198results about How to "Improve fault detection efficiency" patented technology

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses a power grid anomaly detection and diagnosis method based on artificial intelligence and topological data analysis. The method comprises the steps of S1, forming power operation data; s2, constructing a topological network model of the power grid based on the physical structure of the power grid; s3, using a first generator to generate abnormal data in the simulation power grid based on the power operation data; s4, optimization abnormal data generation is carried out; s5, embedding the power operation data and topological structure features of the power grid into the anomaly detection model through a graph convolutional network; s6, power grid anomaly detection parameters are dynamically adjusted through the adaptive learning model, so that the anomaly detection model is continuously optimized along with the change of the power grid operation condition; and S7, based on the topological information and abnormal data generated by the generative adversarial network, combining topological data analysis, and positioning a fault node or an abnormal line in the power grid. According to the invention, the accuracy of fault prediction and positioning is improved.

The invention discloses a power distribution network high-resistance grounding fault detection method and system, and relates to the technical field of power distribution network fault detection. A power distribution network high-resistance grounding fault detection method comprises the following steps: S1, collecting a transient zero-sequence voltage signal of a target feeder line as a to-be-decomposed electric quantity signal; s2, variational mode decomposition is carried out on the to-be-decomposed electric quantity signals, mode functions obtained by decomposing the single to-be-decomposed electric quantity signals are arranged according to the center frequency from low to high, and one-dimensional feature vectors of the to-be-decomposed electric quantity signals are obtained; and S3, inputting the one-dimensional feature vector of the electric quantity signal to be decomposed into a pre-constructed Transform model to obtain a detection result. According to the method, the high-resistance grounding fault and the disturbance condition can be accurately identified, the fault identification accuracy is very high, and the power distribution network fault detection efficiency is improved.

The invention discloses a multi-scene visual algorithm evaluation method based on an electric power artificial intelligence platform, and the method comprises the following steps: S1, integrating data in an electric power scene, and generating a multi-modal data set; s2, storing and preprocessing the data set, and extracting key features; s3, based on a multi-stage active learning mechanism, selectively sampling data and optimizing a visual algorithm; s4, the optimized algorithm is migrated to different environments, parameters are adjusted through a self-adaptive strategy, and models adapting to different conditions are generated; s5, in combination with deep learning and expert knowledge, optimizing an evaluation process; s6, performing real-time fault injection and elasticity evaluation by using a fault mode library and a simulation technology; s7, constructing a double-layer digital twinning virtual evaluation environment, and verifying the validity of the algorithm under different conditions; and S8, recording and tracking the evaluation data and generating an evaluation report by using a distributed data management system. According to the invention, through an intelligent tuning and evaluation mechanism, the efficiency and reliability of power equipment monitoring and fault prediction are improved.

The invention relates to an engine tail flame anomaly detection method and system based on spectrum contrast, and the method comprises the steps: simulating an ideal combustion process of an engine based on a radiation model, generating an ideal tail flame spectrum for reference, and extracting the reference spectrum intensity of a target substance; the actual tail flame spectrum of the tail flame of the engine to be detected is collected, and the actual spectrum intensity of the target substance is obtained; based on the reference spectral intensity and the actual spectral intensity, obtaining dynamic characteristics used for judging whether the to-be-detected engine fails or not; constructing a dynamic threshold range for judging whether the dynamic characteristics are abnormal or not; whether the dynamic features are abnormal or not is judged based on the dynamic threshold value range, if the dynamic features exceed the dynamic threshold value range, the dynamic features are abnormal, and if the dynamic features are within the dynamic threshold value range, the dynamic features are not abnormal. According to the invention, organic combination between simulation and actual monitoring is realized, and high-precision and real-time fault diagnosis can be flexibly realized.

