses a method and system for fault analysis of a neutral beam injection harness system. The method includes: determining multiple fault types based on historical experimental data of the target experimental system; using the fault events of the target experimental system as the top events of a fault tree; using the multiple fault types as initial events; performing upward and downward analysis on each initial event to obtain corresponding first upper-level events and first lower-level events, and constructing a fault tree accordingly; obtaining the current fault event; determining its corresponding second upper-level events and second lower-level events based on the fault tree; determining the priority coefficients of each event in conjunction with historical experimental data and sorting them; and processing the current fault event based on the sorting results. This application can simultaneously consider fault correlation analysis and troubleshooting order determination during the harness system equipment fault analysis process, thereby improving the completeness of fault analysis and the targeting of fault location and troubleshooting.

This invention provides a method and system for diagnosing fault conditions in hot strip rolling mills, relating to the field of automated control technology for hot-rolled strip steel. The method includes: collecting a set of operating signals from the hot strip rolling mill to be diagnosed; selecting evaluation indicators from the set of operating signals; determining multiple fault condition types corresponding to the evaluation indicators; establishing a preset scoring system for the equipment fault conditions corresponding to each fault condition type; constructing an original decision matrix regarding the evaluation indicators and equipment fault conditions; performing cloud roughness number processing on the original decision matrix to generate a cloud roughness number decision matrix; calculating the importance ranking of the evaluation indicators using game theory and a comprehensive weighting method; using the grey relational analysis-TOPSIS algorithm to quantitatively score the equipment fault conditions; constructing a fault type evaluation formula; and diagnosing the equipment fault conditions based on the fault type evaluation formula.

The utility model provides a kind of urban rail transit relay protection practical training device, the device includes: fault simulation module, setting and output analog fault signal, the analog fault signal includes abnormal current, abnormal voltage;Relay protection module is connected the fault simulation module, receives the analog fault signal, and whether output action signal is judged according to the protection logic set;Control management module is connected the fault simulation module, relay protection module and external device respectively, receives the logic judgment result of the relay protection module and the state signal of the external device, the action of the external device is controlled when the relay protection module meets action condition, and the external device includes analog circuit breaker.The utility model equipment concentrates design, can flexibly set multiple fault scenes, easy to operate, improve training efficiency.

The application discloses a kind of based on dynamic threshold and double monitoring strategy fault detection method and system, it is related to the field of fault detection, specific steps are as follows: based on historical process monitoring data, build comprehensive detection index and initial threshold;Initial threshold is adaptively dynamically updated based on dynamic threshold real-time updating mechanism, generate the first threshold for abnormality determination;According to the abnormality cumulative statistics of comprehensive detection index, and set the second threshold for abnormality determination;Acquire the process monitoring data to be processed, and calculate the comprehensive detection index of current time, in the retrieval path of pre-construction, according to the first threshold and the second threshold parallel detection is carried out, and abnormality determination result is obtained.The application is adjusted mechanism based on dynamic threshold and double monitoring strategy, under the condition that without additional prior knowledge and complex model reconstruction, multiple working conditions, multiple fault type industrial process is efficiently and accurately detected.

The present application relates to the technical field of engine life prediction, in particular to an engine life prediction method based on multi-head attention, which first normalizes the original data of the aero-engine sensor and eliminates redundant features; then realizes time-space double-dimension adaptive weighting through the multi-head attention mechanism, and strengthens the key degradation information; then inputs the weighted data into the long short-term memory network to learn the long-time dependence relationship, and completes the preliminary prediction of the remaining useful life; finally, combining Monte Carlo regularization and kernel density estimation, multiple prediction values are generated and the uncertainty is quantified, and the mean value is taken as the final prediction result. Experimental verification shows that this method effectively highlights the key degradation features, reduces the influence of uncertainty, and significantly improves the prediction performance under single working condition and multiple fault modes, providing reliable data support for preventive maintenance of aero-engines.

The application provides an Internet of Things equipment after-sales inspection operation and maintenance large model system fused with RAG technology, relates to the technical field of data processing, and comprises an equipment state analysis module, an operation and maintenance knowledge modeling module, an abnormal state identification module, a combined fault reasoning module and a retrieval enhancement generation module; the equipment state analysis module is used for analyzing equipment operation data and constructing an equipment operation state sequence; the operation and maintenance knowledge modeling module is used for performing semantic processing on equipment manuals and historical operation and maintenance data and generating an operation and maintenance knowledge vector set; the abnormal state identification module is used for identifying abnormal information, constructing an abnormal state set and a corresponding fault feature vector; the combined fault reasoning module is used for performing correlation analysis based on multiple fault feature vectors and screening to generate a combined fault knowledge set; the retrieval enhancement generation module is used for generating an inspection operation and maintenance diagnosis result in combination with the equipment operation state sequence and the combined fault knowledge set; and the system improves the consistency of the diagnosis result and the actual equipment state.

