360 results about "Equipment Defects" patented technology

The invention discloses an electric transmission and transformation equipment state monitoring defect management system and method based on regulation and control integration. The method is characterized in that an electric power regulation and control center is used to monitor the operation of newly added power transmission equipment in a centralized manner, monitor the electric transmission and transformation equipment state online and build an analyzing service so as to perform statistical analysis, processing and monitoring, and closed-loop tracking on equipment defect conditions, summarize defect variation trends and occurrence regularity, and promote comprehensive extension, from scheduling operation to equipment operation, of power grid safety control. Resources of an operation on-duty system, a production management system and other equipment management systems are integrated to build a complete and good working interface, electric transmission and transformation equipment monitoring defect multidimensional information is input, and by the aid of management modules of traverse connection, inner control monitoring and closed-loop tracking, the real-time operation states of the electric transmission and transformation equipment can be controlled, inquiring and statistical counting of various key information can be achieved, screening and tracking of family defects are achieved, and equipment health states can be monitored and analyzed.

The present invention relates to methods for inspection of defects in an electrophoretic display and related devices. The method may be carried out with one or more testing electrodes. The method comprises applying a voltage difference to two testing electrodes which are in contact with the display panel, or applying a voltage difference to a testing electrode and a electrode layer. The methods may be applied in in-line or off-line inspection of a display panel.

The invention provides a defect rate detecting method of an oil immersed type main transformer. The defect rate detecting method includes obtaining determinant attributes of the oil immersed type main transformer serving as a sample through detection, establishing a regression model of the determinant attributes and a defect rate of a device, and accurately predicting the defect rate of an oil immersed type main transformer to be detected by means of the regression model and the determinant attributes of the oil immersed type main transformer to be detected. The defect rate reflects the whole defect level of the oil immersed type main transformer of power grids, and various defects in power equipment can be early found and timely eliminated, so that the phenomenon that large-scale loss of power failure of the power grids is caused by the fact that the defects are developed into faults is avoided. According to defect rate differences among different kinds of the devices, maintainers can purposefully perform focused inspection and maintenance to prevent the defects from developing into device faults; production departments can formulate production personnel plans and spare part requirements and predict a future change trend of the defect rate of the devices in combination of development planning of the power grids to guide producing activities of the power grids to well develop.

The invention discloses an intelligent distribution patrol and management system and a work flow thereof. The system comprises a local area network composed of a GIS work station and a GIS server and an RFID mobile patrol terminal, wherein a wireless network transmission device is used for transmission between the local area network and the RFID mobile patrol terminal. By using the non-contact automatic identification technology of the RFID system, each distribution equipment is printed with a unique electronic marker, and a unique identification marker of each equipment is established in a distribution GIS system database. Patrol staff can accurately track the running condition of each equipment and import data into the distribution GIS system database by using the RFID mobile patrol terminal. Thus, the defects of inappropriate patrol, unscheduled patrol and nonstandard recording on the distribution equipment can be fundamentally solved, and simultaneously, the complicated and repeated work of recording equipment defects in the charge of group members can be avoided, thereby relieving the patrol workload of the group and improving the patrol efficiency and accuracy.

The present invention provides a device management system. The system comprises a device file management module, a maintenance planning management module, a fault management module, an inspection maintenance work order management module, an expense budget module, a statistic analysis management module, a prompting module and a system management module. The device file management module is configured to store and manage the basic static information, the whole life cycle document data, the device faults, the maintenance history and the device changing information of a device and provide functions of addition, deletion, modification, query and statistics analysis; and the maintenance planning management module is configured to perform establishment, examination and approval, issuing and execution, statistics and analysis query of a preset plan project; and the fault management module is configured to report fault records and start a fault confirmation process after device defects or faults are detected, eliminate faults according to the corresponding preset fault processing mode after confirmation of the faults and record the faults in the fault knowledge base. The efficiency and the precision of management are improved, and a lot of manpower is saved.

