380 results about "Fault occurrence" patented technology

The invention relates to an electromechanical fault prediction and diagnosis method and system based on big data, and the method comprises the steps: collecting the operation state data of electromechanical equipment in real time, and synchronously obtaining historical associated data; performing dynamic feature extraction on the operation state data and the historical associated data, constructing a sliding mean value feature matrix, and calculating dynamic weights of feature parameters; generating a fusion weight coefficient according to the dynamic weight and a preset fault threshold interval; extracting a distribution density curve of a historical fault occurrence probability, and calculating a dynamic threshold value; performing weighted reconstruction on the sliding mean feature matrix based on the fusion weight coefficient, and outputting a fault type and an occurrence probability through a pre-trained lightweight residual neural network model; and when the fault occurrence probability exceeds a dynamic threshold value adjusted based on a historical fault occurrence probability distribution density curve, generating an electromechanical fault diagnosis result so as to realize the purposes of real-time monitoring of the operation state of the electromechanical equipment and accurate fault prediction and diagnosis.

A method, system, and readable storage medium for fault line selection in distribution networks is provided. The method includes: obtaining the zero-sequence current of each feeder and the zero-sequence voltage of the busbar within a preset time window after a fault occurs; using these to process the feeder's short-time window zero-sequence instantaneous power curve cluster in the distribution network through KPCA (Kernel Principal Component Analysis) for dimensionality reduction, determining the principal component scores; and performing BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering based on these scores to identify whether a feeder is faulted. This clustering process allows for precise and rapid identification of the faulted feeder, even when the current is small, improving detection accuracy. This solves the problem of quickly identifying the faulted feeder in a small current grounding distribution network during single-phase grounding faults.

The invention provides a circuit board electrical performance test and diagnosis method, which comprises the following steps of: constructing a structured process-electrical data set by collecting process parameters such as etching time, pressing temperature, drilling precision, copper foil thickness and the like and electrical test indexes such as impedance deviation, leakage current, signal attenuation and the like; screening key process variables by adopting mutual information analysis and a maximum correlation-minimum redundancy criterion; through modeling of a three-layer causal diagram and a structural equation, quantitative influence evaluation of process parameters on electrical performance and fault types is realized; when a fault is detected, the system can automatically perform reverse reasoning, identify a main manufacturing deviation item and generate attribution diagnosis and process optimization suggestions, so that the accuracy of fault diagnosis and the pertinence of process adjustment are improved, and the fault occurrence rate is reduced and the product quality is improved.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

The invention relates to the technical field of power system automation, and particularly discloses an intelligent fault diagnosis and analysis system and method for power secondary equipment, and the method comprises the following steps: constructing a sensor network, collecting the operation data of a power system, and extracting the characteristics related to the fault of the power secondary equipment in the operation data; constructing a fault diagnosis model of the power secondary equipment, and obtaining faults of the power secondary equipment in the monitoring time period; constructing an association period according to the fault occurrence time point, generating coordinate points according to the fault occurrence sequence in the association period, and clustering the coordinate points to obtain a cluster; and according to the density of the clustering clusters, strong correlation faults of the faults are obtained, and when the faults occur, prompt information is sent to maintain the strong correlation faults corresponding to the occurring faults in advance. According to the invention, the real-time accurate diagnosis, strong correlation early warning and interpretable visual root cause analysis of the power secondary equipment fault are realized, and the safe operation of the system is effectively guaranteed.

The invention discloses a fault prediction and early warning system and method based on multi-sensor monitoring, and belongs to the field of big data, and the method comprises the steps: deploying various types of sensors, collecting equipment operation data in real time, and forming a multi-dimensional original data set; preprocessing the data to remove abnormal values and missing values, removing noise in the sensor data by adopting a filter, converting the data of different dimensions into a unified range, and extracting time domain features and frequency domain features from the preprocessed data; fusing the features of different sensors to form a comprehensive feature vector; training a fault prediction model by using a machine learning algorithm based on the historical data; and inputting sensor data collected in real time into the trained model, calculating a fault occurrence probability, setting an early warning threshold value, and triggering early warning when the predicted fault probability exceeds the threshold value. The device operation data is collected in real time, potential faults are found in time, early warning is carried out in advance, the device shutdown time is shortened, and the production efficiency is improved.

