151 results about "Fault management" patented technology

The invention discloses a computer memory bank fault prediction method and system based on deep learning, and relates to the technical field of computer hardware fault diagnosis, and the system comprises a multi-source time sequence data collection module which is used for obtaining memory bank operation state data in real time; the dynamic feature enhancement module is based on a composite architecture of a generative adversarial network and transfer learning, comprises a fault mode generator, and generates synthetic data consistent with real fault distribution by using an LSTM network; aligning feature spaces of different hardware platforms through a maximum mean difference loss function; the multi-modal fusion deep learning model comprises a space-time convolutional network, a graph attention network and an adaptive weight adjustment mechanism; and the fault early warning analysis module is used for analyzing a fault probability predicted value, an interpretable thermodynamic diagram and a maintenance suggestion. According to the invention, passive maintenance is changed into active prevention and control, and preposition and precision of fault management are realized through dual mechanisms of long-term trend prediction and short-term risk early warning.

The invention relates to the technical field of lithium battery diagnosis, in particular to a lithium battery fault diagnosis system based on real-time impedance monitoring, which comprises a disturbance signal acquisition module, an impedance track extraction module, a polarization response comparison module, an abnormal period judgment module and a structural risk output module. According to the method, a dynamic comparison mechanism of polarization response time difference and structure state parameters is introduced, periodic risk grading and characteristic quantity marking are linked, tiny response variation in the lithium battery operation process is subdivided, quantitative output of key nodes is achieved by fusing pole piece dynamic characteristics, periodic risk state traceability and abnormal node tracking are supported, and the reliability of the system is improved. According to the invention, the method can achieve the fine structural abnormality marking and multi-stage alarm, improves the timeliness and foresight of structural risk management, can achieve the parallel collection and processing of multi-element response signals, promotes the high-reliability operation and distributed fault management and control of a lithium battery system, expands a safety monitoring boundary, and improves the precision and granularity of active early warning and management.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

The invention provides an intelligent management method and system for global environmental control heat dissipation of a wind power plant, and is applied to the technical field of data processing. According to the application, wind power plant equipment operation and environment parameters are obtained, a global environment control sensing module is used, a core equipment state is monitored by means of multi-sensor fusion, and an equipment-environment association model is constructed to obtain a preliminary heat dissipation demand; an intelligent heat dissipation regulation and control module optimizes a strategy to generate a precise control instruction; the frequency converter fault-free management module dynamically adjusts parameters to form fault-free management information; coordinating equipment linkage through a joint optimization strategy to generate a global environment control scheme; and finally, the operation efficiency evaluation module outputs evaluation information containing efficiency and a safety coefficient, so that intelligent management of heat dissipation of the wind power plant is realized.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

The invention provides a vehicle cloud collaborative vehicle fault monitoring method and system and an electric vehicle, and relates to the technical field of electric vehicles. The method comprises the steps that a vehicle-mounted system collects actual operation data of a vehicle in real time, preliminary fault diagnosis is conducted on the electric vehicle based on the actual operation data of the vehicle, and a local fault diagnosis result is obtained; vehicle actual operation data and a local fault diagnosis result are uploaded to a cloud server; the cloud server inputs the actual operation data of the vehicle into a pre-constructed vehicle dynamic model to obtain theoretical parameter information of the electric vehicle under the current working condition; cloud fault diagnosis is carried out on the electric vehicle according to the theoretical parameter information of the electric vehicle under the current working condition and the actual operation data of the vehicle to obtain a cloud fault diagnosis result, and a corresponding processing instruction is sent to the vehicle-mounted system according to the cloud fault diagnosis result and a local fault diagnosis result. Local end diagnosis-cloud end diagnosis-double-end decision making is realized, and the timeliness and reliability of fault management are improved.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

The invention relates to the technical field of fault detection, in particular to an electronic component fault management method and system. The method comprises the following steps: collecting real-time data and constructing a real-time fault monitoring data set; reading historical data and constructing a historical reference data set; comparing the real-time fault monitoring data set with the historical reference data set item by item in a corresponding time window to obtain a drift amplitude, and mapping according to the drift amplitude to generate a reverse excitation amplitude instruction; a reverse current signal according to the reverse excitation amplitude instruction is injected into the loop node to be measured, and a Hall null drift amount is determined according to Hall output; outputting an electronic disturbance quantity based on the Hall null drift quantity; therefore, through dynamic data cooperation and an active excitation response mechanism, the problems of missing detection recessive degradation, misjudgment transient interference and single diagnosis dimension of traditional fault management are solved, and the hidden danger recognition precision and the protection reliability are remarkably improved.

