119 results about "Fault prevention" patented technology

The invention provides a vehicle fault online detection and early warning device based on the internet of vehicles and a vehicle fault online detection and early warning method thereof. The device comprises a vehicle-mounted terminal, a remote server side, a vehicle owner mobile phone client side, a maintainer client side and a maintenance manufacturer client side. According to the detection method, the vehicle-mounted terminal acquires vehicle information, wherein the vehicle information includes the data of an OBD interface and the data acquired by a sensor so as to realize vehicle online real-time monitoring; the vehicle information is transmitted to the remote server side to perform big data intelligent analysis so that the existing problems of the vehicle are obtained and early warning is provided and faults can be avoided; besides, a vehicle maintenance scheme can be intelligently provided, and the data are fed back to the vehicle owner and the maintenance shop of the maintainer through the vehicle owner mobile phone client side, the maintainer client side and the maintenance manufacturer client side so that the vehicle owner can understand the situation of the vehicle in real time and take measures for fault prevention, the maintainer can view the data and the analysis result of the remote server side and thus maintenance is facilitated.

A method for determining and prolonging hardware life expectancy is provided. The method includes collecting data from a hardware component in a first computational device, creating a quantitative value representing the status of the hardware component, determining a lifetime of the hardware component, and providing an alert to the first computational device based on the determined lifetime of the hardware component. A system configured to perform the above method is also provided. A method for managing a plurality of hardware devices according to a hardware life expectancy includes accessing an application programming interface (API) to obtain status information of a hardware component in a computational device is also provided. The method includes balancing a load for a plurality of redundancy units in a redundancy system and determining a backup frequency for a plurality of backup units in a backup system.

The invention relates to a fault preventing and intelligent repairing method and device for a network management system. The device comprises a fault detection module, a fault prevention module and a fault repairing module; and the method comprises the following steps of: setting corresponding early warning thresholds, fault thresholds, early warning repairing policies and fault repairing policies for system parameters related to faults in the network management system in advance; after monitoring, if the system parameters related to faults are more than or equal to the corresponding early warning thresholds and less than the corresponding fault thresholds, generating early warning messages, finding the corresponding early warning repairing policies and repairing the early warning; through monitoring, if the system parameters related to the faults are more than or equal to the corresponding fault thresholds, generating fault messages and reporting fault warning; and according to the fault messages, finding the corresponding fault repairing policies and repairing the faults. By adopting the fault preventing and intelligent repairing method and device which are disclosed by the invention, fault occurrence probability of the network management system is reduced to the greatest extent, and fault repairing speed is improved.

The invention relates to a converter remote monitoring system used for wind power generation, comprising a plurality of wind power generating sets. Each wind power generating set comprises a converter and a converter remote monitoring console which is provided with built-in converter monitoring software; and a converter monitoring network is formed by networking the converters, wherein the converter remote monitoring console and the converter monitoring network are in communication link. The system communicates with the converter remote monitoring console at the far end by the independent converter monitoring network, and the maintenance personnel directly monitor and maintain the converters and correlative systems so as to sufficiently utilize the data resources acquired and recorded by the converter; the selection of converter monitoring parameters is not restricted by the communication mode, the capacity and the communication protocol of a main controller, but depends on the communication mode, the capacity and the communication protocol of the converter monitoring network, thereby greatly improving the maintenance efficiency and quality of the wind power generating sets, shortening fault treatment time, realizing parametric analysis and fault prevention in daily operation, and obviously saving the maintenance cost.

A failure prevention diagnosis support system includes: an acquiring portion that acquires internal information about an internal state of an image forming apparatus; a storage portion that stores one or a plurality of logistic regression models that define an estimate value of a regression coefficient through a logistic regression analysis using the internal information obtained when the image forming apparatus is in a failed state and in a normal state; and a controller that performs a control operation to select a logistic regression model from the one or the plurality of the logistic regression models stored in the storage portion in accordance with the image forming apparatus, and to calculate risk degrees as objective variables that are indicators of failure degrees in the image forming apparatus by assigning the internal information acquired by the acquiring portion or the value obtained from the internal information to the selected logistic regression model.

