185results about How to "Improve troubleshooting speed" patented technology

The invention relates to a video aided analytical method for criminal investigation and case detection and a system; the system comprises a case file management device, a video library management device, a video decoding and playing device, a blur video processing device, a video analysis and target feature extraction device, a video indexing device and a string case combination analysis device; the method comprises the following steps: case files are built; the related video in the case is imported in a computer system, and the videos related with the case are associated with the case by a relational database; the videos related with the case is analyzed intelligently, feature information of an interested target is extracted from the video and is stored; a user inputs indexing conditionson a system interface, so as to comprehensively and intelligently index the videos and search the video clips. The system has the functions of intelligent indexing to the videos, quality improvement and string case-combination analysis, realizes unified management of the case videos and the comprehensive analysis platform, improves the video inspection speed, and enhances the effect and accuracy of fighting against crimes.

Disclosed in the invention is a three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method. The method is characterized in that faults of the photovoltaic array are classified into four types including an assembly short-circuit type, an assembly open-circuit type, a shadow type, and a multi-fault type under the circumstances that the photovoltaic array is in a formation mode of series connection and parallel connection. During a first-stage fault diagnosis execution process, whether a fault occurs at a branch circuit where a photovoltaic grid-connected inverter as well as the type of the fault is determined by using a power incremental ratio method; during a second-stage fault diagnosis execution process, whether faults occur at all photovoltaic assembly strings of the branch circuit of the photovoltaic grid-connected inverter is determined by using a current and voltage similarity method; and during a third-stage fault diagnosis execution process, positioning of an internal fault assembly of the fault photovoltaic assembly string is carried out by using a trained three-layer BP fault diagnosis neural network. According to the invention, on-line real-time detection of the operation state of the large-scale photovoltaic array is carried out based on the three-stage diagnoses, thereby realizing accurate and rapid fault type identification and fault positioning.

The invention discloses a fuzzy rough set and decision tree-based track circuit red light strip fault positioning method. The method mainly comprises the following steps of: 1) establishing an initial decision table; 2) carrying out fuzzy discretization on continuous fault feature attributes to establish a fuzzy decision table; 3) inputting fault sample training data to obtain a reduced decision table; 4) establishing a diagnosis decision tree model; 5) inputting measured data into the diagnosis decision tree model, carrying out calculation to obtain a fault diagnosis result, inputting the measured data into a diagnosis positioning decision tree model, carrying out preliminary judgement to obtain a fault positioning result, judging faults of specific equipment by combining expert experiences, and giving corresponding fault maintenance suggestions. The method can rapidly and correctly position fault points of uninsulated frequency shift track circuit red light strip faults, greatly reduce the blindness and complexity of fault diagnosis, have relatively good rule explanation and relatively good robustness, improve the fault positioning speed and correctness and provide a new fault positioning technological means for intelligent fault diagnosis of track circuits.

The invention discloses a novel power system electric transmission line fault diagnosis method. The method includes the steps that first, fault recording wave signals of an electric transmission line are extracted, and a three-phase current value and a discrete value of an earth current of the electric transmission line are obtained through discrete wavelet transform; then, according to a wavelet function db10, the wavelet coefficient entropy of each phase current is calculated, the sum of the wavelet coefficient absolute entropy of all the phase currents is calculated, and the largest value phase, the smallest value phase and the medium value phase of the three values are determined through comparison. According to the designed algorithm, the type and the phase of a fault which happens to a circuit where input current signals are located are judged. The algorithm relates to three parameters, and the parameters are set through a training set; model accuracy verification is performed through a test set, and meanwhile the parameters are corrected. With the method, whether a fault happens to the circuit and the type of the fault can be fast judged, and therefore good assistance effect is played for on-site dispatch personnel to fast and accurately position a fault area after the fault happens, and the security and the stability of the electric transmission line can be improved easily.