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 transformer impact fault positioning method based on distributed parameter inversion calculation, and the method comprises the steps: carrying out the impact withstand voltage test of a transformer, collecting a voltage signal and a current signal of the transformer in real time, and calculating a transfer function; establishing a distribution parameter network of the transformer, performing inversion calculation on parameters in the distribution parameter network to obtain parameter values of the transformer equivalent distribution parameter network, and establishing a digital twinborn model of the transformer; simulating fault types of different positions of the transformer, and generating a transfer function data set under different node fault conditions; under the full-impact test voltage, a transfer function obtained through actual measurement is obtained; and evaluating the similarity of the faults at different positions, and determining a simulation fault position with the maximum similarity with the transfer function obtained by actual measurement. According to the method, the efficiency and accuracy of fault detection are effectively improved, the time and cost of fault diagnosis are reduced, and the method has important practical application value for maintenance and fault processing of the transformer.

The invention provides a high-voltage circuit breaker partial discharge monitoring and early warning method and system, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: obtaining the feature information of a target circuit; a circuit analysis model is called for analysis, and a target analysis result is obtained and comprises a plurality of target point locations; dynamically monitoring the first target point location through an ultrahigh frequency sensor to obtain first partial discharge information; calling a signal analysis model for processing and analysis to obtain a first signal feature set; activating a fault diagnosis model to analyze the first signal feature set to obtain a first diagnosis tag; and when the partial discharge fault of the target high-voltage circuit breaker at the first target point location is displayed, a first early warning signal is sent out. The technical problems that in the prior art, partial discharge monitoring mostly depends on artificial experience or fixed rules, intelligent analysis means are lacked, misjudgment or missed judgment is prone to occurring, accurate positioning is difficult, and the safety of the high-voltage circuit breaker is reduced are solved.

The invention relates to the field of intelligent water meter data analysis, in particular to an intelligent water meter data analysis method and system. The intelligent water meter data analysis system comprises a data acquisition module, a fault risk assessment module, an intelligent water meter classification module and a fault detection module. According to the method, the total water consumption, the water quality data, the water consumption behavior data and the historical fault frequency of the intelligent water meter are comprehensively analyzed, and the fault risk of the intelligent water meter of each user is evaluated in a customized mode; the personalized evaluation mechanism not only improves the accuracy of fault detection, but also can provide more accurate fault early warning for the water use habits and environmental conditions of different users, thereby effectively avoiding the problems of false alarm and missing alarm possibly caused by an excessively single detection method.

The invention discloses a model training method and device, a fault prediction method and device, a medium and a program product in the technical field of computers. According to the invention, the multi-modal fusion of the operation data, the log statistical data and the calling delay information of each micro-service in the micro-service system is used to obtain the fusion information corresponding to each micro-service, and then the multi-modal fusion information and the calling graph structure formed by each micro-service are used to train the teacher module. The generality and the difference between different modal data can be captured, and a complex calling relation can be infiltrated into a fault prediction process; a student module is trained through multi-modal fusion information, and a basis is provided for efficient fault detection; effective knowledge distillation is completed through comparative learning in loss calculation, a finally obtained fault prediction model comprising a data processing module, a student module and a fault prediction module can integrate calling relations and multi-modal data among micro-services, and the fault detection efficiency and accuracy of the micro-service system are improved.

The invention discloses a cloud-side collaborative smart grid fault processing method, device and equipment and a storage medium, and belongs to the technical field of grid fault analysis, and the method comprises the steps: reading historical data, obtaining grid fault data, and obtaining grid parameters, the parameter time sequence is formed by power grid parameters at multiple time points, an analysis module is established, a recurrent neural network (RNN) is established according to the power grid fault data and the power grid parameters, the input of the recurrent neural network (RNN) is the parameter time sequence, and the input of the recurrent neural network (RNN) is the parameter time sequence; the output of the recurrent neural network is the probability of occurrence of the fault type and the probability of occurrence of different fault levels, and the power grid fault analysis scheme based on the RNN neural network can significantly improve the efficiency and accuracy of fault detection, and provides powerful support for safe and stable operation of the power grid.

The invention discloses a tower fault detection method and system. The method comprises the following steps: establishing an initial tower finite element model; obtaining material attribute data of a to-be-measured tower, and establishing a target to-be-measured tower finite element model in combination with the initial tower finite element model; acquiring first data of a to-be-measured tower, and acquiring second data in combination with the finite element model of the target to-be-measured tower; and comparing the second data with the first historical data set, and carrying out fault detection on the to-be-detected tower. By establishing the tower finite element mechanism model, performing simulation analysis on the stress-strain states of the tower under different working conditions, and integrating the formed stress field and deformation field data to establish the data model, the real-time analysis of the stress state of the tower and the output of the field data are realized, and the efficiency of tower fault detection is improved.