The application provides a traction converter bridge arm shoot-through fault identification and protection method and device, and solves the problem that the driver cannot locate the fault cause in multiple fault information in the HMI screen. The traction converter bridge arm shoot-through fault identification and protection method comprises the following steps: detecting whether a fault occurs inside the converter; if a fault occurs inside the converter, judging whether the fault inside the converter meets the condition of the bridge arm shoot-through fault; if the condition of the bridge arm shoot-through fault is met, it is determined that the bridge arm shoot-through fault occurs; when the bridge arm shoot-through fault occurs, the network is reported, and the protection measure is started.

The application discloses a rolling mill predictive maintenance method and system based on big data analysis, and relates to the technical field of rolling mill maintenance.The method comprises the following steps: collecting mechanical state parameters, process parameters and electrical parameters of a rolling mill to form multi-source time sequence data; processing the multi-source time sequence data, extracting time domain statistical features and frequency domain features according to the processed data, screening the extracted features, and obtaining fault sensitive features; organizing the fault sensitive features into a feature matrix according to time steps, inputting the feature matrix into a pre-trained fault classification model to perform fault classification, and obtaining a fault classification result including multiple fault types and corresponding probabilities; inputting the data sequence after fault classification into a pre-trained residual life prediction model to perform residual life prediction, and obtaining a residual life prediction result including residual life and a corresponding confidence interval.The application can fuse multi-source data of the rolling mill, realize accurate identification of early faults of the rolling mill, and accurately predict the residual life of the rolling mill.

The application provides a robust multi-fault synchronous reconstruction method for an uncertain quad-rotor unmanned aerial vehicle under external disturbance, and relates to the technical field of quad-rotor unmanned aerial vehicles. First, a dynamic model of the quad-rotor unmanned aerial vehicle is established, and is converted into a T-S fuzzy state space equation to construct an augmented system containing system states, process faults and sensor faults; for the obtained augmented system, a controllable order fuzzy observer is designed, and an error system is derived; the parameter matrix of the controllable order fuzzy observer is solved, and the stability of the error system is analyzed; based on the controllable order fuzzy observer, synchronous reconstruction of multi-faults of the quad-rotor unmanned aerial vehicle is realized. The nonlinear model of the quad-rotor unmanned aerial vehicle is approximated through T-S fuzzy technology, a controllable order fuzzy observer is designed, synchronous accurate reconstruction of multiple faults, effective suppression of external interference and complete decoupling of unknown inputs are realized, and the order of the observer can be flexibly adjusted according to actual requirements.

This application relates to the field of power distribution technology, specifically to a remote maintenance method and system for high and low voltage complete sets of power distribution equipment. The system includes: a cloud platform and an edge terminal; the cloud platform is used to: perform fault mode identification on historical state data to obtain multiple fault events; perform sequence pattern mining based on the time and connection relationship between devices in the historical state data to obtain a fault mode tree; and send maintenance information to the edge terminal; the edge terminal is used to: obtain first state data of the device targeted by the received maintenance information after the historical state data, and obtain second state data of the device targeted by the maintenance information before the occurrence of the fault event; determine a maintenance strategy based on the matching result of the first state data and the second state data, and when the maintenance strategy expands to the maintenance subject, send maintenance information to the edge terminal where the device that has not received maintenance information is located.

Various example embodiments provide for injecting multiple faults in parallel into a circuit design during fault simulation of the circuit design based on functional independence of the faults, where the fault injection and simulation may be performed as part of an electronic design automation (EDA) software system. According to various example embodiments, a structural analysis of a circuit design is performed to determine fault observability per strobe and the results of the analysis are used to determine independent fault groups for parallel injection during fault simulation of the circuit design.