The invention discloses a mobile intelligent patrol inspection management system for new energy power generation equipment and a patrol inspection method. The system includes a mobile intelligent device client and a cloud patrol inspection platform server; the mobile intelligent device client and cloud patrol inspection platform server conduct communication through wireless network; the mobile intelligent device client downloads patrol inspection task from the cloud patrol inspection platform server, and upload the inspection results and the position information of the inspection personnel to the cloud patrol inspection platform server in real time; and the cloud patrol inspection platform server receives the data of the mobile intelligent device client, so as to realize GIS visualization of the patrol inspection equipment, dynamic display of patrol inspection track and defect analysis of patrol inspection equipment. The invention has the beneficial effects that through the function of tracking the mobile patrol inspection path, the quality of new energy equipment inspection is ensured; through depth data mining of the equipment defect information, the defect treatment is more targeted, so as to assist future equipment selection.

The invention provides a current and short-term reliability assessment method of a high-voltage transformer. According to the method, precise and quantized assessment is carried out on the current and short-term reliability of the high-voltage transformer by means of a fault tree and a fault mode assessment model through data obtained through all detection means of a current transformer, wherein the data include routine tour, special tour and preventive test data, diagnosis test data, foundation standing book information of equipment, past-year equipment defect and fault records and other data. After assessment, a mode that each maintainable component of the transformer can possibly have a fault and the fault generation probability can be obtained, and maintenance measures to reduce or prevent the fault mode can be formulated under guidance according to the correspondent relationship between the fault mode and inspection methods. A development trend of the short-term reliability can be predicted through an aging model of the equipment. The method can assess reliability of the high-voltage transformer, and has the advantages of being concise, easy to understand, precise, visual, quantized and the like.

The invention discloses a joint inspection system for transformation equipment based on an intelligent robot and an application method thereof. The system comprises an inspection robot, a joint inspection device, a charging device and a centralized control platform, wherein the inspection robot is used for inspecting a transformer substation, collecting monitoring data of the joint inspection device and uploading the collected data to the centralized control platform in a wireless mode; the joint inspection device is used for being matched with the inspection robot to carry out joint inspection on the equipment state, the operating environment and the like, and comprises an intelligent sensor, a video monitoring sensor and an online monitoring device; the charging device is used for charging the inspection robot; and the centralized control platform is used for monitoring the operation of the inspection robot, analyzing, managing and displaying the detected data and carrying out management and control on the inspection mode. According to the invention, the existing monitoring resources are fully utilized, the comprehensiveness and diversity of data are improved, the inspection taskis dynamically adjusted, the utilization rate of the robot is improved, a data basis is provided for the comprehensive analysis of the equipment state, and the defect detection efficiency of the equipment is improved.

The invention discloses a video monitoring linkage system based on a substation patrol robot and a method thereof. The system comprises a linkage analysis control host computer and a communication server, wherein the linkage analysis control host computer is used for receiving video data collected by a video monitoring device on a patrol robot, analyzing and processing the data and controlling a video device on the robot or the video device of an auxiliary fixed point monitoring subsystem to act on the basis of the related instruction according to the analyzed and processed data; the communication server is used for sending an abnormal condition analyzed by the linkage analysis control host computer to an external system for carrying out linkage; and the communication server is also responsible for receiving a linkage request of the external system and forwarding the request to the linkage analysis control host computer for correspondingly linking. According to the invention, the detection efficiency for equipment defects is increased; the linkage system is perfected through the moveable patrol robot and the intelligent analysis host computer; workers need not participate into the processes of defecting and confirming the equipment defects and intelligently linking with online equipment; and the autonomation and automation of video monitoring linkage can be realized.

The invention discloses an infrared database intelligent diagnosis management system for charged equipment. The system comprises a transformer station operation platform, an intelligent data diagnosis processing center, an infrared dedicated engineer operation platform and a high-level inquiry monitoring platform. A supervisor and an infrared dedicated engineer can log on a remote WEB browser endto view infrared detection reports uploaded by a transformer station, know an equipment operation situation, view historical data, defect reports and equipment historical temperature trend curves, perform secondary diagnosis on equipment defects by using an intelligent diagnosis and auxiliary decision system, query by taking equipment of a certain factory as a condition to obtain all infrared charts of defective equipment, and provide reference for later purchase by comparing the quality of the equipment of different factories laterally. Transformer station staff can handle defects in time, upload the infrared detection reports and generated historical temperature trench curves, receive a superior indication, and perform emergency processing in time.