The invention discloses a method and system for screening potential traffic safety hazard road sections. The method for screening potential traffic safety hazard road sections comprises the steps of obtaining a map data base containing geographic data of roads and a road traffic accident data base containing traffic accident data; reading the geographic data of the roads from the map data base and the traffic accident data from the road traffic accident data base; utilizing the geographic data of the roads to generate a road route network and displaying the generate road route network in an interactive interface; utilizing the traffic accident data to calibrate accident points in the road route network; confirming accident blackspots according to the accident points calibrated in the road route network; confirming the potential safety hazard type of the accident blackspots to obtain the potential traffic safety hazard road sections. The method and system for screening the potential traffic safety hazard road sections is a set of effective and objective scheme for managing road traffic safety and operation and improves accuracy and troubleshooting efficiency of dangerous road section troubleshooting results.

A fault diagnosis method of a vehicle lithium battery based on a multi-class support vector machine algorithm belongs to the field of vehicle lithium battery fault diagnosis. In order to solve the problem that the existing vehicle lithium battery fault diagnosis technology requires high amount of training data, which leads to the difficulty of realizing real-time on-line fault detection, the method of the invention comprises the following steps of: grouping battery samples into experiments, and sorting the collected data to form a training set and a test set; specifying battery failure criteria; using cross-validation and network search methods to optimize the parameters; constructing a kernel function support vector machine; constructing as biased binary tree five-class support vector machine to get the fault diagnosis model of vehicle lithium battery which can identify different states of lithium battery. The method can quickly and accurately complete the fault diagnosis of a lithiumbattery for a vehicle.

The invention discloses a new method of gas path fault diagnosis of aeroengine based on hybrid particle swarm optimization twin support vector machine (SVM) algorithm. In view of the fact that the gas path fault is the most frequent in the whole aeroengine failure and the strong demand for the intelligent diagnosis method in the field, and TWSVM has the advantages of faster theoretical calculation and better response to the sample imbalance problem, and adopts the TWSVM algorithm to study aeroengine gas path fault diagnosis. In this paper, the hybrid kernel function is introduced to improve the performance of the kernel function, so that the TWSVM algorithm can better balance the generalization ability and good learning ability. In addition, the optimal parameters of TWSVM are optimized by using HPSO, and the fault classification model is obtained. The aeroengine gas path fault diagnosis is realized with high precision.

The invention provides a LED automatic control system which includes an upper computer, the upper computer belongs to a computer hardware equipment which is installed with an intelligent system control software in inner. The upper computer communicates with a control host by way of electrical power carrier, the control host is installed in a distribution cabinet, the control host connects with a series communication interface of the electrical power carrier router, the electrical power carrier router is jointed to the electrical power carrier channel of each branch computer through the electrical power line, each electrical power carrier channel connects with the control module with electrical power source through the series communication interface, the electrical power source control module connects with the LED router electrical power source. The control system can process category setting according with grade and characteristic of the road for controlling the road lamp opening or closing automatically. The system also can adjust the lamp brightness and working power, and monitor using condition of the road lamp and the line in time, the system also can jump the control system and control the road lamp switch by hand.

The invention belongs to the field of fault diagnosis methods of oil-immersed power equipment and discloses a fault diagnosis method of oil-immersed power equipment by combining a fuzzy theory and improving a genetic algorithm. Aiming to a common improved IEC three-ratio method, by combining the fuzzy theory and improving the genetic algorithm, the method realizes treatment on other gas ratio boundaries and fault codes through fuzzy treatment and obtains a fault diagnosis result through a fuzzy inference. For carrying out fuzzy treatment on the boundary values of the improved IEC three-ratio method by adopting the fuzzy theory, the invention solves the problem of poor fault diagnosis accuracy caused by too absolute code boundary condition of the improved IEC three-ratio method and takes a certain action of improving the diagnosis accuracy of the improved IEC three-ratio method. The invention adopts the genetic algorithm for self-adaptive regulating mutation probability to revise an experience fuzzy membership function to obtain an optimal parameter of the fuzzy membership function and lays a good foundation of application of the fuzzy theory in improving the IEC three-ratio method.

The invention provides a security fault diagnosis system and method based on Internet of Things, an intelligent transmission case is arranged between a front-end intelligent device and a back-end processing center, a first fault diagnosis is performed on the front-end intelligent device by the intelligent transmission case, a power source of the front-end intelligent device can be cut immediately when a major fault occurs, the back-end processing center performs a further fault diagnosis when the major fault does not occur, so that the speed of fault diagnosis is improved, and the burden of the back-end processing center is reduced; besides, the system and the method can monitor and perform fault diagnosis on the working conditions of the intelligent transmission case and the front-end intelligent device, thereby more reliably ensuring normal operation of the front-end intelligent device. According to the invention, the front-end intelligent device with fault can be diagnosed in real time without the user performing an on-site inspection tour, a power supply can be cut remotely and automatically, and the user can be informed that the fault can be pointedly maintained in first time, so that the safety and reliability of the security monitoring system are ensured.

