33 results about "Reliability management" patented technology

The invention belongs to the technical field of reliability management of nuclear power plants, and particularly relates to a multi-focal-length visible light array cameras, a cooperative control unit and a central analysis platform, the multiple multi-focal-length visible light array cameras are located on the end side, and conversion from physical world real scenes to digital signal images is completed; the cooperative control unit is located on the side and is responsible for receiving, processing and analyzing data of the multiple multi-focal-length visible light array cameras, executing AI reasoning with high delay requirements, driving a holder and a lens to act and uploading related result data. The central analysis platform is located at a cloud side, gathers alarm data of all side devices, and carries out global situation presentation, historical data storage and backtracking, long-term trend analysis and multi-system linkage. The system has the beneficial effects that the tradeoff limitation among the view field, the distance and the precision in the prior art is overcome, and the industrial problem that the large view field cannot be considered is creatively solved through the cooperative working mode of the multi-focal-length optical array.

The invention discloses a generator control system operation reliability analysis method based on RCM, and belongs to the technical field of power equipment reliability management, and the method comprises the steps: determining the boundary of a generator control system based on the performance standard of functional elements, and dividing functional subsystems; identifying a failure mode type of each functional subsystem through FMEA, and generating a minimum failure set in combination with a redundancy check strategy; performing risk scoring on the failure mode of each functional subsystem based on multiple influence dimensions by using the minimum failure set, and generating a risk priority matrix in combination with historical shutdown loss data; dividing risk levels according to the risk priority matrix, and matching the risk levels with a repair strategy of a failure mode type; and integrating real-time state data of the equipment, verifying a repair strategy through Monte Carlo simulation, and iteratively correcting a minimum failure set and a risk score.

The invention belongs to the technical field of nuclear power DCS health management, and aims to provide a DCS health management platform for nuclear power plant DCS operation and maintenance business application. The invention discloses a DCS (Distributed Control System) health management platform based on agile development and a general-branch architecture, which comprises a display layer, a business application layer, a model and service layer, an operation and maintenance deployment layer and an infrastructure layer, and is characterized in that intelligent operation and maintenance management, software and hardware state remote monitoring and service life prediction of a nuclear power plant DCS are realized through the business application layer; and a dynamic logic deduction and fault tracing mechanism is constructed, full-process digital operation management and control, overall planning of a maintenance plan, authority auditing and intelligent guiding are carried out, and a full-life-cycle data system is established. According to the invention, DCS operation and maintenance service application is provided for the nuclear power plant, the reliability management of the DCS of the nuclear power plant and the risk analysis level of an instrument control system can be improved, and the probability of unexpected unit operation events caused by the DCS is reduced.

The invention relates to the technical field of battery management, in particular to an intelligent charging and discharging management method and system for an aviation storage battery, and the method comprises the steps: superposing a zero-mean symmetric current waveform to a pilot substring in a floating charge state, and combining with the synchronous sampling of voltage and temperature, obtaining a voltage maintaining transient core and a hot ready transient core, and finally, predicting a voltage holding track and a temperature rise track through nuclear playback, and extracting key parameters as response indexes. According to the invention, on the premise of not interrupting the flight power supply safety, the health state and dynamic characteristics of the aviation storage battery can be evaluated in real time, and support is provided for high-reliability management and service life prolonging.

The application discloses a kind of based on the automatic construction method of fault tree of multi-signal flow graph.The fault tree automatic construction method is aimed at solving the problems such as low construction efficiency, many logic blind areas, dynamic optimization difficulty in complex system in existing method.The fault tree automatic construction method is divided into three steps: multi-signal flow graph entity extraction based on ontology constraint, entity structure regularization based on flow graph connectivity and fault tree data automatic layout based on depth-first.The fault tree automatic construction method uses the node and attribute information in multi-signal flow graph, establishes dynamic corresponding relationship with fault tree node, realizes accurate mapping from entity to fault tree node;By skipping irrelevant node, completing associated relationship and automatically inserting "or" node, the integrity of fault tree logical link is ensured;By depth-first branch statistics and breadth-first automatic layout, global information is obtained and top-down layered layout and inter-node connection are realized, and finally the automatic drawing of fault tree is completed.This method supports complex system fault analysis requirements across components, across levels, significantly improves the efficiency and accuracy of fault tree construction, and provides a new technical means for modern reliability management.