This invention discloses a voltage recovery control method and device based on multi-fault scenario classification, belonging to the field of power operation and maintenance technology. Its main purpose is to solve the problem of insufficient AC voltage support capacity after a receiving-end fault. The method mainly includes: determining the real-time fault scenario category based on real-time acquired fault characteristic data; determining a target reactive current reference value based on the real-time fault scenario category and the reactive power output strategy corresponding to the current control stage during multiple sequential control stages; controlling the reactive power output of the receiving-end converter station based on the target reactive current reference value; and, under the condition of active power absorption coordination, determining the active power absorption amount of different generator sets based on the active power absorption strategy, and controlling the corresponding generator sets to absorb the surplus active power of the receiving-end grid based on the active power absorption amount, so as to prioritize reactive power output under current limit constraints. This method is mainly used for voltage recovery after a fault.

This invention discloses a method and system for managing the requisition of electrical appliances, relating to the field of appliance management technology. It includes constructing a historical knowledge base, standardizing fault text descriptions into multiple fault types based on historical fault work order data using natural language processing technology, and establishing a fault-tool mapping set for each fault type. Tools are dynamically marked as frequently used or occasionally used tools based on their probability of occurrence in historical work orders corresponding to the fault type. This application can improve operational safety and prevent the omission of core tools: by establishing a "fault-tool" mapping model and dynamically marking the core "frequently used tools set," the system can perform mandatory verification during the requisition process. Once a core tool is detected as unclaimed, an audible and visual alarm is triggered, and the process is intercepted, fundamentally eliminating the problem of safety tool omissions due to human negligence and greatly reducing the risk of serious accidents such as electric shock and falls from heights.

The present disclosure relates to the technical field of network-constructing energy storage, and particularly relates to a black start technology simulation method, device, equipment, medium and product. The method comprises: performing simulated power failure processing on a built power system according to a preset target fault type; starting an energy storage converter and adjusting parameters of the energy storage converter according to a control strategy; starting an auxiliary converter and adjusting parameters of the auxiliary converter according to the control strategy; starting the electronic load when the energy storage converter and the auxiliary converter are in a stable output state, increasing the load of the electronic load according to a load recovery strategy, and continuously collecting operation data of the power system under different loads; and updating the control strategy of the energy storage converter and the auxiliary converter and the load recovery strategy based on the operation data. The preset target fault type can cover multiple faults, and fault simulation and fault recovery are performed on each fault type one by one, which is conducive to improving the comprehensiveness of the simulation results.

This application provides a method for simulating and acquiring acoustic signals from multiple faults in an inverted bucket-type steam trap, relating to the field of thermal power technology. An experimental platform for simulating and acquiring acoustic signals from multiple faults in an inverted bucket-type steam trap is established; normal operating conditions of the steam trap are constructed, and reference acoustic signals under normal operating conditions are acquired and corresponding operating parameters are recorded synchronously; using the same acquisition specifications as under normal operating conditions, fault acoustic signals under corresponding fault conditions are acquired and operating parameters are recorded synchronously; under the condition that the steam trap is bypassed, interference noise signals are acquired using the same acquisition specifications as under normal operating conditions; after preprocessing and time alignment of each signal sample, they are coupled according to preset rules; all samples are uniformly labeled, quality checked, and classified for storage, constructing and outputting a standardized dataset of acoustic signals from multiple faults in the steam trap. This application realizes controllable simulation, acquisition, and standardization of acoustic signals from multiple operating conditions and multiple faults in steam traps, providing data support for research on steam trap fault diagnosis.

The appropriate analysis team will be responsible for troubleshooting information processing system failures. [Solution] A fault response request system for performing fault countermeasures for information processing systems comprises: a first prompt input unit that receives fault information and requests a first generation AI to summarize the fault information; an update processing unit that receives the summarized fault information from the first generation AI and registers it in the stored fault DB; a similarity comparison unit that searches the stored fault DB based on the summarized fault information and finds multiple faults similar to the summarized fault information; a second prompt input unit that receives the summarized fault information and inference criteria and requests a second generation AI to infer an analysis team that fits the inference criteria using the summarized fault information from the multiple faults obtained; and a fault response request output unit that receives the analysis team inferred by the second generation AI and outputs a countermeasure request to the received analysis team.

The application provides a kind of high-precision photoelectric encoder measurement result high fault-tolerant design method and system on satellite, comprising: by radial transceiving light path double-channel design, obtain two-way absolute angle data and two-way incremental data, four-way measurement data output angle data according to predetermined judgment rule, judgment rule is suitable for single point failure to output correct data, when multiple faults occur, according to the specific situation analysis, the judgment rule can be avoided to avoid the fault state and change to obtain correct angle data output, realize automatic fault-tolerant design.