34results about How to "Realize fault identification" patented technology

The invention discloses a method and device for online monitoring and fault identification of a mechanical state of a high-voltage circuit breaker, and relates to the technical field of electrical equipment testing. The method for online monitoring and fault identification of a mechanical state of a high-voltage circuit breaker uses four different sensors to synchronously monitor various parameters of an operating mechanism of the high-voltage circuit breaker during operation, and performs time-domain and frequency-domain analysis on the information obtained by the sensors in different mannersto form a feature hybrid discriminant matrix, thereby realizing on-line monitoring and fault identification of the state of the high-voltage circuit breaker, solving the problem that it is difficultto accurately determine the failure of the high-voltage circuit breaker during the operator's regular inspection only due to perceptual knowledge and experience, improving the inspection efficiency, and saving resources such as manpower and materials.

The invention discloses a railway wagon bogie side frame fracture fault image recognition method, and belongs to the technical field of railway wagon bogie safety. The invention aims to solve the problem of poor reliability due to the fact that the side frame fracture detection of the existing railway wagon bogie is carried out in a manual mode. The method comprises the steps of collecting an original gray image of a truck bogie side frame in operation; determining a side frame area of each grayscale image, preprocessing the side frame areas to obtain side frame area sample images, forming a sample image set from all the side frame area sample images, configuring mark information for each side frame area sample image to form a mark file, and forming a sample data set based on the sample image set and the mark file; training the convolutional neural network inception v2 and the convolutional neural network Faster rcnn, and obtaining a trained inception v2 model and a trained Faster rcnnmodel; and processing the to-be-detected image by using the trained inceptionv2 model and the Faster rcnn model to obtain a corresponding side frame state prediction result so as to realize fault identification. The method is used for bogie side frame fracture identification.

The invention relates to a motor fault diagnosis method and system based on a cavity convolution capsule network, and the method comprises the following steps: (1), obtaining a training sample with alabel, wherein the training sample comprises a motor vibration signal and a corresponding operation state, and the operation state comprises a normal state and a fault type in a fault state; (2) establishing a cavity convolution capsule network, and performing training by using the training sample; and (3) acquiring a to-be-diagnosed motor vibration signal, inputting the to-be-diagnosed motor vibration signal into the trained cavity convolution capsule network, and outputting the operation state of the motor. Compared with the prior art, the method has the advantages that the effective features of the motor signals can be automatically extracted, intelligent fault diagnosis is achieved, the diagnosis accuracy reaches 99% or above, the robustness and generalization ability are high, and theerror recognition rate is remarkably reduced.

The invention relates to a high-pressure diaphragm pump check valve fault diagnosis method based on vibration signal analysis, and belongs to the field of mechanical fault diagnosis and signal processing. The high-pressure diaphragm pump check valve fault diagnosis method based on the vibration signal analysis comprises the following steps that firstly, VMD decomposition is carried out on a vibration signal of a check valve of a high-pressure diaphragm pump, and the number K of decomposition modes is determined through a center frequency to obtain K IMF components with physical significance; the MPE of the IMF components is then calculated to form a multi-scale complexity measure feature vector; and finally, a high-dimensional feature matrix is input into a classifier established by a support vector machine optimized based on a genetic algorithm to identify a working state of the check valve of the high-pressure diaphragm pump. According to the high-pressure diaphragm pump check valvefault diagnosis method based on the vibration signal analysis, the vibration signal of the check valve is denoised and decomposed into the IMF components without mode-mixing through a VMD algorithm, the multi-scale arrangement entropy of each IMF component is calculated to collect fault characteristic information distributed on multiple scales, the correct rate of fault identification of the checkvalve is improved, and higher practicability and engineering significance are achieved.

The invention discloses a CNN based early-stage fault classification method and device of power distribution network. Wavelet decompotion and CNN related theories and methods are introduced to early-stage fault classification of the power distribution network, and whether the methods are reasonable is verified. Waveform approximation and details can be separated by wavelet decomposition, and symptoms are highly related to early-stage faults. The symptoms are combined to form input of the CNN, the CNN learns the combination of the symptoms to capture detail information related to early stage fault, and the early-stage faults are classified. The method is better than traditional detection in the aspects of required data bulk and accuracy, and has significance in early-stage fault classification and handling of the power distribution network.