The invention provides a tolerance analog circuit fault diagnosing method based on wavelet transform and fractal dimension, which comprises the following steps of: aiming at the problem of analog circuit error diagnosis brought by element tolerance, circuit noise and measurement error, carrying out noise elimination processing on a response signal of a circuit by using wavelet transform; aiming at the problem that a transverse shaft and a longitudinal shaft of a sampling signal of an analog circuit are two physical quantities, improving the accuracy of fault diagnosis by using a method for respectively uniformizing the transverse shaft and the longitudinal shaft; aiming at the problem of error diagnosis caused by characteristic overlapping which is probably generated by directly extracting a wavelet decomposed coefficient as the fault characteristic, calculating a box dimension as the fault characteristic by using a wavelet decomposed signal; and inputting the characteristics into a BP (Back Propagation) network for carrying out fault positioning, wherein the adjustment of BP network weight is realized in a batch processing way. By adopting the invention, the speed and accuracy of fault diagnosis can be improved.

The invention discloses an Internet of things LVD (Low Voltage Detector) road lamp urban illumination control system which comprises an urban illumination management platform, a GPRS (General Packet Radio Service) communication module, an electric power acquisition module, a plurality of electric communication intelligent service terminals, a plurality of illumination units, a plurality of video conversion servers and a plurality of video camera units, wherein the GPRS communication module is communicated with the urban illumination management communication platform through a GPRS network. The plurality of video conversion servers are respectively communicated with the urban illumination management platform through a 3G network. According to the invention, communication is carried out by using electric power carriers, every day subset and sunrise time can be measured by using a GPS system, a road lamp switch is automatically controlled according to changes of the sunset and sunrise time, the brightness of the road lamp can be regulated according to the sunset and sunrise time, the operation state of the road lamp is monitored in real time, and mutual switching of automatic control and manual control can be realized.

The invention belongs to the testability technology and relates to a built-in testing design method based on a relevance model. The method uses a product relevance model as a basis and comprises the following steps of: at first, simplifying the relevance model, and removing a redundancy test and combining an ambiguity group; secondly, identifying a minimum test vector matrix corresponding to each fault; thirdly, determining an optimal testing vector according to a reliability index and a test cost of a test as a criterion for fault detection and separation; and finally, converting the criterion into an embedded diagnostic program and arranging the embedded diagnostic program in a built-in test of a product. With the adoption of the method provided by the invention, a fault source can be accurately found out through the optimal test cost and the least test quantity, so that the testability and the design level of an electronic product can be improved.

The invention provides a wind generating set fault diagnosis system and method, and relates to the field of wind power generation. The wind generating set fault diagnosis system comprises a database subsystem and a self-diagnosis subsystem, wherein the database subsystem comprises a preset fault diagnosis model library and a preset fault solution database; the self-diagnosis subsystem is used forobtaining a fault file of a wind generating set, searching a target fault diagnosis model and outputting a diagnosis result according to the wind power variable parameter value in the fault file, thetarget fault diagnosis model and the fault diagnosis model corresponding to the fault identifier, wherein the fault file comprises a master control version information, a fault identifier and a wind power variable parameter value, the target fault diagnosis model is a fault diagnosis model corresponding to the master version information and the fault identifier in the fault file in the preset fault diagnosis model library, and the diagnosis result comprises a fault solution. By utilizing the system and method, the efficiency of fault diagnosis of the wind generating set can be improved.

The invention discloses a fault diagnosis system for electric power measurement automatic verification assembly lines. Production control strategies of assembly lines are independent from one another at present, the production efficiency is severely affected, and potential safety hazards are caused for automatic production equipment. The fault diagnosis system comprises a data acquisition and processing module, a diagnostic analysis module, a system configuration module and a function display module, wherein the diagnostic analysis module implements offline analysis service and online diagnosis service, the offline analysis service includes establishment of a system SDG model, layering of an HSDG model, classification of target variables, establishment of a determined variable PS model and establishment of a variable DKPLS SVR model to be evaluated, and fault factors are extracted and serve as nodes of the HSDG model through manual abstraction of a physical structure of hardware equipment and by combining with historical production data information. The fault diagnosis system can reduce the searching space of effective nodes, thereby accelerating the fault diagnosis speed.