The application discloses a power supply reliability full-service intelligent retrieval method and system, and the method comprises the following steps: storing power supply reliability management system data into a graph database; preprocessing natural language to obtain a standardized input sentence; performing word segmentation on the standardized input sentence, performing word segmentation screening on the word segmentation list, and generating a corresponding cypher query statement based on a rule matching principle and a decision tree; generating a cypher query statement based on rule matching and a decision tree for the user input sentence, respectively, performing consistency calculation on the coded results of the two, when there is consistency, querying the corresponding data through the cypher query statement, and when the two are inconsistent, converting the cypher query statement generated by the decision tree into corresponding natural language to ask the inputter for confirmation; the application has the advantages that the query accuracy is improved, and retrieval failure is avoided.

The embodiment of the invention discloses an insulation monitoring and early warning method and device for high-voltage equipment, equipment and a medium. A specific embodiment of the method comprises the steps of obtaining a multi-source signal set corresponding to high-voltage equipment in a smart power grid; performing interference separation processing on the partial discharge pulse signal to obtain a processed partial discharge pulse signal; performing first regulation and control processing on the high-voltage equipment based on the processed partial discharge pulse signal and the temperature distribution signal; performing optimization processing on the dielectric loss factor signal based on the temperature distribution signal to obtain an optimized dielectric loss factor signal; performing second regulation and control processing on the high-voltage equipment based on the processed partial discharge pulse signal and the optimized dielectric loss factor signal; and executing early warning processing according to the acquired first identification information and second identification information. According to the embodiment, insulation on-line monitoring and early warning are achieved, and a reliable decision basis is provided for safe operation and maintenance and reliable management of high-voltage equipment in an intelligent power grid.

The invention relates to the technical field of reliability management of electromechanical equipment, in particular to an electromechanical equipment reliability evaluation method based on a switching Markov process and fractional Brownian motion, which comprises the following steps: constructing a switching Markov process model, the description module is used for describing a multi-stage degradation process experienced by equipment in a life cycle and state transition thereof; based on the external working condition parameters and the switching Markov process, constructing a stage dependency model; establishing a nonlinear degradation model of the electromechanical equipment based on the fractional Brownian motion process, and introducing a random effect into the model to represent individual differences; and constructing a two-stage parameter estimation method, and sequentially estimating unknown parameters in the switching Markov process and the degradation model. The performance degradation of the electromechanical equipment is accurately modeled, and the nonlinearity, randomness, individual difference, long-term memorability and stage dependence of the multi-stage degradation of the electromechanical equipment are effectively captured, so that the reliability of the electromechanical equipment is accurately evaluated.

To provide a software reliability management device and a software reliability management method, capable of determining a security measure considering a terminal environment in which software operates, and of protecting a business continuation.SOLUTION: A software reliability management device includes a processor and a storage unit. The storage unit stores software that serves as a management object. According to information on a terminal environment in which the software operates, the processor calculates a reliability for one or more items to the software stored in the storage unit, compares, by each item, the reliability and a reliability threshold value corresponding to each of one or more items of the reliability, and determines that a reliability improvement measure is required to the item of the software according to a result from the comparison.SELECTED DRAWING: Figure 1

The application discloses a power distribution network supply and demand reliability management method and a terminal, establishes a historical power supply database, establishes a supply and demand comparison database according to the historical power supply database and a historical simulation database, and can obtain the historical supply and demand comparison situation of the power distribution network; total power supply percentage data is established according to the supply and demand comparison database, and a comparison difference value of the supply and demand comparison database is processed through a big data calculation mode; then, the supply and demand difference value of simulation power supply is reduced by combining the total power supply percentage data and the processing result of the comparison difference value, a power supply simulation database is established, and data in the power supply simulation database is encrypted and transmitted to a target power grid. In this way, the supply and demand excess difference value can be reduced, the reliability of the power distribution network can be effectively improved, and the operation and maintenance cost of the power distribution network is reduced.

The invention provides an industrial equipment preventive maintenance decision-making method based on a discrete time Markov chain, and relates to the technical field of industrial equipment reliability management and maintenance strategy optimization. The method comprises the following steps: based on historical maintenance record statistics, establishing a discrete time Markov chain maintenance effect state transition model and determining a state transition probability matrix thereof, carrying out eigenvalue decomposition on the state transition probability matrix to obtain state probability distribution after n times of maintenance, and determining each state probability; carrying out weighted fusion on the random intensity functions of the three maintenance processes through corresponding state probabilities, and constructing a mixed fault rate function; calculating an expected failure frequency based on the mixed failure rate function; and based on the expected failure times, establishing a long-term cost rate function, and solving to determine an optimal replacement period. According to the method, three basic maintenance effect types are integrated through the dynamic probability, and accurate prediction of the number of failure times of equipment and calculation of the optimal maintenance period are achieved.

