309 results about "Fault management" patented technology

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

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 invention relates to the technical field of computing power sharing, in particular to a computing power sharing system and method in a multi-tenant environment, and the system comprises a computing power monitoring module, a load analysis and prediction module, a task priority judgment module, a resource optimization configuration module and a fault management module. According to the invention, by implementing real-time resource monitoring and data analysis and accurately capturing the speed and mode of resource consumption, the resource allocation is more accurate, the waste of computing power resources is avoided, the response capability is improved, the load prediction is generated by using historical and real-time data, the computing power resources can be ensured to be fully utilized in the peak period of demand, overload is avoided, and the service life of the system is prolonged. Through intelligent analysis of the resource utilization rate and the predicted completion time and optimization of priority ranking of the tasks, the emergency tasks can be rapidly responded, implementation of a real-time computing power distribution strategy is achieved, high-efficiency computing power resource use is guaranteed, the stability and reliability of the system are improved, and the cost problem caused by fault response delay is reduced.

The invention relates to the technical field of foundation pit monitoring, in particular to a foundation pit monitoring system of a self-repairing wireless sensor network and a method thereof, which realize high-precision (+ / -0.5 mm) displacement monitoring by integrating a multi-source heterogeneous data acquisition module, a self-diagnosis processing module, a predictive fault management module, a network topology management module and an intelligent resource scheduling module. The system adopts multi-dimensional information fusion and preprocessing, the perception capability is improved, nodes and network states are comprehensively monitored through a hierarchical health degree evaluation system, a foundation is laid for fault prediction and active maintenance, the system can dynamically adjust a network topology structure, resource allocation is optimized by using a reinforcement learning algorithm, and the system has a wide application prospect. Node task allocation, communication power control and sampling frequency adjustment are effectively executed, and the efficiency and reliability of foundation pit monitoring are remarkably improved.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention relates to the technical field of automatic control, in particular to an automatic control method of a BOG low-temperature recovery system for a storage tank for an LNG (liquefied natural gas) ship, which comprises the following steps: acquiring BOG pressure fluctuation data and temperature difference trend, analyzing pressure rise rate and cooling efficiency change, identifying a pressure peak value and a cooling lag section, screening a low-efficiency section and marking an optimization unit. And identifying an energy matching degree, analyzing energy loss distribution, identifying an over-limit node and a fault signal, and obtaining an energy and fault synchronous control table. According to the system, through real-time data monitoring and intelligent analysis, the BOG recovery process can be accurately controlled, energy circulation and cooling treatment can be optimized, the efficiency of a low-temperature recovery system can be improved, BOG pressure and temperature difference changes can be monitored, pressure fluctuation and cooling lag can be analyzed, continuous improvement of condensation efficiency can be ensured, control parameters can be accurately adjusted, energy loss is avoided, and the energy efficiency of equipment can be improved; energy consumption and equipment fault management are optimized, and efficient and safe operation of the low-temperature recovery system is ensured.

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 discloses a high-performance satellite platform based on a real-time operating system and a wireless bus and a working method of the high-performance satellite platform, and belongs to satellite communication technologies, future deep space exploration and low-orbit satellite constellations. On the basis of an original structure of a satellite platform, a real-time operating system is effectively combined with a wireless bus system, and the real-time operating system comprises a kernel module, a communication module, an equipment driving module, a file system module, an application interface module, a safety module and a fault management module. The wireless bus system comprises a physical layer, a data link layer, a network layer, a transmission layer and an application layer. According to the invention, efficient and reliable communication and task execution capabilities are provided for a satellite platform, the requirements of real-time performance and reliability of a satellite in a complex environment are effectively met through modular design and tight work of the system, and meanwhile, complex lines and interfaces brought by a wired system are avoided.

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.

The present technology relates to fault-managed power (FMP), and particularly, to generating FMP in an industrial automation environment using single-pair Ethernet (SPE) cabling for use by DC motors. An FMP system may include transmitter circuitry and receiver circuits coupled together via a transmission link formed using SPE cable. The transmitter circuitry can generate a FMP and transmit a pulsed signal having the FMP to the receiver circuits. The transmitter circuitry can also exchange data signals with the receiver circuits over a communication channel using the SPE cable. The receiver circuits can identify an expected power consumption of DC motors coupled to the receiver circuits and provide an indication of the expected power consumption to the transmitter circuitry. The transmitter circuitry can detect a fault based on a comparison between the transmitted FMP and the power consumption and terminate transmission of the FMP in response to detecting the fault.

The invention discloses a transformer substation comprehensive monitoring management platform and a control method, and the method comprises the steps: collecting equipment operation data and environment parameters in a transformer substation in real time, and carrying out the preprocessing of the collected data; environmental parameters inside and around the transformer substation are monitored in real time, and the environmental parameters are adjusted to optimize equipment operation conditions; analyzing real-time data, identifying potential abnormity and automatically responding; according to the real-time data of the equipment, running an adaptive optimization algorithm to obtain an adjustment signal to optimize the running state of the equipment; comprehensively analyzing operation data and historical data of the transformer substation, and generating decision support suggestions and operation strategies; collecting equipment energy efficiency data in real time, analyzing the energy efficiency data through a dynamic energy efficiency optimization algorithm, and generating an energy efficiency optimization strategy; and combining positioning data of the RFID technology and the UWB technology, analyzing behavior patterns of personnel in the transformer substation, identifying potential safety hazards according to the behavior patterns of the personnel, and giving an alarm. According to the fault management mechanism, the reliability of the substation equipment is remarkably improved.