The invention discloses a CDN fault node isolation method and system based on telecom operator network management data, and relates to the technical field of CDN fault management and operation and maintenance, and the method comprises the steps that an operator network management center collects multi-stage network equipment and link data, and a CDN health center collects indexes such as node packet loss rate; comparing an index and a threshold value quality index degree which exceeds a threshold value, and marking a suspected node; querying network management data to locate a fault category; calculating a score by combining a fault position weight, and isolating if the score exceeds a threshold value and the fault position is a node or a direct connection link; eliminating nodes and scheduling traffic; and quickly triggering the alarm, subscribing the network management alarm, directly marking the suspected node and triggering the process. According to the method, the isolation accuracy of the CDN fault node is improved, and misjudgment caused by an upstream network fault is reduced; the isolation response time delay is shortened; congestion aggravation is inhibited; a quantifiable and configurable isolation decision system is formed, the operation and maintenance intelligence is improved, and the service quality and the user experience are optimized.

In some examples, a system monitors for a memory fault associated with execution of software. Based at least on receiving an indication of a possible memory fault, the system scans a first area of the memory based on a data structure that indicates that the first area of the memory stores a portion of parameters of the software that affect an accuracy of the software if there is a memory fault in the first area of the memory. Based at least on the scanning indicating that there is a memory fault in the first area of the memory, the system remaps the portion of parameters of the software from the first area to a second area of the memory that is determined to be free.

The application discloses a kind of air pump unit fault management systems, it is related to air pump fault management technical field, real-time acquisition and air pump safety operation related multi-source data are obtained by sensing equipment, processing module is preprocessed to the multi-source data collected, including data cleaning, denoising, etc. Operation is to ensure data quality and accuracy. Feature data is extracted from the data after preprocessing, analysis module will be obtained into air pump prediction model and comprehensively analyzed after feature data, air pump prediction model is air pump and outputs an operation assignment, regional division module will air pump be divided into corresponding area according to the comparison result of operation assignment and gradient threshold value, and according to the regional division result of air pump, corresponding management strategy is generated, and management strategy is sent to administrator. The management system can effectively predict the fault of air pump in the operation process, so as to carry out early warning and management before air pump failure, guarantee the safety and stability of air pump operation.

A device and method for fault management for an electric power distribution network incorporating intermittent generation sources. The method involves configuring multiple hyperparameters for a series of Convolutional Neural Networks (CNNs). A first CNN is trained using current signal imagery from phasor measuring units (PMUs) during fault conditions to classify faults. A second CNN is trained with signal images from PMUs captured during pre-fault and fault cycles for identifying fault sections. Similarly, a third CNN is trained using these images to determine the exact fault location. Once trained, the CNNs are employed sequentially. The first CNN classifies the fault, the second detects the fault section, and the third ascertains the fault location. Subsequently, a comprehensive fault management strategy is deployed.

Implementations for chiplet fault management through hierarchical error reporting and coordinated recovery in an accelerator interconnect. In some implementations, a system for a UALink-based network comprises a chiplet die coupled to an accelerator die, a management agent on the accelerator die, and a centralized controller. Upon fault detection, the chiplet die transmits a fault notification to the accelerator die, the management agent reports the fault to the centralized controller, and the centralized controller determines a recovery action based on the fault identifier and severity. In some implementations, the accelerator die reports the fault through dual paths, to a host processor via CXL or PCIe error reporting and to the centralized controller via the management agent, and the host processor and the centralized controller coordinate recovery, with the host managing workload-level recovery and the centralized controller managing network-level recovery.

The invention discloses an anti-interference measurement system of an out-of-pile pulse type detector, which comprises an FPGA (Field Programmable Gate Array), and the FPGA is characterized in that in a normal working state, a test module controls a channel selection module to be switched to a measurement channel; the signal processing module performs validity detection on the signal waveform, generates a fault information data packet and sends the fault information data packet to the fault management module if the signal waveform fails, performs three-path parallel processing on the signal if the signal waveform is valid to obtain the signal waveform, timestamp information and a signal type, and obtains counting rate information of the signal according to the signal type and the timestamp information; an interference signal data packet and a nuclear signal data packet are generated and sent to the communication module, the signal processing module detects the validity of signal waveforms and timestamps in real time, and when the signal waveforms and the timestamps fail, a fault information data packet is generated and sent to the fault management module; the parameter module carries out self-recovery processing, and the test module and the signal processing module verify the self-recovery effect. The system improves the anti-interference capability, accuracy and reliability of measurement.

The present invention provides a physical signal monitoring-based transmission link fault location method and system in the technical field of transmission link fault location. The method comprises: step S1, a layer 2 switching chip creating a register group, and an MCU creating a MAC address table; step S2, when a new network device is accessed, the MCU performing anomaly verification on a new MAC address by means of the MAC address table and then updating the MAC address table; step S3, performing fault location by means of a queue controller watchdog event, and updating an event register on the basis of a fault type; step S4, the MCU reading the fault type from the event register to match a fault handling measure from a fault management table; and step S5, when a fault handling measure is found, executing a transmission link recovery operation on the basis of the fault handling measure, and when no fault handling measure is found, reading switch data from the register group and uploading the switch data to a server for fault location, or executing fault location on the basis of a console command inputted from a Console interface. The present invention has the advantage that the convenience and timeliness of transmission link fault location are greatly improved.