The invention relates to the technical field of power system reliability management, and discloses a power reliability management platform based on defect integration and grading classification, which comprises a defect acquisition module, a grading classification module, a reliability evaluation module, a dynamic monitoring module, a mode switching module, an emergency response module and a risk early warning module. The platform provides reliability evaluation in a conventional mode and an emergency response mode by integrating defect information of power equipment and performing grading and classification processing, dynamically adjusts an evaluation process in combination with real-time operation state data, and predicts a potential risk level through a risk early warning module. The method can improve the reliability management level of the power system, is suitable for a complex and changeable operation environment, and achieves the precise evaluation and dynamic optimization.

The invention discloses a power failure data credible management method based on a block chain, and relates to the technical field of power supply reliability management, and the method comprises the following steps: data uplink management: obtaining a power failure event and reliability data, carrying out Hash calculation, generating a unique data fingerprint, and writing a Hash value and related metadata into the block chain; performing uplink configuration management: performing failure compensation operation after the uplink failure event is triggered; on-chain checking management: through comparison of Hash calculation and block chain original evidence, performing automatic and manual checking on business data consistency; checking task management: carrying out Hash calculation on the power supply reliability power failure data, then comparing the power supply reliability power failure data with an on-chain Hash value, marking items with inconsistent comparison results, and recording the items; and chain traceability management: displaying the traceability list according to a reverse order of time, positioning corresponding items according to query conditions, and checking chain evidence storage tracks and operation history of the corresponding items. The defects in the prior art can be overcome, and the efficiency and accuracy of data checking are improved.

The application discloses a power outage data credible management method based on a block chain, relates to the technical field of power supply reliability management, and comprises the following steps: data chain management: obtaining power outage events and reliability data, performing hash calculation, generating a unique data fingerprint, and writing the hash value and related metadata into a block chain; chain configuration management: performing failure compensation operation after a chain failure event is triggered; on-chain verification management: comparing the hash calculation with the original block chain storage, automatically and manually verifying the consistency of business data; verification task management: comparing the hash calculation of power supply reliability power outage data with the on-chain hash value, marking the inconsistent items and recording; on-chain traceability management: displaying a traceability list in reverse chronological order, locating corresponding items according to query conditions, and checking the on-chain storage track and operation history. The application can improve the defects of the prior art and improve the efficiency and accuracy of data verification.

The invention discloses an equipment operation state processing method and device, equipment, a medium and a product. The method comprises the following steps: if it is detected that current time meets an evaluation period condition preset for an equipment class, obtaining a period measured value of the equipment class under each preset evaluation index from a data table through a configured data extraction rule; the evaluation indexes cover the five fields of power generation health, equipment fault, state monitoring, reliability management and experience feedback; determining an actual score and a color grade of each evaluation index of the equipment class according to a pre-configured total score of the evaluation indexes, a scoring criterion of the evaluation indexes and a periodic measured value; determining the total score of the operation state of the equipment class according to the actual scores of all the evaluation indexes under the equipment class; and generating a structured equipment class health evaluation report based on the operation state total score of the equipment class, the actual score of each evaluation index and the color grade. The embodiment of the invention can improve the equipment operation management efficiency and the unit operation reliability.

A compute apparatus and method provide redundancy-aware dynamic power and performance balancing. When redundant columns are activated to replace defective columns, a control processor or local controller automatically adjusts clock frequency, voltage, and workload mapping to maintain uniform timing, throughput, and thermal conditions across compute rows. Compensation is determined using telemetry feedback and predictive models and applied through per-row voltage regulators and clock circuits. The system preserves consistent performance after redundancy reconfiguration without manual recalibration, linking reliability management with real-time adaptive power control.

The invention relates to the technical field of degradation prediction of power electronic devices, in particular to a short-time overload degradation prediction method and system for a power electronic device. The method comprises the following steps: acquiring short-time overload data; performing feature extraction according to the short-time overload data to obtain short-time overload feature data; performing time-frequency diagram construction according to the short-time overload characteristic data to obtain time-frequency diagram data; performing acoustic pseudo-spectrum feature generation according to the time-frequency graph data to obtain acoustic pseudo-spectrum feature data; and performing degradation prediction on the acoustic pseudo-spectrum characteristic data to obtain short-time overload degradation prediction data. According to the method, the pseudo-spectrum characteristics can be constructed through the short-time overload electric parameters under the condition that no extra sensor is needed, early perception and degradation trend prediction of device micro-damage are achieved, and the reliability management capacity of a power electronic system under the high-frequency dynamic working condition is improved.