The invention relates to the technical field of unit detection, and discloses an intelligent fault self-diagnosis method for an evaporative condensation integrated screw water chilling unit, which comprises the following steps of: decoupling operation data of a compressor through a blind source separation algorithm, performing image processing on operation data of a condenser in combination with a morphological analysis method, and performing fault diagnosis on the operation data of the condenser; the operation characteristics of the compressor and the operation characteristics of the condenser can be extracted respectively, effective decoupling and clear expression of coupling signals are realized, and by constructing a dual-channel LSTM network comprising a gating fusion unit and introducing a cross-channel coupling gating mechanism, the system reliability is improved. The dynamic coupling relation between the compressor and the condenser can be modeled and regulated in the time dimension, the coupling mechanism between the compressor and the condenser is truly reflected, the time sequence parameter sequence is input into the pre-trained deep LSTM network, the fault types such as control lag and performance degradation can be accurately recognized, and the fault diagnosis accuracy is improved. Therefore, the accuracy and real-time performance of fault diagnosis are improved, and the requirement for intelligent fault management of the water chilling unit is met.

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 present invention relates to the fields of computer science and information technology, and specifically to a fault isolation and rapid recovery system based on a microservice architecture, which integrates automated fault detection and identification technology, intelligent grayscale release mechanism, fault isolation technology, rapid recovery strategy and elastic microservice design to provide efficient and reliable fault management and system recovery solutions. By analyzing the operating indicators and log data of microservices, using machine learning algorithms to detect abnormal behaviors, automatic fault detection is achieved; using containerization technology and Kubernetes clusters for intelligent grayscale release, and real-time monitoring of the application status of new versions; achieving fault isolation through service grids, and using Kubernetes automatic expansion strategies to increase the number of healthy Pods and quickly isolate faults; when a fault occurs, through the rollback operation in the CI / CD process, the above technical measures are used in combination to effectively improve the stability, flexibility and maintainability of the system, and are suitable for complex and changeable production environments.

A fault-managed power delivery system for delivering power across a distance with fault management is provided. The system includes a fault management circuitry including a reference resistor having a known resistance, a reference resistor switch, line switches configured to electrically connect or disconnect line conductors, and a controller. The reference resistor switch is configured to electrically connect or disconnect the reference resistor, wherein a duty cycle of switching of the reference resistor switch includes a fault detection portion. The controller is configured to control switching of the reference resistor switch and the line switches. The controller is further configured to determine an impedance of the power delivery system during the fault detection portion, determine a fault condition based on the impedance, and disconnect a line if the fault condition is detected. The fault management circuitry is positioned in the transmitter-side subsystem and / or the receiver-side subsystem.

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 discloses a baseboard management function fault emergency system and method, a server and a storage medium, and relates to the technical field of communication, the system comprises a management module, a switching module and a controller, when any function of the management module has a fault, the fault of any function of the management module is switched through a link of the switching module; the task of the fault function is redistributed to the device capable of replacing the fault management function to achieve the corresponding management function, timely replacement of the fault management function is achieved, the reliability of the system is improved, the management function is achieved through the device on the substrate, a redundant management module does not need to be added, the device of the substrate is used, and the cost is reduced. The hardware cost is effectively reduced, meanwhile, the number of the management modules is reduced, the analysis complexity can be reduced when the fault occurs, and the fault analysis and recovery efficiency is improved. Therefore, the problems that the cost is increased, resources are wasted and the maintenance complexity is increased due to the fact that double-BMC configuration is adopted to improve the running stability of the server in the related technology are solved.

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.

A method for reporting data to a Northbound Management System (NBMS) via a Network Function (NF) level Northbound FCAPS Reporting Microservice (MS) in an NF of a Radio Access Network (RAN), the method including receiving, by a first microservice (MS) from at least one other MS of the NF, data, the first MS being one of a Fault Management (FM) MS and a Performance Management (PM) MS and the data comprising performance data for the PM MS or event data for the FM MS; processing, by the first MS, the received data; obtaining, by the FCAPS Reporting MS, the processed data; and reporting, from the FCAPS Reporting MS to the NBMS, the processed data.