The invention relates to a power distribution network rapid operation and maintenance and first-aid repair service realization method on an Android platform, and belongs to the technical field of power distribution network operation and maintenance and first-aid repair. The technical scheme is that server side software of an intelligent operation and maintenance service system is deployed at a fault positioning master station, an interface with a fault positioning system is realized via a WebApi technology, the line load condition and alarm information data in the fault positioning system are read, and a data management function and a data releasing function are realized; operation and maintenance and repair personnel can check the line condition and timely understand the line condition anytime and anywhere and discover line hidden troubles in advance and solve the line hidden troubles in advance so as to realize a fault prevention function; when the line fails to work, fault information and fault data can be timely known, fault points and fault causes can be judged in advance and repair tools can be prepared according to experience and the fault data, and the operation and maintenance and repair personnel instantly depart after a scheduling command comes and automatically select the most appropriate first-aid repair path; meanwhile, first-aid repair process information can be recorded so that post analysis is facilitated.

The invention discloses a power system vulnerability evaluation method based on multiple attack strategies. The method comprises the following steps: 1) constructing a power grid model; 2) evaluatingthe importance of nodes in the network from four aspects of node degree, betweenness, local centrality and node fault loss, and completing the formulation of an attack strategy according to the importance of the network nodes; 3) establishing a betweenness virtual flow model to simulate a cascade fault propagation process in a power system; and 4) establishing a vulnerability index based on the betweenness virtual flow model, simulating a network attack by adopting a plurality of attack strategies, and evaluating the vulnerability of the IEEE300 power system network according to the change ofthe vulnerability index. Aiming at the vulnerability of the power network under the condition of various attack strategies, the betweenness virtual flow model and the various attack strategies are established to analyze the performance change of the power grid under the cascade failure. The importance of nodes in a power network can be identified, and decision support is provided for maintenance work of power system managers and power system fault prevention of designers.

The invention provides a detecting mistake-proof system applicable to an electronic part production line and a method of the detecting mistake-proof system. The detecting mistake-proof system comprises a main control unit, a data center, a bar code unit, an assembling unit and a detecting unit. The assembling unit comprises an automatic welding machine. Under the overall control of the main control unit and through the matching of the data center, the bar code unit, the assembling unit and the detecting unit, detecting control and fault prevention of the assembly line are conducted. Problems which occur in an assembly line production process can be found out timely, through the monitoring method where various means suck as element scanning are combined, mistakes can be reported effectively, timely processing of the production mistakes is benefited, the number of broken machines is reduced, cost consumption is avoided, production personnel are reduced, scrapped products are reduced, cost for maintaining damaged products is reduced, manpower and task time consumed by reworking are reduced, normal production task time which is lost is reduced, at the same time, reducing of the whole production cost is benefited, efficiency of a production workshop is improved and product quality is improved.

The invention provides a WiFi-based IOT equipment monitoring system which comprises a controller, a wireless router, a mobile terminal, a cloud server, monitored equipment, and a service provider monitoring platform. The controller carries out data acquisition and control on the monitored equipment through a built-in data acquisition control module and storing the collected operation data and fault data into a data storage module. According to the network environment of a field, the controller allows the monitored equipment and the cloud server to carry out data interaction through a built-in WiFI module by using three methods. The invention also provides a data processing method of the WiFi-based IOT equipment monitoring system. Three steps are comprised. Finally wherever the monitored equipment is, the operation data can be reliably transmitted to the cloud server, a service manufacturer can carry out remote fault diagnosis, potential fault prevention and maintenance prompting of the equipment.

The Tail Load Monitoring System detects faulty low frequency (e.g. those in the range from 0.1 to 1 Hz) oscillatory conditions caused by Flight Control System malfunctions while the aircraft is in air by means of a continuous assessment of the estimated tail load behavior and data processing. Both estimation and data processing activities are provided by a dedicated architecture featuring a tail load estimation module, a band-pass filter and three independent paths that continuously monitor nuisance fault detection events avoidance, catastrophic events avoidance (addressing a limit load criterion), and structural damage avoidance (addressing fatigue life criteria).

The invention provides a call center outbound call test method, which belongs to the technical field of communication and automation. The method is applied to a server of a call center and is characterized by comprising the following steps: acquiring an information table of trunks, wherein the information table at least includes the unique identity and the last outbound call time of each trunk; determining a trunk meeting the condition that the difference between the last outbound call time and the current time exceeds a preset time as a trunk under test; sending an outbound call test instruction to the terminal of the trunk under test, receiving an outbound call test request sent by the terminal of the trunk under test in response to the outbound call test instruction and forwarding the outbound call test request to a test terminal; and receiving test feedback information obtained after test for the outbound call test request from the test terminal, and judging whether the trunk undertest is normal or not according to the test feedback information. Test and fault prevention of outbound call lines can be realized, and the stability of an outbound call center system can be improved.