The application discloses an artificial intelligence silent evolution control system based on a fusible, contractible, auditable and interpretable edge protocol and belongs to the technical field of artificial intelligence system safety and reliability management. The system comprises an edge protocol bus, an interpretable module, a fusible module, a contractible module, an auditable module and a silent evolution management layer. The interpretable module calculates the confidence and the feature attribution path of the model output. The fusible module triggers the fuse according to the preset confidence condition and publishes an event. The contractible module executes dynamic resource contraction in response to the fuse event. The auditable module generates a three-tuple association log of the confidence, the fuse action and the contraction action. The edge protocol bus provides standardized event communication, module registration and version negotiation. The application solves the technical problems that the existing AI management module is isolated and cannot actively defend and heat evolve based on the output quality, and is suitable for high-risk AI application scenarios such as finance, medical treatment and automatic driving.

A method for managing reliability of a cloud application includes deploying one or more detection engines into a customer environment. The method also includes monitoring data related to the reliability of the cloud application from within the customer environment. The method further includes detecting, by the one or more detection engines, a parameter affecting the reliability based on a set of rules. In addition, the method includes generating a response to interrogate the customer environment based on the parameter.

The present application relates to the technical field of power electronic device degradation prediction, and particularly relates to a power electronic device short-time overload degradation prediction method and system. The method comprises the following steps: obtaining short-time overload data; performing feature extraction according to the short-time overload data to obtain short-time overload feature data; performing time-frequency graph construction according to the short-time overload feature data to obtain time-frequency graph data; performing acoustic pseudo-spectrum feature generation according to the time-frequency graph data to obtain acoustic pseudo-spectrum feature data; and performing degradation prediction on the acoustic pseudo-spectrum feature data to obtain short-time overload degradation prediction data. The present application can construct pseudo-spectrum features through short-time overload electrical parameter data without additional sensors, realize early perception and degradation trend prediction of device micro-damage, and improve the reliability management capability of the power electronic system under high-frequency dynamic working conditions.

The invention discloses a mining equipment fault causal analysis method based on a knowledge graph, equipment and a medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: collecting real-time sensor data flow, historical operation data, maintenance cost records, production plan data and unstructured historical maintenance reports of gold mine key equipment; constructing an equipment fault causal knowledge graph; according to a causal analysis path packaged in the operation and maintenance decision script, directionally collecting refined data of gold mine key equipment, performing logic consistency verification, and determining a fault root cause and a causal chain influencing a production plan; and generating a decision scheme based on the fault root cause and the causal chain influencing the production plan. According to the method, the equipment fault causal knowledge graph is constructed, so that early-stage accurate early warning and predictive maintenance decision support of the gold mine key equipment hidden fault are realized, and the equipment reliability management level is improved.

The application relates to the technical field of battery management, in particular to an intelligent charge-discharge management method and system for an aviation storage battery. The method comprises the following steps: superimposing a zero-mean symmetric current waveform on a pilot sub-string in a floating state, combining synchronous sampling of voltage and temperature, obtaining a voltage holding transient kernel and a thermal readiness transient kernel, thereby characterizing the potential holding capacity and temperature rise accessibility of the sub-string, finally predicting the voltage holding trajectory and temperature rise trajectory through kernel playback, and extracting key parameters as response indicators. The application can evaluate the health state and dynamic characteristics of the aviation storage battery in real time under the premise of not interrupting the safety of flight power supply, and provides support for high reliability management and life extension.

The application discloses a kind of oil and gas equipment fault database construction methods based on multiple algorithms, method includes (i) the basic information of oil and gas equipment is collected;(ii) data management and configuration;(iii) oil and gas equipment fault record preprocessing;(iv) oil and gas equipment fault library analysis method and path;(v) oil and gas equipment fault library version update etc. Step.The application establishes oil and gas equipment hierarchy and fault mode, and the preprocessing and self-matching process of fault record;Innovative application OREDA method, OREDA-based bayesian method, multiple distribution life data analysis method Multiple algorithms, according to the failure characteristics and quantity of oil and gas equipment Actual selection of the best analysis method path, realize the result analysis and comparison between different methods;Through oil and gas equipment fault database construction and accumulation, realize higher scientificity and accuracy in equipment management, bring greater value and help for equipment reliability management and operation optimization.