The invention discloses a fault detection method and device for a power distribution system, and a terminal equipment storage medium, and the method comprises the steps: dividing a target power distribution system into a plurality of power distribution regions, and laying current sensors between the power distribution regions, so as to enable each current sensor to obtain a current vector and a current value at each moment; constructing a current identification matrix of the target power distribution system according to the current vectors of the current sensors; wherein each element in the current identification matrix represents the current flow direction of each power distribution area; for each current sensor, calculating a current phase vector at each moment according to a current value and a current vector at each moment and the current identification matrix; and determining a fault current sensor and a fault power distribution area according to the current phase vector at each moment, the preset difference threshold value of each current sensor and the current identification matrix. According to the invention, the fault detection efficiency and the fault detection accuracy can be improved.

The invention relates to the technical field of fault detection, and discloses a fault detection method and system for a charger, and the method comprises the steps: obtaining the charging state of a battery, and the charging state of the battery comprises the charging current, the charging power, the state of charge, and the temperature of the battery; performing feature extraction on the battery charging state, and constructing a charging feature matrix; inputting the charging characteristic matrix into a battery health prediction model to obtain a battery charging demand prediction value; performing state analysis and calculation according to the battery charging state to obtain a charger state; performing correlation analysis according to the charger state and the battery charging demand prediction value to obtain fault prediction information; and obtaining a charger fault detection result according to the fault prediction information. The method can improve the fault detection efficiency of the charger.

The invention relates to the technical field of transformer fault detection, and particularly discloses a transformer fault monitoring method and system, and the method comprises the steps: obtaining the historical fault parameters of a transformer, and drawing a transformer fault parameter change curve according to the historical fault parameters of the transformer; clustering the curve period according to the transformer fault parameter change curve, and determining a fluctuation tolerance threshold value of the curve period according to a clustering result; determining a fluctuation index of the transformer according to the fluctuation tolerance threshold value, setting an initial # imgabs0 # value, and correcting the initial # imgabs1 # value according to the fluctuation index of the transformer; and extracting abnormal data points of a curve period based on an LOF algorithm according to the corrected # imgabs2 # value, and monitoring the current operation parameters of the transformer according to the abnormal data points of the fault period. The operation parameters of the transformer are monitored in real time, abnormal data points are quickly positioned, and operation faults are found in time.

The invention discloses a fault detection method and system for an electric energy metering device. The fault detection method of the electric energy metering device comprises the following steps: acquiring a historical electric energy metering data table and a current day electric energy metering data table of the electric energy metering device; determining an electric energy change metering data table in the electric energy metering data of the current day according to the electric energy metering data table of the current day and the historical electric energy metering data table; and determining the fault type of the electric energy metering device according to the state of the fault screening item in the electric energy change metering data table. According to the invention, the efficiency and accuracy of fault detection are effectively improved, and stable operation of a power system is ensured.

The invention relates to a cluster fault detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of reading configuration information carried by a custom resource when a creation event of the custom resource for detecting a cluster fault is monitored; determining a to-be-detected node and a to-be-detected container according to the configuration information, and creating a fault detection container; for each to-be-detected node, taking the node namespace of the to-be-detected node as an access object, controlling a fault detection container distributed to the to-be-detected node to access the access object, and controlling a fault detection script to be executed in the node namespace to obtain a node fault detection result corresponding to the to-be-detected node; and taking the container namespace of the to-be-detected container deployed in the to-be-detected node as an access object, and controlling the fault detection script to be executed in the container namespace to obtain a container fault detection result corresponding to the to-be-detected node. By adopting the method, cluster faults can be efficiently detected.