The invention discloses a product early-fault root cause recognition method based on fuzzy data processing. The method comprises the following steps of 1, constructing a product early-fault root cause relevance tree layer model; 2, constructing a potential fault root cause data model; 3, collecting product service life period quality and reliability data; 4, on the basis of the fault relevance tree layer model, a process target node and data fuzziness analysis are determined, and then node influence factors and fuzzy values are determined; 5, constructing a product early-fault root cause fuzzy data envelopment analysis model; 6, estimating an efficiency evaluation value of a fault relevance tree node; 7, fault relevance node relative weights are divided, and node priorities are ranked; 8, results are analyzed, and fault root cause recognition is completed. Development of the early fault root cause recognition technology under the early fault mechanism recognition cognition fuzzy environment is broken through, prevention measures are adopted for product design, technological design stage and other early fault forming stages, afterward treatment is changed into beforehand prevention, and the new idea is provided for early fault prevention and rectification.

The invention discloses a symbolizing system and method for detecting abnormality of a bearing. The method comprises the steps of firstly, acquiring vibrating signals during the running time of the bearing at a certain time interval by utilizing an acceleration sensor, wherein vibrating data xn with the length as n can be acquired at each time; then performing probability density symbolization on each vibrating data; converting original vibrating signals into a symbolized sequence sn; determining a word length L of a coded sequence and coding the symbolized sequence sn; finally, calculating a standard difference of the coded sequence of each vibrating data and regarding the standard difference as a characteristic quantity for the abnormality detection of the running condition of the bearing. Compared with the traditional symbolizing method, the probability density symbolizing method adopted in the invention is more sensitive to the abnormal information of the bearing and can find faults earlier, so that great significance is achieved for the fault prevention, bearing maintenance and safe production.

The invention discloses an improved fault tree analysis method for environmental risk evaluation. The improved fault tree analysis method is characterized in that membership degree set method is introduced into the fault tree analysis; and a weighted average method is adopted to calculate the membership degree of the upper level event according to the membership degree of the lower level event without erasing the value or contribute of any factor. During environmental risk evaluation, the method can overcome 'this or that' disadvantages of the prior fault tree analysis method to achieve scientific environmental risk evaluation method and ensure scientific and reasonable results, thereby more effectively performing the functions of environmental risk prediction and fault prevention of the environmental risk evaluation.

The invention relates to a parameterization Thevenin equivalence-based alternating current direct current received end power grid commutation failure fault preventive method. A parameterization received end power grid Thevenin equivalence model is obtained by adopting a generalized Galerkin method according to an alternating current direct current received end power grid short circuit current calculation model by considering a direct current commutation failure and multi-convertor-station concurrence failure caused by the alternating current system faults; and by taking the weight sum for improving the single convertor station commutation failure immune factor and the multi-convertor-station commutation failure immune factor as the optimization target, and by taking the parameterization Thevenin equivalence model as the constraint condition, an optimization model is established, so that the alternating current direct current received end power grid commutation failure fault defensive capability is improved. The method has relatively high applicability and high calculation speed, and can adapt to the alternating current direct current received end power grids in different operationmodes, and can better satisfy the actual demands.

The invention discloses a method for identifying a vulnerable line and preventing a cascading fault of a power system. The method comprises the following steps of firstly, converting a complicated power grid to a bidirectional weighing dual graph; secondly, determining node constraint coefficients in the dual graph by active power coupling between power transmission lines and a dependence relation; thirdly, sequencing the vulnerable lines according to a sequence of constraint coefficients from small to large, and completing vulnerable line identification; and finally, improving a typical OPA model according to a vulnerable power transmission line identification result, and proposing a vulnerable power transmission line identification-based cascading fault prevention method. With the methodproposed by the invention, the vulnerable power transmission line which probably causes high risk can be effectively and accurately identified, and the power failure risk and the cascading fault power-failure probability of the system can be reduced.