The invention belongs to the technical field of nuclear power plant equipment reliability management, and particularly relates to a nuclear power plant conventional island pipeline health management and state evaluation system and method. Comprising an external data module, a standardized input tool, a solver and a prediction module, after information is input into external data in the external data module through the standardized input tool, intermediate process data is generated through calculation of the solver, an output result is evaluated through the prediction module, and corresponding operation and maintenance decision suggestions are generated according to evaluation results of different degrees. The beneficial effects are that health management and state evaluation are carried out for fault modes of nuclear power plant steam-water pipeline corrosion, small branch pipe vibration fatigue failure and the like, and the nuclear power plant pipeline operation and maintenance efficiency is improved.

The invention discloses a single-station electric detection-photoelectric cooperative unmanned aerial vehicle identification and positioning method based on confidence propagation, and relates to the technical field of unmanned aerial vehicle positioning. The technical problems of difficulty in reliability quantification, fusion strategy solidification and insufficient anti-interference capability in heterogeneous sensor data fusion are solved, and full-process reliability management from bottom layer sensor data to top layer track output is realized. The confidence coefficient serves as a throughout-and-all key variable, is a quantitative representation of the working state of the sensor, is a driving factor fusing the weight and is also a judgment basis of anomaly detection, a multi-level quality guarantee system is formed, environment changes can be adapted without manual parameter adjustment, the positioning capability is kept when a single sensor breaks down, and the reliability of the system is improved. Abnormality is automatically recognized when multiple sensors conflict, and the intelligent level and actual combat reliability of a low-altitude unmanned aerial vehicle prevention and control system are remarkably improved in a complex confrontation scene.

The utility model relates to a multifunctional attendance machine based on RCM system reliability management, which comprises a communication interface connected with an RCM system, the communication interface is connected with an identification card punching module used for collecting employee identity information and recording attendance data, the communication interface is connected with a card tape printer used for printing paper files, and the card tape printer is connected with a communication interface. The communication interface is connected with a display screen used for displaying a work task list pushed by the RCM system. The system has the beneficial effects that hardware support is provided for effective implementation of an RCM system so as to realize deep fusion of personnel dynamic attendance management and an equipment maintenance strategy, the intelligent level and efficiency of enterprise management are improved, a display screen is arranged so that employees can conveniently check a work task list at any time, the transparency and execution efficiency of work arrangement are improved, and the working efficiency is improved. The cassette printer is convenient for printing work task related files, is convenient for staff to refer during field operation, and improves the work accuracy.

The invention discloses a power plant equipment two-dimensional code and data synchronization information management and control system. The system comprises a multi-source data access module, a state abstract generation module, a two-dimensional code graphic rendering module, a code scanning interaction module, a log auditing module, a clock synchronization module, a centralized configuration operation and maintenance module and an emergency bypass hardware module. The system collects DCS, EAM and point inspection historical data in real time to generate a compressed and encrypted equipment state abstract, the abstract is rendered into a high-resolution two-dimensional code with a state color band, and the equipment operation state, the maintenance plan and the point inspection record can be obtained by scanning the code on site. And the mobile terminal dynamically displays information according to role permission after scanning the code, and supports offline caching and code scanning record write-back. And the log auditing module records the abstract message, the two-dimensional code version number and the code scanning event. According to the system, real-time visualization, safety, controllability, traceability and high-reliability management of equipment information are realized, and the operation and maintenance efficiency and safety of a power plant are remarkably improved.

The invention relates to a hard disk state determination method and device, computer equipment and a readable medium. The method comprises the steps that according to the current state of a target hard disk in a storage pool, reliability management is conducted on the target hard disk, a management result is determined, the actual state of the target hard disk is determined from a hard disk state configuration table according to the management result, and the target hard disk is any hard disk existing in the storage pool at the current moment or in the historical time period; the hard disk state configuration table comprises a plurality of states configured for the hard disk in advance in different operation stages. According to the method, after the current state of the target hard disk in the storage pool is obtained, the real state of the target hard disk is determined by conducting reliability management on the target hard disk, and therefore the precision of the determined actual state of the hard disk is improved.

The invention belongs to the technical field of equipment reliability management, and particularly relates to an outlier analysis method for same-type key equipment of a nuclear power plant. The method comprises the following steps: firstly, collecting operation data of various nuclear power equipment, and eliminating shutdown related equipment data through shutdown logic judgment; and then inherent difference is reduced through data normalization, difference analysis and clustering analysis are carried out, the outlier degree of the equipment is calculated, the equipment with abnormal operation is identified, an early warning or alarm value is set, and the alarm condition after the equipment exceeds the limit is tracked. According to the method, an overall model is established for monitoring all the devices of the same type, the device abnormity is identified through outlier analysis, the modeling and operation and maintenance cost can be reduced, abnormity occurrence can be more accurately captured, and the method is suitable for performance monitoring work of the nuclear power cluster device.