The invention discloses an information operation and maintenance monitoring system, which belongs to the technical field of information operation and maintenance monitoring and comprises a heterogeneous data acquisition layer, a streaming processing engine, an intelligent analysis center, a closed-loop control module, a visual platform and a safety protection system. Physical equipment, virtualized resources and a cloud native environment can be fully covered, the breadth and depth of data acquisition are ensured, the adaptive sampling and edge computing technology improves the efficiency and real-time performance of data acquisition, the data transmission delay and the central processing pressure are reduced, a streaming processing engine and an intelligent analysis center are combined with various algorithms and models, and the real-time performance of data acquisition is improved. Millisecond-level data processing and anomaly detection are realized, the accuracy and timeliness of fault identification are improved, the root cause positioning and fault prediction functions help operation and maintenance personnel to quickly position the root cause of a problem and formulate preventive measures, and the fault occurrence probability and the influence range are reduced.

The invention discloses a big-data-oriented real-time analysis and fault positioning system, and the system comprises a data collection module which is used for collecting a multi-source data stream, and constructing a high-dimensional feature data set; the confrontation feature compression module is used for compressing high-dimensional features through multi-layer nested mapping and generating initial potential feature representation; the sparrow population module is used for constructing a sparrow individual population containing a plurality of hyper-parameter combinations; the feature evolution analysis module is used for deploying the optimized analysis confrontation model to a big data platform; the abnormal event detection module is used for carrying out sliding window analysis based on the change amplitude and the deviation trend of the potential characteristic track in the time sequence and judging whether a real-time abnormal event is formed or not; the historical alignment matching module is used for marking a specific time index and a source channel of fault occurrence; and the label identification module is used for completing fault type identification and fault type label output based on the fault occurrence time index and the source channel. According to the invention, an accurate big data real-time analysis and fault positioning scheme is provided for the user.

The invention discloses a power distribution system fault analysis method and system. The method comprises the steps of collecting operation related data of a power distribution system in real time through various sensors and intelligent metering equipment; preprocessing the collected operation related data to remove noise and interference signals, and establishing a multi-dimensional feature set of the real-time operation state of the power distribution system; extracting potential fault features from the multi-dimensional feature set by using a convolutional neural network, and generating a fault feature vector; performing time sequence analysis on the fault feature vector by using a recurrent neural network, and identifying a possible fault type in the current power distribution system; and in combination with the topological structure and the electrical model of the power distribution system, spatial positioning is performed on the identified fault type based on the graph neural network, and specific nodes of the fault and corresponding fault severity levels are output. According to the embodiment of the invention, the fault detection capability and response speed of the power distribution system can be remarkably improved, and the power distribution system is promoted to develop towards intelligentization and automation directions.

The invention provides a fault diagnosis method, a fault knowledge graph construction method and device and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring fault alarm data, wherein the fault alarm data refers to data describing fault occurrence conditions; identifying fault entity information in the fault alarm data, wherein the fault entity information comprises at least one of fault entities, relationships among the fault entities and attributes of the fault entities; and based on the fault entity information, searching is carried out in the fault knowledge graph to obtain a fault diagnosis result, and the fault diagnosis result is used for describing a fault reason corresponding to the fault alarm data and / or a fault processing measure.

The invention discloses an electrical pipeline fault early warning method and system, and relates to the technical field of electrical pipelines, and the method comprises the steps: collecting electrical pipeline data, carrying out the preprocessing of the collected data, packaging the processed data, transmitting the packaged data to a central processing unit, analyzing the data, extracting characteristic parameters, and combining a predefined rule base, the method comprises the following steps: carrying out fault feature identification and abnormity determination, matching a fault mode based on identification features, evaluating a fault type and a severity degree, predicting a fault probability in combination with a historical data trend, automatically generating an early warning message when real-time data exceeds a preset threshold value, sending structured early warning information through a multi-mode communication channel, and tracking and confirming a state. And triggering preset emergency operation, and meanwhile, providing a manual intervention interface and recording a processing process. By monitoring the parameters of the electrical pipeline in real time and triggering an early warning mechanism, the safety problem is identified and processed before the fault occurs, the downtime is reduced, the reliability of the pipeline is improved, and preventive maintenance is realized so as to reduce the emergency maintenance cost.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

The invention discloses an error correction method of a neural network processor and a related device, and belongs to the technical field of neural networks. The method comprises the steps that according to a fault PE table obtained through error detection, the fault PE table comprises a two-dimensional list of fault occurrence and position information, a newly detected fault PE is positioned according to the fault PE table, and a multi-stage fault-tolerant decision is executed: i, a fine-grained error correction stage; ii, a coarse granularity error correction stage; according to the current error correction mode, a pipeline submission mechanism or a submission mechanism is selected, and a calculation result is output. According to the method, a mixed granularity fault recovery scheme is adopted, and the error correction function can be achieved under the condition that any standby PE is not added. The fine-grained scheme provides more flexibility and does not waste PE, and the coarse-grained scheme ensures that the reasoning precision is not reduced along with the increase of the failure rate.