The invention provides a service-oriented network performance managing view organization method. The service-oriented network performance managing view organization method includes the following steps that firstly, network flow data are obtained on a key path and a data source of index analysis on a network service operation state through a decoding analysis manner; secondly, basic network service index data are stored and data are summarized according to the analysis requirement of the network service; thirdly, network service performance indexes are defined; fourthly, according to a network service accessing relation, a key index monitoring view is organized according to a hierarchical structure and an accessing relation provided by the network service; fifthly, the network service index data are presented in real time and historical data inquiry ports are provided. According to the service-oriented network performance managing view organization method, a traditional network performance managing mode is broken through and a manager can set up a service-oriented network performance managing view, is assisted in setting up a service-centered network performance managing train of thought and integrates the service-centered network performance managing train of thought into an operation and maintenance guarantee process.

The invention discloses a six-phase permanent magnet fault-tolerant motor system power tube open-circuit fault diagnosis method. The method comprises steps of conducting park vector transformation onthe six-phase current to obtain current vectors in two orthogonal subspaces; and calculating a current vector average value in a period, realizing open-circuit fault detection of the power tube by judging whether the current vector module value average value exceeds a fault threshold or not, and realizing positioning of the fault power tube according to positive and negative polarities of the current vector real part average value and the imaginary part average value. According to the method, additional hardware equipment does not need to be added, the utilization of the zero-sequence orthogonal subspace current vector ensures strong robustness of the fault diagnosis method to the rotating speed and the load sudden change. The fault location only needs to effectively simplify the operationaccording to the positive and negative polarities of the location variable, the method can simultaneously realize the power tube open-circuit fault diagnosis of the permanent magnet fault-tolerant motor system in normal and open-circuit / short-circuit fault-tolerant operation, and the fault diagnosis capability of the permanent magnet fault-tolerant motor system in the case of primary and secondary open-circuit faults is significantly improved.

The invention belongs to a real-time on-line self-diagnosis method and system for a video monitoring equipment failure. A real-time on-line self-diagnosis system is formed by a video signal sensor, a video signal control relay, a video failure signal generator, an intelligent one-out-of-eight selection output instrument and a failure displayer. The video signal sensor additionally installed on a video signal source is adopted to induce and detect the state of a video signal in real time, and automatic induction detection, failure precise warning, real-time displaying of failure equipment related information and other functions of the video monitoring equipment failure are achieved. The method comprises the following steps that (1) the video signal sensor induces the video signal; (2) the video signal control relay carries out gating control; (3) the video failure signal generator is closed and powered on; (4) the intelligent one-out-of-eight selection output instrument selects failure information needing to be output; (5) the video failure information is output and displayed. According to the real-time on-line self-diagnosis method and system, an existing failure maintenance mode is innovated to an on-line real-time self-diagnosis mode, lots of manpower, material resources and maintenance cost are saved, and far-reaching influences on social management innovation are achieved.

The invention discloses a failure processing method for a rail-train wired electronic-control air braking system. The failure processing method includes the steps that failure information is classified according to the types of the failure information, and corresponding failure guiding operation is set for the failure information of all types; the failure information is obtained, the class of the failure information is judged, and the corresponding failure guiding operation is performed according to the judgment result. Failure of the system is classified, the corresponding failure guiding operation is set for the failure of all types, and after the failure information is obtained, the class of the failure information is judged; then the failure guiding operation corresponding to the class is performed; the types of the failure can be determined in time, the failure processing speed can be increased, normal running of a train is guaranteed, and safety is improved.