The application provides a power battery fault management and control method and system, wherein the method comprises: acquiring a fault data set and a knowledge graph, the fault data set comprising various signal time-frequency graphs of the power battery and corresponding fault descriptions, the fault descriptions comprising fault phenomena and fault keywords, and the knowledge graph comprising mutual relationships between the fault phenomena, the fault keywords, fault causes and management and control decisions; performing multi-task reasoning training on a multi-task large model according to the fault data set to obtain an initial fault management and control model; performing multi-task reasoning optimization training on the initial fault management and control model by taking the mutual relationships in the knowledge graph as reasoning constraints to obtain a target fault management and control model; and performing fault management and control on the power battery by using the target fault management and control model. The target fault management and control model is obtained in a combination mode of the multi-task large model and the knowledge graph, the power battery fault management and control is realized, and the accuracy and efficiency of the power battery fault diagnosis are effectively improved.

This application relates to the field of industrial configuration technology, and provides a fault management method, system, and electronic device applied to industrial configuration. This application sets up original nodes, including master nodes and redundant nodes, for different business layers of the industrial configuration. Based on monitoring data corresponding to the industrial configuration, the node status of different original nodes is predicted. If the predicted status of the master node corresponding to any business layer is abnormal, a new master node is determined from the redundant nodes corresponding to the master node. This transforms the independent fault management functions of each business layer into a cross-layer collaborative management mechanism. Before any master node at any business layer may experience an abnormal state, a smooth switch of business tasks is completed, thereby greatly enhancing the continuity and stability of the production process, avoiding production interruptions or fluctuations caused by sudden system failures, and thus reducing the management and maintenance costs of the industrial configuration.

A Kafka header based schema version management in encoding and decoding Performance Management (PM) / Fault Management (FM) data. A microservice prepares one or more Encoding Type or Schema Version Tags based on one or more Encoding Type or Schema Source Files used by the microservice. The microservice encodes data using the Schema Version identified by the one or more Encoding Type or Schema Version Tags to produce encoded data, embeds the one or more Encoding Type or Schema Version Tags in a Header of a Kafka Message, and embeds the encoded data in the Message Body of the Kafka Message. The Microservices then sends the Kafka Message to a Northbound Management System for decoding based on the one or more Encoding Type or Schema Version Tags in the Header of the Kafka Message.

Provided are a method and system for handling northbound fault event reporting for the FM MS. The method, may be performed by at least one processor for reporting data to a northbound management system (NBMS), the method including: receiving, by a fault reporting microservice (FR MS) from an Fault Management microservice (FM MS) of a first Radio Access Network (RAN) Network Function (NF), first data comprising fault event data, the FR MS being a host level MS; and reporting, from the FR MS to the NBMS, the first data.

The application relates to the field of financial technology, and discloses a fault management method, device and equipment of a service arrangement system and a storage medium, comprising the following steps: acquiring historical system data of the service arrangement system in different time periods; performing feature selection on the historical system data, constructing a data set according to the selected features; constructing a to-be-trained anomaly detection model by using the data set and training the to-be-trained anomaly detection model to obtain a trained anomaly detection model; screening target features from the acquired current system data of the service arrangement system; inputting the target features into the trained anomaly detection model to obtain an anomaly detection result of the service arrangement system. The application supports intelligent fault detection, improves the existing fault detection mechanism, introduces machine learning and anomaly detection algorithms, can automatically identify abnormal conditions by analyzing a large amount of logs, index data and behavior patterns, and more accurately detects and identifies potential faults.

The application discloses a computer fault alarm system and method, which is applied to the technical field of fault alarm and solves the technical problem of computer fault alarm. The computer fault alarm method comprises the following steps: (S1) collecting and storing computer fault information in a listener database through a fault management module; (S2) periodically deleting the computer fault information in the listener database on the basis of partitioning by a listener service process module; (S3) updating the computer fault information in an alarm database and an event database by a listener service program module, and transmitting the computer fault information to the listener database; and (S4) receiving fault alarm information output by the listener database by the fault management module, and controlling an alarm to send an alarm sound and light signal. The computer fault alarm system greatly improves the efficiency of processing computer fault information, and ensures that the error of the processing result of the computer fault information is less than 3%.