The invention discloses a network operation and maintenance method and system based on a digital twinning technology, and the method comprises the steps: collecting and analyzing various data in a real network based on big data, cloud computing and artificial intelligence technologies, and constructing digital twinning network models in one-to-one correspondence with the real network; various fault scenes are simulated in the digital twin network model, and the probability and position of fault occurrence are predicted through a machine learning algorithm; when a fault occurs in the real network, using the digital twin network model to locate the fault type and position; based on the fault type and position, calling a preset repair strategy library or generating a new repair scheme, and implementing repair through an automatic tool; based on network load prediction, user demand analysis and fault prediction results, simulation and optimization of a resource allocation scheme are carried out by using a digital twin network model, and the method is implemented in a real network. According to the invention, rapid response, accurate positioning and intelligent repair of network faults and dynamic optimal configuration of network resources can be realized.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

Disclosed is a method for predicting faults in an electric grid, the method comprising: detecting one or more events (102) that are precursors of one or more faults in the electric grid, the detection is based parameters associated with signals propagating in the electric grid or radiation produced by components in the electric grid; determining one or more locations (104), in the electric grid where the one or more events are detected, the determination is based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, or location-specific sensor measurements; and generating an alert (100) that includes the one or more events, the one or more locations, and one or more components (106) of the electric grid in which the one or more events are detected, the alert corresponds to predictions of occurrences of the one or more faults.

The invention discloses a gate fault detection method and system, and relates to the technical field of fault diagnosis and health monitoring, and the method comprises the steps: collecting a gate vibration signal, extracting a main modal frequency, setting a sampling frequency and a passband range, designing a finite impulse response filter for filtering, and carrying out the optimization through a particle swarm algorithm, thereby obtaining an enhanced signal; extracting features in the enhanced signal for integration to obtain a feature vector, mapping the feature vector to a discrete state set by using a rule threshold matching method, and obtaining a gate state; the method comprises the following steps: acquiring historical data for sampling, constructing a state transition probability matrix, calculating a steady-state probability vector according to the state transition probability matrix, combining a gate state, generating a state sequence track, setting an influence factor and a weight by using an empirical rule, and calculating a fault occurrence probability for risk detection; according to the method, the gate fault detection precision, efficiency and reliability are remarkably improved.

The invention discloses a fault detection method, a chip, a storage medium and an electronic device, and relates to the technical field of computers, and the method comprises the steps: carrying out the edge detection of a pulse signal received from a fault counting pin of a processor, and when the pulse signal is detected to be at a first edge of a first level, carrying out the detection; and the number of the received pulse signals at the first level is accumulated, so that the fault occurrence frequency can be accurately counted. And then, generating an interrupt signal based on the first edge, and sending the cumulative number of the interrupt signal and the pulse signal to the controller unit, so that the controller unit triggers an interrupt program in time and stores the cumulative number in real time, thereby not only quickly finishing fault processing when a fault is detected, but also storing the cumulative number of the fault in real time, and improving the fault processing efficiency. The fault detection accuracy and the fault processing efficiency are improved, and the technical problem that the fault detection accuracy is low is solved.

The invention provides a wide-area synchronous power distribution monitoring system and a working method thereof, and relates to the technical field of power distribution monitoring. The method comprises the following steps: a high-speed sampling ADC unit circuit of the synchronous power distribution terminal samples voltage and current data of a power distribution network according to a set sampling frequency; the voltage and current data of the power distribution network collected by each synchronous power distribution terminal are sent to a server through a 4G / 5G network, and the server monitors the received data in real time and judges a current abrupt change fault and a voltage amplitude abnormity; and if a fault occurs, the server collects synchronous sampling data of the plurality of synchronous power distribution terminals before and after the fault occurs, analyzes characteristics including fault current flow direction and phase change, and adopts a fault positioning algorithm model to determine a line section where the fault position is located to perform fault positioning. According to the invention, the monitoring data of the power distribution terminals at different positions can be accurately synchronized in time, and the fault type can be quickly positioned and the position of the fault point can be accurately calculated through the preset fault judgment logic.

The invention discloses an operation and maintenance fault positioning method and device based on a large model, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: if a system fault is monitored, collecting current system operation and maintenance data in real time, and carrying out the preprocessing of the current system operation and maintenance data, so as to obtain target preprocessed system operation and maintenance data; extracting data features corresponding to the system operation and maintenance data after target preprocessing, inputting the data features into a preset large model, and analyzing the data features through the preset large model to obtain a fault analysis result; and constructing a fault tree corresponding to the fault analysis result based on a fault tree analysis method, determining a fault reason of the system fault through the fault tree, and analyzing the fault reason to complete fault positioning. Therefore, when a fault occurs, various information related to the fault can be rapidly collected, the root cause of the fault can be rapidly and accurately positioned, the troubleshooting time is shortened, and the fault processing efficiency is improved.