The invention discloses a method for monitoring the insulation state of epoxy resin insulating media in a sulfur hexafluoride electrical device. The method comprises the steps of (1) acquiring a gas sample, wherein gas in the sulfur hexafluoride electrical device is acquired to serve as a sample to be detected; (2) detection, wherein the sample to be detected is detected with the gas chromatography, the gas-chromatography-mass spectrometry or the infrared spectroscopy to obtain analysis data of COS in the sample to be detected; (3) comparing the analysis data obtained from the step (2) with standard data of COS so that qualification and quantification of COS in the sulfur hexafluoride electrical device can be achieved and the insulation state of the epoxy resin insulating media in the sulfur hexafluoride electrical device can be judged. With COS serving as characteristic gas, whether solid insulating media, including basin-type insulators, of epoxy resin in the SF[6] electrical device are subjected to discharge erosion can be judged accurately.

A device based on network big data storing, collecting and analyzing is disclosed. The device includes a host computer of the device and an installed operating system installed in the host computer, awireless network AP big data collection management software, a wireless network AP fault prediction and diagnosis software, a wireless network AP resource recovery and release software, a wireless network AP user location service software and a wireless network security management software. Also disclosed are a collection and storage method based on the device, a wireless network intelligent operation and maintenance method, a wireless network AP resource recovery and release method and a wireless network security judgment method. The invention has the advantages of strong self-determinationperformance, high stability, extremely high adaptability, safety and expansibility, can meet the needs of schools, government agencies, shopping malls, tourist attractions, improves the utilization oforiginal resources, and improves the level of smart city construction.

The invention provides a power distribution network weak feature fault identification method based on transfer learning, and relates to the technical field of power grid detection. The identificationmethod comprises the following steps: firstly, establishing a 20kV neutral point ungrounded AC power distribution network model, setting two data acquisition points at an outgoing line, and constructing a training sample set and a transfer learning sample set; and training a sparse auto-encoder by using the training sample set to realize high accuracy of fault identification, and finally carryingout transfer learning of the network model by using a small number of transfer sample sets so that the accuracy of the algorithm model can reach 98% under a new topological structure. According to theinvention, in power distribution networks of different topology types, high-resistance fault type identification and fault line selection can be realized, and interference signals of capacitor switching and load switching can be distinguished; and the method is simple in principle, high in reliability, small in training sample number and high in generalization ability, and can realize weak feature fault identification in different power distribution network topologies.

The invention relates to a construction method of a motor bearing fault identification model, in particular to a construction method of a wind driven generator bearing fault identification model. Theinvention aims to provide a construction method of a wind driven generator bearing fault identification model based on deep learning by utilizing a deep learning method, and further realize fault identification and positioning of a wind driven generator bearing. The construction method can greatly improve the accuracy of fault prediction. Compared with other methods, for the construction method, the general applicability and generalization of the deep learning network model to wind driven generator bearing fault identification are greatly improved. According to the construction method, accurate identification of different faults of the existing wind driven generator bearings of various models is easy to realize. The construction method is realized by the following steps: 1, presetting bearing fault types and quantity; 2, acquiring and preprocessing an original signal; 3, creating and configuring a deep learning network; 4, training a network; and 5, verifying the network accuracy.

A method for protect grounding electrode line of UHVDC system include injecting high-frequency current signal at head end of grounding electrode line, injecting high-frequency current signal at head end of grounding electrode line, inject high-frequency current signal at head end of grounding electrode line, injecting high-frequency current signal at head end of grounding electrode line. 2, collecting electric quantity data required by fault criterion of grounding electrode line based on injected high-frequency current signal; 3, jud that symmetry of the ground pole circuit structure based onthe data collect in the step 2; 4, based on the symmetry judging result of the step 3, judging the fault of the earth pole line; 5, aft that grounding electrode line fail, the relay protection devicesends out a fault alarm signal to the monitoring device; The reliability of grounding line protection method is improved.

The invention discloses a current transformer iron core coil fault diagnosis method based on spectral analysis. The method comprises the following steps of classifying typical faults of a current transformer iron core coil; establishing a current transformer equivalent circuit model under the typical faults, and obtaining equivalent circuit parameters of a current transformer iron core coil in a healthy and fault state; constructing a frequency spectrum response analysis circuit, acquiring frequency spectrum characteristic response curves of the current transformer under health, different fault types and different fault degrees, acquiring frequency spectrum change characteristics of the transformer in fault, and establishing a frequency spectrum characteristic library reflecting the state of the current transformer; and acquiring the current frequency spectrum characteristic curve of the current transformer iron core coil, then analyzing the frequency spectrum characteristics, comparing with data in a frequency spectrum characteristic library for analysis, and determining the fault type and the fault degree of the transformer iron core coil. According to the method, accurate diagnosis of the fault reason and the fault degree of the transformer iron core coil is achieved, and the defect that the specific fault type and the fault degree cannot be judged through an existing common method is overcome.