The embodiment of the invention provides a passive equipment state monitoring method, device and equipment, a medium and a product, which are applied to the technical field of optical communication. The method comprises the following steps: acquiring multi-source monitoring data of a target passive optical network based on a preset acquisition frequency; wherein the multi-source monitoring data comprises passive equipment state data, network performance data, service operation data and environment associated data in the target passive optical network; performing feature fusion processing based on the multi-source monitoring data to obtain a multi-dimensional feature vector; and performing multi-task analysis on the target passive optical network based on the multi-dimensional feature vector to obtain a passive equipment state reasoning set, a passive equipment fault root cause set and a passive equipment predictive maintenance set of the target passive optical network. The method achieves the technical effect of improving the fault management efficiency of the passive optical network.

The application belongs to the technical field of integrated circuit design and manufacturing, and particularly relates to an integrated circuit memory fault management method and system based on an NPU processor architecture. First, multi-dimensional detection is carried out specially for the memory size, storage area and handshake protocol effectiveness of a physical address, which changes the status of passive transmission of abnormal signals in the traditional scheme. Then, according to the fault detection result, a control signal is reversely output to intervene in the mapping logic, so as to effectively avoid fault diffusion caused by continuous transmission of abnormal addresses, and when an address abnormality is detected, subsequent physical addresses can be forcibly relocated to a preset safe address area, reliable safety backup is provided, and the running stability is improved.

The invention relates to the field of device fault management, in particular to a fault prediction and management system for an overdue service relay protection device, and the system comprises a device monitoring unit which is used for determining the type of a target device, and determining whether to adjust the fault prediction trigger reference from the same-batch image difference degree to a review image difference degree based on the device category difference value; the data analysis unit is used for judging whether fault analysis needs to be carried out or not based on a fault prediction triggering benchmark corresponding to a benchmark analysis condition, and judging whether the index revision instruction comprises acquisition frequency increasing processing or not and whether manual early warning is carried out or not according to a response control delay degree difference value corresponding to one type of devices; the fault analysis unit is used for judging whether aging fault early warning is carried out or not and whether comparative analysis is carried out or not according to the gray level change reference value and the column pixel brightness difference value corresponding to the first-class device, and judging whether the index revision instruction comprises interface increment acquisition or not; according to the invention, the fault monitoring automation efficiency is improved.

The invention discloses an intelligent unmanned aerial vehicle fault early warning and self-healing control system based on deep learning, and relates to the technical field of unmanned aerial vehicle control, and the system comprises an unmanned aerial vehicle monitoring module which is used for collecting operation state parameters including attitude, sensor detection and environment perception data, carrying out the preprocessing of the operation state parameters to obtain a standardized time sequence data sequence, and carrying out the self-healing of the operation state parameters; transmitting to a deep learning module; the deep learning module is used for receiving the standardized time sequence data sequence and completing fault feature extraction and type identification based on a pre-training model of a PatchTST-Mixer improved architecture; according to the model, historical faults and normal operation data of the unmanned aerial vehicle are taken as samples, and after comparative learning and Focal Loss mixed strategy training optimization, a fault identification result is output. According to the method, the fault identification precision is improved, the missed detection and misjudgment rate is reduced, autonomous and rapid fault response is realized, and manual dependence is greatly reduced; intelligent and autonomous unmanned aerial vehicle fault management is integrally realized, and complex operation scenes and large-scale operation and maintenance requirements are met.

The invention discloses a three-phase photovoltaic inverter fault management and control system based on data integration, relates to the technical field of inverter fault management and control, and aims to solve the technical problem that in the prior art, iterative operation analysis cannot be performed through operation simulation model construction, so that blind detection is caused during fault recognition. The method specifically comprises the steps that the system integrates data of an operation simulation unit, a fault feature extraction unit and an environmental influence analysis unit through a fault management and control platform, a whole-process data closed loop of a fault from positioning, diagnosis, adjustment and environmental adaptation is achieved, meanwhile, a failure mechanism and an adjustment mechanism of operation ends of the same type are continuously accumulated and updated, and the fault management and control platform is established. And a reusable operation and maintenance knowledge base is formed, and data support is provided for long-term operation and maintenance and preventive maintenance of the photovoltaic inverter.

The invention discloses an energy storage system NTC sensor fault detection method. The method comprises the steps that NTC fault data and battery operation condition data are acquired; according to a correlation result of the NTC fault data and the battery operation condition data, constructing an initial condition probability table based on the battery operation condition data and the NTC fault data with strong correlation; constructing a to-be-trained dynamic Bayesian network model based on the initial condition probability table, and iteratively training the to-be-trained dynamic Bayesian network model by using the verification set to obtain a target dynamic Bayesian network model; the verification set comprises the input real health degree; and inputting the current NTC fault data and the current battery operation condition data into the target dynamic Bayesian network model to obtain a detection result. According to the NTC sensor fault treatment method based on cooperation of real-time fault diagnosis and long-term risk prediction, instant fault positioning and potential risk pre-judgment can be achieved at the same time, and comprehensiveness and perspectiveness of NTC fault management and control are improved.