The invention discloses a switch equipment defect intelligent detection and analysis system including an intelligent detection terminal, an IoT communication system and a background monitoring system. The intelligent detection terminal includes a sensor unit and a data processing unit. The IoT communication system adopts a ZigBee wireless network for communication. Different signals detected by the sensor unit are processed by the data processing unit and are then transmitted to the background monitoring system through the ZigBee wireless network. According to the invention, engagement and disengagement current parameters, mechanical property parameters and contact temperature data of the switch equipment are collected in real time and evaluation and failure diagnosis of the integral state of the switch equipment are performed through the intelligent detection terminal and the background monitoring system.

The invention relates to a device defect management module of an EAM system in a power plant, comprising: a defect recording submodule for recording the defect information of a device; a defect processing submodule for identifying and analyzing the device defect information, generating a first work task list and transmitting to relevant personnel; a timing submodule for measuring the working hour consumed from the stage when a defect occurs to the elimination process, wherein the device defect management module can perform overall process management on the device defect. The invention can timely preserve the device defect condition and the defect eliminating condition according to the specialty, system and unit number so that management personnel and repair personnel can timely search the defect that fails to be eliminated so as to timely design the defect eliminating step, ensuring the reliability of the device and improving the use ratio of the device.

The invention discloses a data association relationship-based equipment defect assessment and prediction method. The method mainly comprises the following steps: integrating historical monitoring data of power grid equipment, and extracting a certain equipment defect type of the power grid equipment and data factors corresponding to the equipment defect type; integrating the data factors corresponding to the equipment defect type so as to obtain a factor monitoring database, wherein the factor monitoring database comprises monitoring data of the equipment defect type; obtaining association coefficients of elements in a set of the data factors corresponding to the equipment defect type; constructing an assessment and prediction model of the equipment defect type; and combining the assessment and prediction model and the factor monitoring database, calculating all assessment and prediction model y values corresponding to the defect type, and carrying out equipment defect assessment and prediction according to the assessment and prediction model of the equipment defect type and an interval distribution probability corresponding to all the equipment defect types. According to the method, the defect analysis flexibility is enhanced.

The invention discloses an improved Mask R-CNN image instance segmentation method for identifying defects of power equipment. The method comprises the steps of constructing a convolutional neural network; reading the power equipment defect pre-processed picture, inputting the power equipment defect pre-processed picture into a convolutional neural network, and performing feature extraction on thepower equipment defect pre-processed picture by the convolutional neural network to obtain a feature-containing region; refining the region containing the characteristics through an RPN network; and in the refined region, carrying out RoIAgign operation through a bilinear difference method to process the refined region, generating a feature map of a fixed size for each region of interest, and obtaining power equipment defect instance segmentation through the category, the coordinate information and the mask information. By improving the Mask R-CNN image instance segmentation method, the basiceffect of Mask R-CNN segmentation is reserved, the bilinear interpolation speed in the RoIAgign process is increased, meanwhile, the mapping process fully and uniformly utilizes power equipment defects to preprocess all pixels of an image, and the segmentation effect is more obvious.

The invention belongs to the technical field of operation and maintenance application of power lines and specifically discloses an equipment parameter inspection management system based on two-dimensional code recognition. The system comprises RFID tags, a plurality of inspection item modules, a post inspection planning module, a computer, a daily inspection task module, an inspection circuit planning module, an inspection record module, a handheld terminal module and a data reporting module arranged on to-be-inspected equipment. The system disclosed by the invention has the beneficial effectsthat 1) inspection required according to the plan is really and objectively ensured on time; 2) the field state and related data can be conveniently and really recorded and uploaded in real time or in non-real time; 3) the inspection data can be subjected to statistical analysis, so that the inspection result is utilized; 4) the archiving management of the equipment is performed; and 5) the datadoes not need to be recorded for the second time, office automatization can be realized, the in-place number of each shift, the missing inspection number and missing inspection rate are counted, and the operating state and data report of each equipment can be formed, so that the equipment defects are conveniently analyzed by utilizing the data.