The invention discloses a bar code printing tracing system. Due to the adoption of tracking checking software for acquiring, settling and fitting information of any production period of a product, not only is the product traced forwards, but also information of an unqualified product can be rapidly and accurately traced backwards, so that the after-sell service quality of a company can be greatly improved, and brand construction of the company can be promoted; in addition, the system has a fault prevention management function in production process management; the bar code printing tracing system is relatively complete in information acquisition, good in function and relatively good in practicability.

The invention discloses an identification method of a complex power grid self-organization critical state in the technical field of electric power system cascading failure prevention. The method includes: obtaining actual load loss caused by random failures occurring on a power grid; calculating cumulative probability of the random failures occurring on the power grid according to the actual load loss caused by the random failures occurring on the power grid and set load loss amount; performing regression analysis on the set load loss amount and the cumulative probability so as to determine whether the power grid is in a self-organization critical state; calculating a power grid load loss expectancy value and determining relative probability of load loss failures occurring on the power grid when the power grid is in the self-organization critical state. The identification method enables users to accurately judge whether the power grid is in the self-organization critical state at present, obtain probability of cascading failures occurring on the power grid, calculate load loss amount after the cascading failures occur and meanwhile estimate relative probability of large-scale power failures caused by the load loss.

The invention provides a power generator unit bluetooth data acquisition system comprising a bluetooth data acquisition module, a controller module, a mobile phone bluetooth module, a cloud server module and a monitoring terminal. The bluetooth data acquisition module is connected with the controller module, an excitation regulator and an electronic speed regulator. The bluetooth data acquisition module acquires and saves operation data of a power generator unit through the controller module and acquires and saves excitation voltage and speed regulating driving voltage. The bluetooth data acquisition module is connected with the mobile phone bluetooth module through a bluetooth transmission mode. The mobile phone bluetooth module is connected with the cloud server module through the internet. The cloud server module is connected with the monitoring terminal through the internet. The invention also provides a power generator unit bluetooth data acquisition method. A communication pipeline is established between the power generator set and the cloud server to capture data during the fault period and reproduce a fault site so that remote fault diagnosis, potential fault prevention and maintenance prompting are realized, and the objective of healthy monitoring with fault prevention acting as the main part is achieved.

An example system for processing a federated query includes a query proxy that receives a federated query including a plurality of source queries and receives an indication that a failed set of one or more source queries failed to execute successfully. Each source query is specific to an autonomous data source belonging to a federation. The system also includes a data federation engine that identifies a plurality of autonomous data sources to which to send the plurality of source queries. The plurality of autonomous data sources belong to the federation. The system further includes a query fail analyzer that updates a data structure to reflect the unsuccessful execution of one or more source queries of the failed set.

The invention discloses an MES system used for engine chain wheel chamber shell assembling detection. The system comprises a lower computer data monitoring acquisition system located on 4 processing devices and an upper management system located on 1 gathering server. The whole system integrates management functions of production data acquisition, production process fault prevention, workpiece preorder processing detection, workpiece encasement coding, product tracing, production data export, team management, material detection and the like. 5 devices are located in a same local area network. Through uniformed relation database software and network communication, database inter-access operation between a local industrial control computer and the server is performed. By using the system, in a final assembling detection operation of a chain wheel chamber shell, real-time monitoring of a production state is performed; a completion condition of each operation of each workpiece and good and bad information can be inquired and analyzed in an upper computer; the MES system can help a user to carry out production query; and encasement efficiency is increased and a product error rate is reduced.

The invention provides a method for determining state maintenance time of power distribution equipment, belongs to the technical field of electric power engineering, and solves the problems that conventional equipment maintenance of a power distribution network is mostly periodic maintenance and after-event maintenance, the utilization efficiency of maintenance resources is low, and fault prevention cannot be performed for various equipment differences. According to the method, health state grading is carried out according to a device comprehensive deduction value, an entropy weight method isapplied to assign weight to a device state evaluation index, then a health state cloud chart of a to-be-evaluated device is generated through a cloud model, and then the membership degree between thehealth state cloud of the to-be-evaluated device and each health state grade cloud is calculated; and finally, inputting data such as the membership vector, the number of faults and the operation record into a long-term and short-term memory network for training to predict the next fault occurrence time of the to-be-evaluated equipment so as to reasonably plan an overhaul plan. Reasonable planningof an in-operation equipment maintenance plan is realized according to the predicted fault occurrence time, maintenance resources are effectively and reasonably utilized, and the equipment fault riskis reduced.