The invention discloses a fault detection system for power distribution equipment, and relates to the technical field of fault detection, and the system comprises a power distribution equipment prior data acquisition module, a power distribution equipment fault item evaluation module, an unmanned aerial vehicle building module, a fault detection execution terminal and a database. And the pre-estimated fault item set of the transformer in the region is determined, the fault of the high-altitude transformer is predicted, and data support is provided for follow-up targeted inspection of the transformer in the region. Based on regional transformer group tags, each detection route in the region is generated, blindness of transformer inspection is avoided, the fault detection efficiency of subsequent transformers is improved, the frequency of power grid faults is reduced, convenience of high-altitude transformer inspection by the unmanned aerial vehicle is improved, redundancy of sensors carried by the unmanned aerial vehicle is also avoided, and the reliability of transformer inspection is improved. Operation load of the unmanned aerial vehicle is reduced, and use efficiency and service life of the unmanned aerial vehicle are guaranteed.

The present invention relates to the field of solid-state drive fault prediction, and in particular to a solid-state drive fault prediction method and system based on artificial intelligence. The method comprises the following steps: fine-grained division of the solid-state drive into storage units, and serialization of the storage units to construct a storage unit sequence for each partition; obtaining a solid-state drive operation log; performing multi-time point state evolution on the storage unit sequence for each partition based on the solid-state drive operation log, and performing deep dynamic evolution learning to generate a state evolution law for each storage unit; performing periodic access load peak calculation on the solid-state drive operation log, thereby generating a simulated access data packet for each storage unit; performing dynamic pressure access simulation based on the state evolution law of each storage unit and the simulated access data packet of each storage unit to obtain response state evaluation data for each storage unit. The present invention realizes efficient and accurate solid-state drive fault prediction.

The invention discloses a fault self-checking and error-reporting system for a robot, and relates to the technical field of computer software, and the fault self-checking and error-reporting system comprises a power electrical detection module, a physical mechanical detection module, a software detection module, an ethical detection module and a fault feedback module. The power electrical detection module is used for detecting short circuit and overload fault conditions in a robot power electrical system; the physical mechanical detection module is used for detecting the fault condition of physical hardware, and the physical hardware comprises but is not limited to a motor, a sensor and electronic skin. According to the fault self-checking and error-reporting system for the robot, a plurality of modules are integrated, so that the operation condition of each component of the robot can be comprehensively and quickly detected and reflected, the fault detection and diagnosis efficiency is remarkably improved, maintenance personnel can quickly position and solve the fault problem, and the fault detection and diagnosis efficiency is improved. The maintenance cost and the maintenance difficulty are reduced, and the running state of the robot can be monitored in real time.

The embodiment of the present invention provides a method and system for constructing a digital twin of a panel production line, which belongs to the field of data processing technology. The method includes: obtaining a first image of a first target plate and a second image of a second target plate, and obtaining first sensor data of multiple initial sensors for data detection; performing target extraction on the first image to obtain first target information and performing target extraction on the second image to obtain second target information, and determining first abnormal information based on the first target information and the second target information; performing abnormal data identification on the first sensor data to obtain second sensor data, and screening from the initial sensors based on the second sensor data to obtain second abnormal information; determining abnormal equipment based on the first abnormal information and the second abnormal information; generating abnormal pictures based on the first abnormal information and the second abnormal information; constructing a digital twin of the target production line based on the abnormal pictures to obtain a real state model of the target production line.

The invention discloses a wind turbine generator variable pitch communication fault monitoring system and device, and relates to the technical field of monitoring systems.The wind turbine generator variable pitch communication fault monitoring system comprises an on-site monitoring system which comprises a wind turbine generator variable pitch control unit, an on-site monitoring module, a data acquisition module and a communication module and is used for acquiring, processing and transmitting monitoring data of a unit in real time; and the central monitoring system comprises a data receiving module, a data storage library, a state judgment module and a fault signal sending module, and is used for receiving, storing and analyzing data and early warning faults. According to the method, equipment damage and casualty risks caused by fault upgrading are avoided, powerful support is provided for safe and stable operation of the wind generating set, and the shutdown risk and the maintenance difficulty are remarkably reduced.