The invention discloses an infrared image-based intelligent substation equipment defect diagnosis method and system, and the method comprises: employing a YOLO deep learning model and a data analysismethod to achieve the automatic recognition and intelligent defect diagnosis of a substation equipment part in the infrared image through the steps of substation equipment part positioning, temperature feature extraction and temperature threshold judgment. On one hand, depending on the target detection performance of the YOLO deep learning network, the accuracy of substation equipment part positioning is ensured; on the basis of accurate positioning of power transformation equipment components, characteristic parameters are extracted, and the infrared image data is converted into a temperaturecharacteristic value capable of being quantitatively analyzed; on the other hand, depending on detection data and through the extracted temperature characteristic values, the distribution characteristics reflecting massive temperature difference data are obtained and analyzed, so that the defect threshold judgment method with higher reliability is obtained, the problems of a diagnosis method of pure image analysis or manual threshold setting are solved, and the reliability and the accuracy of infrared image defect diagnosis of power transformation equipment are improved.

The invention provides a switch cabinet big data state evaluation system based on intelligent partial discharge live-line detection. The system comprises an intelligent inspection unit, a partial discharge diagnosis and positioning unit, an equipment state evaluation unit, an intelligent terminal and a control unit, wherein the equipment state evaluation unit comprises a communication unit, a signal storage unit, a characteristic signal extraction unit, a defect type identification unit and an equipment defect severity analysis unit. The system is advantaged in that the system has properties of high intelligentization level, good partial discharge detection sensitivity and high risk evaluation accuracy, and powerful technical support is provided for developing power-on maintenance of the switch cabinet.

The invention discloses a method and system for detecting defects of power transmission line equipment, and belongs to the technical field of power transmission line inspection. The method comprises:training by taking an equipment image as a training set to obtain a primary defect detection model; taking the generated defect recognition result data as a training set, and performing training to obtain a secondary defect detection model; and obtaining an inspection image of the target power transmission line as input data, inputting the inspection image into the primary defect detection model,obtaining an equipment image of the target power transmission line, taking the equipment image of the target power transmission line as secondary input data, inputting the secondary input data into the secondary defect detection model, and obtaining an equipment defect identification result of the target power transmission line. A two-stage detection and incremental detection model training mode is adopted, so that the defect recognition accuracy can be improved.

The invention discloses an XGBoost-based electric power secondary equipment defect degree evaluation method, which comprises the following steps of: firstly, acquiring and arranging related defect data of electric power system secondary equipment, and performing a series of preprocessing work such as duplicate removal, abnormal value filtration, missing value removal and the like on the acquired historical defect data of the electric power system secondary equipment; then carrying out association rule mining on the processed data based on an Apriori algorithm; screening out features having strong association rules with the defect degree of the secondary equipment of the power system to establish a feature index set, performing feature and label coding on index data, and after data grouping, performing training and parameter optimization on an XGBoost model by utilizing data of a training set and a test set respectively; and finally, achieving accurate classification of the defects of the secondary equipment of the power system by using the trained classification model. Maintenance personnel can be well assisted in maintenance and management of the equipment.

The invention relates to a method and a system for managing equipment defects by fault codes. Through the known equipment defects, faults are classified according to fault phenomena and fault reasons, for each kind of types and reasons, different phenomena and reasons are represented by different codes, and a multilayer fault code system is formed. A fault code link is built according to the layer level of the multilayer fault code system, and a fault code link data model is obtained. Fault data after equipment fault processing in each time is input according to the fault code link data model, and fault records are generated. The occurrence probability of equipment faults is calculated according to the fault records. According to the fault types, the fault codes are subjected to classification processing, in addition, the fault code link data model is built, next, fault data after equipment fault processing in each time is input according to the fault code link data model, the fault records are generated and can be conveniently subjected to inductive statistics, the occurrence probability of equipment faults is calculated, and the error probability during the fault rate statistics is reduced.

The invention provides an information identification method for a defect text of power grid equipment. The method comprises the following steps: firstly, constructing a dependency syntax tree based ona main transformer defect text and a national power grid main transformer defect classification standard text by utilizing a dependency syntax analysis technology, and pruning, segmenting and reconstructing the dependency syntax tree by combining actual characteristics of the defect text; and then, aimed at the constructed dependency syntax tree, providing a tree matching algorithm based on a dependency relationship, finding out a standard expression corresponding to the actual defect, and identifying the actual defect information contained in the defect text; and finally, using a main transformer defect text to construct an example, and analyzing the accuracy of the defect information identification method. According to the method, the defect text is analyzed from the perspective of semantics, accurate defect information contained in the text is identified, and guidance is provided for fault rate modeling, health state evaluation and other problems of equipment.