The invention discloses a mobile intelligent direct current power supply device, comprising a mobile control charging cabinet, a mobile pressure regulating feed cabinet and a mobile battery combined tank, wherein a first group of connecting interfaces on the mobile control charging cabinet are connected with a second group of connecting interfaces on the mobile pressure regulating feed cabinet byvirtue of cables; and the first group of connecting groups on the mobile control charging cabinet on the mobile control charging cabinet are also connected with the mobile battery combined tank by virtue of the cables. By applying the mobile intelligent direct current power supply device disclosed by the invention, electricity utilization reliability is improved, economic loss caused by fault andpower failure time is reduced, production cost is saved, working efficiency is improved, and safety management level is enhanced.

The invention discloses a fault diagnosis system and a fault diagnosis method based on a massive data technology. The fault diagnosis system comprises a data acquiring module, a data pre-processing and exchanging module, a data analysis and diagnosis module and an intelligent control module, wherein the data pre-processing and exchanging module is connected with the data acquiring module; the data analysis and diagnosis module is connected with the data pre-processing and exchanging module in a bidirectional manner; and the intelligent control module is connected with the data pre-processing and exchanging module. Acquired state data are pre-processed through the data pre-processing and exchanging module, noise interferences are eliminated, and acquired state data can be conveniently analyzed well; and the state data are analyzed through the data analysis and diagnosis module by using the massive data technology, all the state data are analyzed to obtain specific and comprehensive diagnosis information, the fault diagnosis speed is increased, and the fault detection accuracy is improved. By the fault diagnosis system and the fault diagnosis method, faults of a wind driven generator can be detected and controlled, and the labor cost is greatly reduced.

The invention relates to a power grid anomaly detection method and device, computer equipment and a storage medium. The method comprises the steps of obtaining the sample set of the state informationof a power grid in each time period, and training the power grid anomaly detection model according to the sample set to obtain an anomaly score corresponding to each sample, calculating the actual deviation between the reference score and the anomaly score, optimizing the power grid anomaly detection model according to the actual deviation until the actual deviation reaches the expectation, and completing training to obtain a trained power grid anomaly detection model. By adopting the method, the diagnosis speed of the power grid fault can be improved, and the fault source can be accurately recognized.

The invention discloses a track circuit red-light strip default diagnosis method based on the FTA and a multilevel fuzzy-neural sub-network. The establishment of a default diagnosis module mainly comprises flowing steps of (1) FTA qualitative analysis for determining a default tree module according to default investigation and analysis and using the descending method to search the minimum cutset; (2) FTA quantitative analysis for solving probability of occurrence of an intermediate event and a top event according to probability of occurrence of a bottom event obtained according to the investigation and analysis and applying the Fussell method to analyze the importance degree of the minimum cuset; (3) extracting default diagnosis rules by analyzing knowledge contained in the default tree; (4) designing a diagnosis system structure and constructing a multilevel fuzzy-neural sub-network module according to the FTA analysis results; (5) setting fuzzy subordinating degree function parameter according to a to-be-diagnosed object, and adjusting network weights via sample learning and training so as to determine a default diagnosis module; and (6) inputting the measured data into the default diagnosis module in the actual running so as to obtain default diagnosis results via calculation. The track circuit red-light strip default diagnosis method can be used for automatically positioning a default point rapidly during a ZPW-2000 non-insulated frequency shift track circuit red-light strip default.

The invention relates to a remote control method of an industrial robot, and is characterized by comprising the following steps: all KUKA robots on a production line and a criterion for a device, which conforms to a TCP / IP (Transmission Control Protocol / Internet Protocol) are connected through network cables, an original registry of the robots is modified, and configurations of the KUKA robots are changed; a computer 1 is provided with a DameWare remote control software server, and each KUKA robot is provided with a DameWare client software, through a remote training function of the robot, the computer 1 connected with the robot and a computer 2 connected to a projector in a training room are connected to an Internet network, and the KUKA robots on the production line are subjected to remote diagnosis through Internet. According to the invention, the fault handling time is saved, and a remote control function is realized; meanwhile, dependency of the robot training on a field device is solved, a field interface of the robot is shown through the network and is connected to the projector so as to carry out large-scale targeted training and provide convenience for optimization and adjustment of a usual welding process.