The invention provides an intelligent sensor abnormity management system based on a genetic algorithm, and the system optimizes the sensor position and parameter configuration through a genetic algorithm optimization module, and a central control and scheduling module sends an adjustment instruction to a sensor hardware module according to an optimization result, so as to adjust the layout and parameters of each sensor. Meanwhile, the sensor abnormity management module detects the abnormity of the sensor in real time and automatically selects an optimal adjustment strategy. Besides, the states and abnormal information of all the sensors can be displayed in a geographic information system through a GIS visualization module, so that layout is dynamically adjusted, abnormal points are rapidly positioned, the unmanned aerial vehicle can be matched, automatic repair is achieved, and reliability and maintenance efficiency are improved. Advanced technologies such as Internet of Things, edge computing, deep learning, GIS, genetic algorithm and reinforcement learning are fused, an intelligent and automatic sensor management and optimization platform is constructed, and efficient data acquisition, real-time optimization and fault management are achieved. By means of the modular design, the system has high expandability and flexibility and is suitable for various environment monitoring application scenes.

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 an equipment full life cycle management system and method, a storage medium and equipment. The system comprises an equipment full-life-cycle management module which is configured to be used for overall management of equipment information in a life cycle; the equipment monitoring module is configured to be used for monitoring the equipment state according to the equipment real-time data and triggering an alarm based on a preset early warning condition; the equipment detection module is configured to be used for detecting and maintaining equipment based on a preset period; the fault management module is configured to receive and respond to the fault report from the equipment monitoring module; and the health management module is configured to perform preventive maintenance and health assessment on the equipment and predict potential fault risks. According to the equipment full-life-cycle management system, the reliability and the management efficiency of the equipment are improved.

The present technology relates to fault-managed power, and particularly, to generating fault-managed power in an industrial automation environment using single-pair Ethernet cabling. A fault-managed power system may include transmitter circuitry and receiver circuitry coupled together via a transmission link formed using single-pair Ethernet cable. The transmitter circuitry can generate a fault-managed power based on power from a power source and transmit an unpulsed signal, including the fault-managed power, to the receiver circuitry via the transmission link. The receiver circuitry can identify an expected power consumption of one or more loads coupled to the receiver circuitry and provide an indication of the expected power consumption to the transmitter circuitry. The transmitter circuitry can detect a fault based on a comparison between the transmitted fault-managed power and the power consumption and terminate transmission of the fault-managed power in response to detecting the fault.

Techniques are described for monitoring network performance and managing network faults in a computer network. A cloud-based network management system stores path data received from a plurality of network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices for one or more logical paths of a physical interface from the network device over a wide area network (WAN). The network management system determines, based on the path data, one or more WAN link health assessments, wherein the one or more WAN link health assessments include a success or failure state associated with one or more of service provider reachability, physical interface operation, or logical path performance; and in response to determining the at least one failure state, outputs a notification including identification of a root cause of the at least one failure state.

The present technology relates to fault-managed power (FMP), and particularly, to generating FMP in an industrial automation environment using single-pair Ethernet (SPE) cabling for use by DC motors. An FMP system may include a transmitter circuit and receiver circuits coupled together via a transmission link formed using SPE cable. The transmitter circuit can generate a FMP and transmit a pulsed signal having the FMP to the receiver circuits. The transmitter circuitry can also exchange data signals with the receiver circuits over a communication channel using the SPE cable. The receiver circuits can identify an expected power consumption of DC motors coupled to the receiver circuits and provide an indication of the expected power consumption to the transmitter circuit. The transmitter circuit can detect a fault based on a comparison between the transmitted FMP and the power consumption and terminate transmission of the FMP in response to detecting the fault.

The invention discloses a memory fault management system and method, a server and electronic equipment, and relates to the technical field of computers, the memory fault management system comprises a processing circuit of a hardware memory, a hardware layer and a processor are arranged on the processing circuit, and the processor is further divided into a kernel layer, a user layer and an input and output layer. Through cooperative work of the hardware layer and each software layer, hierarchical detection, classified processing and automatic isolation of memory error data are realized, and server downtime caused by memory fault error data is effectively prevented. And meanwhile, through a linkage mechanism of a kernel mode and a user mode and in combination with visual display, the monitorability and maintainability of memory errors are enhanced, operation and maintenance personnel can quickly position and repair problems conveniently, and the operation and maintenance cost of the system is reduced.

An enhanced lighting control system is provided that utilizes updated communications technology to provide a cost and resource efficient lighting control system that allows for a retrofit installation into existing lighting systems. The lighting control system utilizes single pair ethernet connection protocols, as well as a pulsed power source, to enable the efficiencies.

The invention discloses a shield tunneling machine fault diagnosis method and system based on a self-attention mechanism, and the method comprises the following steps: obtaining multi-dimensional sensor data of a shield tunneling machine, and carrying out the data preprocessing, and obtaining a plurality of groups of coded data; calculating the sliding window variance of each group of coded data, and adjusting the confidence coefficient weight of the sensor; calculating intra-group self-attention of each group of coded data to obtain intra-group self-attention output; splicing the in-group self-attention output of each group, and performing inter-group self-attention calculation to obtain inter-group self-attention output; and performing fault classification and confidence evaluation on the inter-group self-attention output to obtain fault type output and fault confidence output. According to the method, through data grouping, a self-attention mechanism, sensor confidence adjustment, sliding window variance calculation and the like, efficient processing and real-time fault detection and classification of multi-dimensional sensor data are realized, so that the fault management precision and efficiency in the tunneling process of the shield tunneling machine are improved.