The embodiment of the invention provides a volatile storage chip fault detection method and device and electronic equipment. In the embodiment of the invention, for each created thread, a part of space in the current free memory space of the NUMA node bound with the thread is used as the distributed test memory block of the thread, so that the data does not need to be read and written across the NUMA node in the process of carrying out pressure read-write test on the test memory block, and the test efficiency is improved. The problem that the read-write efficiency is reduced due to the fact that a memory is randomly distributed on a heap to serve as a test memory block can be avoided, and therefore the fault detection efficiency is improved; according to the embodiment of the invention, the pressure read-write test is carried out on each volatile storage chip configured by the electronic equipment in parallel through the created N threads under the same process, so that the fault detection of the volatile storage chips is realized, and the fault detection efficiency can be further improved through the multi-thread concurrent test mode under the single process.

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 relates to an intelligent fault detection method for a rotating machine based on a nonparametric self-adaptive dynamic threshold. The method comprises the following steps: constructing a sample feature data set in a normal working state; generating a model through a preset mechanism characteristic, and generating a virtual fault sample; training a preset LSTM-AE network model by using the sample feature data set to obtain an identification model of normal engine data; identifying a preset test set by using the identification model of the normal engine data to obtain a reconstruction error of the test set; wherein the test set comprises sample feature data in a normal working state and a virtual fault sample; comparing the reconstruction error of the test set with the reconstruction error of the sample feature data set, and if an error threshold value exceeds a preset dynamic threshold value; and if yes, determining that the vibration signal is abnormal. According to the method, the detection process is more efficient and reliable, the false alarm rate and the missing report rate are remarkably reduced, and an effective solution is provided for fault detection and quality control of the rotating machine.

The invention provides a battery fault detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage data of a single battery; respectively determining a voltage curve and a median voltage curve of the single battery according to the voltage data of the single battery; according to the voltage curve and the median voltage curve of the single battery, determining a deviation value between the voltage curve and the median voltage curve of the single battery; for any battery monomer, acquiring an average normalized value of the deviation values and a mean value of standard scores of the deviation values as characteristic data of the battery monomer; determining a local outlier factor of the single battery according to the characteristic data of the single battery; and when the local outlier factor of the single battery is smaller than a preset value, determining that the single battery does not have a fault. The battery fault detection is realized by combining the standard score related to the battery voltage and the average normalized value, and the information related to the battery voltage can be fully utilized, so that the accuracy of the battery fault detection can be improved.

The invention discloses a method and a device for detecting a high-resistance grounding fault of a power distribution network. The method comprises the following steps of: acquiring residual current measured by an annular current transformer mounted in a transformer substation, residual voltage along a feeder line and a V2 / V1 ratio at the downstream of a high-resistance grounding fault test point; and detecting the high-resistance grounding fault of the power distribution network according to the residual current, the residual voltage, the V2 / V1 ratio and a preset high-resistance grounding fault detection rule. According to the invention, the efficiency of high-resistance grounding fault detection can be improved.

The invention discloses a booster station fault detection method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the main technical scheme comprises the steps: obtaining the electrical quantity data of a to-be-detected target; inputting the electrical quantity data into a preset fault detection model to obtain a fault judgment result output by the preset fault detection model; and sending the electronic quantity data and the fault information to a scheduling end for storage under the condition that the existence of the fault information is determined in the fault judgment result. The electrical quantity data of the to-be-detected target is acquired and input into the preset fault detection model, and detection is performed through the preset fault detection model so as to judge whether the to-be-detected target has a fault or not. The efficiency and accuracy of fault detection are improved, and timely response can be ensured when a fault occurs. Meanwhile, the electronic quantity data containing the fault information is sent to the scheduling end to be stored, so that subsequent fault analysis and processing are facilitated.

This specification discloses a hydraulic brake circuit fault diagnosis method based on the OneBox wire control brake system, including four parts: electric cylinder circuit fault diagnosis, master cylinder and simulator brake circuit fault diagnosis, PSV fault diagnosis, and CSV fault diagnosis. The embodiment of this specification detects brake system circuit faults through four diagnostic processes, uses pressure sensors and electric cylinder units to achieve pressure detection and pressure increase and decrease control, realizes pressure building function diagnosis, valve, hydraulic cylinder, wheel cylinder leakage performance diagnosis; valve, electric power unit jam fault diagnosis, can quickly and comprehensively detect the boost isolation valve, master cylinder isolation valve, simulation valve, pedal simulator, isolation valve and wheel cylinder with high accuracy.