The invention provides a transformer fault identification method based on a kernel function extreme learning machine, and the method comprises the steps: obtaining a vibration signal as an analysis sample under the operation condition of a transformer, carrying out the noise elimination of the vibration signal, obtaining each frequency energy characteristic value of the vibration signal based on wavelet packet decomposition and reconstruction, extracting each frequency energy characteristic value as a fingerprint vector, dividing the fingerprint vectors into two sample sets, namely a training set and a test set, establishing an optimized kernel function extreme learning machine network model, performing model training by utilizing a fingerprint vector training set, inputting a to-be-tested set into the model, analyzing, calculating and outputting a check result, obtaining a working state of the transformer, and achieving fault identification of the transformer. According to the method, the abnormal problem that the system is caught in dimensionality disaster is solved, the performance of a fault judgment analysis result is further optimized and improved, and automatic identification of the abnormal fault of the power transformer is achieved.

The invention relates to a deep learning coalbed methane screw pump well health index prediction method. The method comprises one or more of the following steps: selecting at least one of a plurality of original parameters collected from a coalbed methane screw pump well as a main control parameter; performing merging processing on the at least one master control parameter to construct a health index; dividing the health state of the coal bed gas screw pump well into at least two different stages according to the health index; extracting health index data of the coal bed gas screw pump well as sample data, and constructing a health index prediction model by adopting a long-short term memory neural network; and predicting the health state change of the coal bed gas screw pump well by using the health index prediction model.

According to the invention, considering that unknown faults which are not considered in the operation process of a wind turbine generator are also likely to occur in addition to known faults, a wind generating set variable pitch bearing fault identification method based on an improved hidden Markov model is disclosed, comprises offline modeling and online identification, in the offline modeling step, a threshold statistic is defined based on the hidden Markov model, and the threshold statistic is used for identifying unknown faults. According to the method, a new threshold statistic is introduced, so that the probability that other unknown faults are mistakenly recognized as the variable-pitch bearing faults can be greatly reduced when online recognition is carried out on the variable-pitch bearing faults of the wind generating set. Therefore, compared with other existing methods, due to the fact that the time sequence of the data and the occurrence of unknown faults are fully considered, the method has higher accuracy for identifying the variable pitch bearing faults of the wind generating set.

The invention relates to a power distribution cabinet cable joint looseness early-warning method based on real-time data acquisition. The method comprises steps that a cable joint looseness early-warning comprehensive judgment expression is built through comprehensive analysis of cable joint looseness accident data and the corresponding relation between online monitoring data and joint fault data in a high-voltage power distribution cabinet; firstly, electrical parameters and external environment parameters influencing the stability of a cable joint are collected, calculated and stored to form a complete cable joint loosening accident data characteristic model; and then the corresponding relationship between the online monitoring data and the joint fault data in the power distribution cabinet is analyzed, the loosening state of the cable joint is judged more accurately, and fault identification is realized.

The invention provides a pilot protection method and device and a storage medium. The method comprises the following steps: acquiring time domain signal data of a target element at a preset sampling frequency; performing fusion processing on the time domain signal data of the plurality of first sampling periods to obtain first time domain signal combined data; based on a machine learning model, judging whether the target element has a fault according to the first time domain signal combination data; when it is judged that the target element breaks down according to the first time domain signal combination data, whether the target element breaks down in a second sampling period or not is judged according to the second time domain signal combination data based on a machine learning model, and the second sampling period is a sampling period after it is judged that the target element breaks down; and when it is judged that the target element has the same type of faults in multiple continuous second sampling periods, controlling the pilot protection system to execute a protection action on the target element. A machine learning model is utilized to realize fault identification of power system elements, so that the protection performance of a smart power grid is remarkably improved.

The invention discloses a power transmission line low-power-consumption control device and method. The device comprises an edge computing intelligent terminal, a low-power-consumption service node, an external sensor and a power supply module. The external sensor is used for monitoring an external environment to obtain monitoring data and transmitting the monitoring data to the low-power-consumption service node; the edge computing intelligent terminal is connected with the low-power-consumption service node and used for receiving the monitoring data sent by the low-power-consumption service node, achieving fault recognition through an artificial intelligence algorithm according to the monitoring data and entering a dormant state after fault recognition is completed; and the low-power-consumption service node is used for receiving and storing the monitoring data of the external sensor, awakening the edge computing intelligent terminal in the dormant state at a specific time interval and sending the monitoring data to the edge computing intelligent terminal. According to the invention, the edge computing intelligent terminal automatically enters the dormant state after completing fault identification, so that the demand of the power transmission line device for power consumption is reduced.