The invention discloses a short-circuit current protection method for a direct-current port of a power electronic transformer, which relates to the technical field of power control, and comprises the following steps: when detecting that the short-circuit current of the direct-current port is abnormal, generating a control instruction, driving an ultrafast electronic switch to execute a cut-off action, and recording and uploading the fault occurrence time, position and abnormal parameters; generating a fault diagnosis result and a maintenance suggestion; when the fault diagnosis result and the maintenance suggestion confirm that recovery conditions exist, a recovery process is started, and the power electronic transformer is connected to the direct current power supply loop again; continuously collecting operation state data in real time, and dynamically correcting a short-circuit current response mechanism by adopting a self-adaptive updating algorithm in combination with an operation state data set and abnormal parameters recorded in a fault diagnosis result; by optimizing a protection mechanism and monitoring and analyzing faults in real time, risks and shutdown loss caused by equipment failure or external environment change are reduced.

The invention discloses a power distribution network high-resistance fault positioning method and positioning system based on multistage joint filtering, and the method comprises the steps: collecting the power data of a power distribution network in real time, including a zero-sequence voltage signal and a zero-sequence current signal; constructing a signal flow graph strategy to process the power data by using a multi-stage joint filtering technology, and generating output data; the extraction of fault features is completed while the signal descrambling is carried out; establishing a dynamic threshold value, and performing comparative analysis based on the output data and the dynamic threshold value to determine a fault occurrence moment; based on a fault occurrence moment, generating a fault measure by using a heuristic method and feature polarity accumulation; based on two-dimensional characteristics of fault measurement amplitude and polarity and according to distribution characteristics of fault measurement of each measurement point relative to a fault point, a fault section is positioned through a recursion positioning algorithm containing an adaptive threshold value, a fault feeder line is quickly positioned, recursion positioning is carried out by using the adaptive threshold value, abnormal values caused by interference are eliminated, and the fault feeder line is quickly positioned. Therefore, accurate positioning under complex working conditions is realized.

The invention discloses a power transmission cable ground fault adaptive diagnosis method and system, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing and extraction of the multi-modal data, and forming a statistical feature vector; the method comprises the following steps: adaptively decomposing sheath current data by adopting OVMD, calculating segmented TKEO energy operators, and realizing fault early warning through a ratio criterion of maximum values of TKEO derivatives of adjacent segments; inputting the real-time acquisition time sequence working condition class feature vector into a trained LSTM to obtain a fault probability, dynamically updating a fault probability threshold based on LSTM output, and judging that a fault occurs when the fault probability exceeds a continuous set period of the fault probability threshold; and inputting the statistical class feature vector into ANN, inputting the sheath current data into 1D-CNN for fault classification, and performing adaptive weighted voting integrated decision on the output of the two types of networks to identify the fault type. According to the invention, the problems of low fault detection sensitivity, poor positioning precision, lack of early warning capability and the like in the prior art are solved.

An MMC-based open-circuit fault diagnosis method and system for a medium-voltage direct-current converter. The method comprises: acquiring on / off states and voltage and current signal data of a sub-module in a modular multilevel converter when the sub-module is in a normal operating state and a fault state; establishing different types of fault determination logic on the basis of the on / off states and the voltage and current signal data of the sub-module when same is in the normal operating state and the fault state, and establishing a fault indicator on the basis of different types of fault determination logic; establishing a sub-module capacitor current observation equation on the basis of the structure of the sub-module in the modular multilevel converter, and determining the relationship between an actual value and a predicted value of a capacitor current and a first threshold value; and on the basis of a determination result regarding the relationship between the actual value and the predicted value of the capacitor current and the first threshold value, displaying an open-circuit fault diagnosis result in combination with the fault indicator. In the method and the system, a capacitor current path and on / off state information are used to perform fault diagnosis quickly and accurately when a fault occurs, thereby providing a solution for actual engineering application.

The invention provides a fault determination method based on a network rail tunnel system. The fault determination method can be applied to the technical field of rail vehicle operation and management. The fault determination method comprises the following steps: acquiring first real-time running state information of a target train in a target time period and first real-time running environment information of a running route; determining a target association relationship among the real-time operation state information, the real-time pantograph-catenary monitoring data, the real-time track monitoring data and the real-time tunnel monitoring data based on a predetermined map; and inputting the target association relationship into a trained target map neural network model, and outputting potential fault information related to the operation environment of the target train, the potential fault information at least comprising a potential fault point and a fault occurrence probability, so that before the target train travels to the potential fault point, troubleshooting operation is performed on the potential fault point.