The invention belongs to the field of power system relay protection and automation, and specifically relates to an adaptive reclosing method for a DC power grid. The method comprises the steps: guaranteeing the full de-ionization of a fault point after a considerable delay time period after the tripping of a fault pole circuit breaker; comparing a fault pole end voltage detection value with a single-phase grounding fault reclosing DC voltage threshold value for a single-pole grounding fault, determining that the fault disappears if the absolute value of the fault pole end voltage is greater than the single-phase grounding fault reclosing DC voltage threshold value, and carrying out the reclosing operation of a fault pole line; carrying out the sequential reclosing of circuit breakers at two ends of one pole of the fault line with a bipolar short circuit fault, i.e., firstly recovering the single-pole operation; detecting the voltage of a non-reclosing pole of a fault line after reclosing: determining that there is no physical connection point between positive and negative poles, the fault is instantaneous and is completely removed, the circuit breakers at two ends of the negative pole can be reclosed and the normal operation of the line is recovered if the voltage of the non-reclosing pole is less than a bipolar short circuit fault reclosing DC voltage threshold value.

The invention discloses an intelligent campus network operation and maintenance system. The intelligent campus network operation and maintenance system is mainly based on application layer monitoring. The intelligent campus network operation and maintenance system comprises a management module, a monitoring module, an operation and maintenance knowledge base module, a fault reporting module, a user terminal and an evaluation module; wherein the monitoring module, the operation and maintenance knowledge base module, the fault reporting module and the evaluation module are all connected with the management module; and the user terminal is in communication connection with the management module. The invention provides an intelligent campus network operation and maintenance system. By means of the intelligent campus network operation and maintenance system, the elements such as the operation and maintenance personnel, the network devices and the fault repair in the operation and maintenance process of the network are managed and deployed comprehensively combined with the corresponding monitoring function, therefore, the network faults can be troubleshot as fast as possible, the repair time of the fault can be shortened, the availability of the campus network can be furthest guaranteed.

The invention discloses a wind power generator fault diagnosis system comprising the following parts: a plurality of sensors used for acquiring a plurality of characteristic quantities of the wind power generator; a first processing unit used for receiving the plurality of characteristic quantities sent by the sensors, using the plurality of characteristic quantities as a fault classification condition attribute set, using a rough set theory to fuse the information, and building a fault diagnosis decision table; extracting fault diagnosis rules according to the fault diagnosis decision table;using the fault diagnosis rules to diagnose the wind power generator faults. The invention also discloses a wind power generator fault diagnosis method; the wind power generator fault diagnosis systemcan diagnose a plurality of fault factors, can extract the plurality of wind power generator characteristic quantities for analysis and diagnosis, thus spotting each fault factor of the wind power generator faults, solving relations between the fault factors, and finally obtaining the fault diagnosis result with a high accuracy.

The invention relates to an internet-of-things-based electrodeless-lamp urban lighting control system. A 3G communication module is in communication connection with an urban lighting management platform by a 3G network. A plurality of power communication intelligent service terminals are connected with the 3G communication module by RJ45 interfaces respectively and are connected with an electric power collection module by RS485 interfaces. A plurality of illumination units are connected with the power communication intelligent service terminals by power lines. A plurality of video conversion servers are in communication connection with the urban lighting management platform by the 3G network; and a plurality of video shooting units are connected with the video conversion servers by video transmission lines. According to the invention, with power carrier communication, the sunrise and sunset time each day can be measured and calculated by using a GPS positioning system; streetlight switches can be controlled automatically according to changes of the sunrise and sunset time and the streetlight brightness can be adjusted based on time; and the operation states of the streetlights can be monitored in real time. Moreover, mutual switching of an automatic control way and a manual operation way can be realized.

The invention discloses an on-line state monitoring control method and a monitoring system for a hydropower station sluice gate. The method comprises the following steps: a usage and operating state of the sluice gate is monitored in real time through a sluice gate control cabinet, a sluice gate motor frequency converter and a sensor arranged on the sluice gate; monitored data is transmitted to adata acquisition instrument via the sensor; the controller is used for reading and recording data signals from the data acquisition instrument, the sluice gate control cabinet and the sluice gate motor frequency converter respectively, performing spectrum analysis on the data signals, extracting signal characteristic values of the data signals of all test points from spectrum analysis results, establishing an early warning mathematical model, achieving real-time monitoring and control of the sluice gate, and giving early warnings upon occurrence of exceptions of the sluice gate. The on-line state monitoring control method and the monitoring system are characterized by ability to reduce the maintenance cost and improve troubleshooting speed.