The invention discloses a circuit breaker fault identification method based on time-domain statistical characteristics of the closing coil current, which collects the current data of the closing coil during the closing operation of the circuit breaker, and extracts its feature points; calculates the characteristic points of the signal to be identified and the known The Euclidean distance between the feature points of the category signals, and find out the multiple signals closest to the feature points of the signal to be identified, and judge the state of the circuit breaker where the signal to be identified is located according to the circuit breaker status of these signals, so as to realize the circuit breaker. fault identification. The invention can diagnose the common mechanical faults of the operating mechanism of the circuit breaker, and accurately locate the faulty components and faulty parts when the fault occurs, thereby helping maintenance personnel to handle faults, improving maintenance efficiency, and ensuring safe and reliable operation of the circuit breaker.

The embodiment of the invention relates to troubleshooting technology, and discloses a method, device, processor and storage medium for generating a troubleshooting strategy for an Oracle database. This method creates abstract Oracle troubleshooting rules based on Oracle troubleshooting rule data. Abstract Oracle troubleshooting rules include abstract configuration events and abstract configuration rules. When the troubleshooting start condition is triggered, according to the abstract Oracle troubleshooting rules and Oracle troubleshooting knowledge graph Generate an example Oracle troubleshooting diagram. The Oracle troubleshooting knowledge map includes fault characteristics and corresponding failure reasons. The example Oracle troubleshooting diagram includes instantiated virtual events and instantiated abstract configuration rules. For different troubleshooting scenarios, according to expert experience and fields Knowledge, establish troubleshooting rules in different scenarios, effectively solidify the known expert troubleshooting experience, and realize expert experience storage, fault identification, and troubleshooting reasoning in automated troubleshooting. At the same time, the entire process is streamlined, visualized, and automated. , to speed up the troubleshooting process.

A method for protect grounding electrode line of UHVDC system include injecting high-frequency current signal at head end of grounding electrode line, injecting high-frequency current signal at head end of grounding electrode line, inject high-frequency current signal at head end of grounding electrode line, injecting high-frequency current signal at head end of grounding electrode line. 2, collecting electric quantity data required by fault criterion of grounding electrode line based on injected high-frequency current signal; 3, jud that symmetry of the ground pole circuit structure based onthe data collect in the step 2; 4, based on the symmetry judging result of the step 3, judging the fault of the earth pole line; 5, aft that grounding electrode line fail, the relay protection devicesends out a fault alarm signal to the monitoring device; The reliability of grounding line protection method is improved.

The present invention discloses a circuit breaker fault identification method based on closing coil current time domain statistical characteristics. The method collects current data of a closing coil during closing operation of a circuit breaker, and extracts characteristic points thereof; and calculates the Euclidean distance between a characteristic point of a signal to be identified and a known category signal characteristic point, finds a plurality of signals that are closest to the characteristic point of the signal to be identified, and according to state of the circuit breaker of the signals, determines the state of the circuit breaker where the signal to be identified is located, thereby realizing fault identification of the circuit breaker. The method can diagnose common mechanical faults of the circuit breaker operating mechanism, and accurately locate faulty components and faulty parts when fault occurs, thereby helping maintenance personnels to perform fault handling, improving maintenance efficiency, and can ensure safe and reliable operation of the circuit breaker.

The application discloses a method for fault identification of photovoltaic modules. A flight device is equipped with a GPS module and a thermal imager. The method includes: the flight device detects the photovoltaic modules to be detected in the photovoltaic power station according to the set cruising route and cruising altitude. The area is cruised; during the cruise, the thermal imager shoots and outputs the thermal distribution map of the cruise area, and the GPS module determines and outputs the GPS coordinates when the thermal imager shoots each thermal distribution map; in the output Identifying photovoltaic components in all thermal distribution maps; identifying faulty photovoltaic components in each thermal distribution map taken, and determining the actual geographic location of the faulty photovoltaic component. By applying this application, fault identification of photovoltaic modules can be realized simply and efficiently.

The invention discloses a relay protection and control logic fault location method and device. The fault location method includes: responding to the acquired data of each node, judging whether a protection event in each node data has an action; Take the substation configuration data associated with the protection event in the preset logical relationship library, and dynamically create a logic analysis data table associated with the protection event that has occurred; based on the substation configuration data, fill the logic analysis data table in real time; respond In order to obtain the instruction to complete the filling, it is judged whether there is a missing item in the logical analysis data table. If there is a missing item in the logical analysis data table, the fault location is performed based on the association relationship of the configuration data that has not been filled. According to the association relationship of the missing configuration data, the cause of the logical fault can be quickly found out, thereby realizing the automatic fault identification of the protection and control logic.

The invention belongs to the technical field of rolling bearing fault recognition, and discloses a low-speed heavy-duty bearing fault recognition method and system, a medium, equipment and a terminal, and the method comprises the steps: carrying out the filtering decomposition of a signal, solving the feature quantities of the first three component signals obtained through the decomposition, and constructing a feature value matrix; carrying out dimensionality reduction on the characteristic value matrix by adopting a distance evaluation technology, and screening out significant characteristics; and inputting the significant features into a BP neural network for training and testing, thereby realizing fault identification of the low-speed heavy-duty bearing. Aiming at the problems of broadband, non-stability and strong noise of an original signal, the method focuses on analyzing the step of filtering decomposition of the signal, solves the characteristic quantities of the first three component signals obtained by decomposition and constructs a characteristic value matrix, adopts a distance evaluation technology method to carry out dimension reduction on the characteristic value matrix, screens out significant characteristics, and finally obtains the characteristic value matrix. And inputting the significant features into a BP neural network for training and testing, thereby realizing accurate identification of the fault type of the low-speed heavy-duty bearing.

The invention provides a motor train unit traction converter performance detection method and device and terminal equipment. The method is applied to the field of data processing, and comprises the steps of obtaining target detection data of a motor train unit traction converter, wherein the target detection data comprises a target motor train unit speed and target performance influence information of multiple groups of traction converters corresponding to the target motor train unit speed, determining a target performance index value of a traction converter of the motor train unit based on the target speed of the motor train unit and a preset performance detection model, determining theoretical performance index values of the traction converters of the motor train unit based on the target performance influence information of the multiple groups of traction converters, and detecting the performance of the traction converter of the motor train unit according to the target performance index value and the theoretical performance index value. According to the motor train unit traction converter performance detection method and device and the terminal equipment provided by the invention, the working state of the traction converter can be evaluated more accurately.

The invention discloses an early fault classification method and device of a distribution network based on a convolutional neural network. The relevant theories and methods of wavelet decomposition and convolutional neural network are introduced into the early fault classification of distribution network, and the rationality of the method is verified. Wavelet decomposition can isolate waveform approximations and details that are relevant for early failures. Combining these representations constitutes the input to a convolutional neural network. The convolutional neural network can classify early faults by learning the combination of these representations and capturing the detailed information about early faults. This method is much better than traditional detection in terms of required data volume and accuracy. It is of great significance to the classification and treatment of early faults in distribution network.

The invention discloses a 500kV high-voltage cable fault automatic identification system, which is characterized by comprising a plurality of detection terminals, a cable fire-fighting terminal and anidentification controller, wherein the identification controller identifies the type of a fault occurring in a high-voltage cable based on data received from the detection terminals and the cable fire-fighting terminal and sends a processing instruction based on the type of the fault. The 500kV high-voltage cable fault automatic identification system disclosed by the invention can realize automatic detection and identification of high-voltage cable line faults, and can identify a fault in real time and output a processing instruction, so as to reduce the processing delay. System breakdown caused by a fault of a high-voltage cable is avoided to the greatest extent, and the working reliability of the power system is greatly improved.

The invention discloses a three-phrase pulse-width modulation (PWM) rectifier fault diagnosis method based on wavelet packet analysis and a support vector machine. The three-phrase PWM rectifier fault diagnosis method based on wavelet packet analysis and the support vector machine includes the steps: first, building a three-phrase PWM rectifier, determining classification principles and utilizing a wavelet packet arithmetic to analyze a direct current side output voltage of the rectifier; then, conducting energy spectrum and power spectrum analysis on a rebuilt small signal, determining a fault characteristic vector and building a data sample; and finally, choosing a support vector machine kernel function and a parameter, and building a multiple-value classifier so as to achieve fault diagnosis of the three-phrase PWM rectifier. The three-phrase PWM motor-generator set fault diagnosis method based on wavelet packet analysis and the support vector machine can improve fault diagnosis rate of the three-phrase PWM motor-generator set, avoid the problems of the data process and optimization of the traditional test method and effectively improve safety of an electric and electronic rectifier device.