511 results about "Fault management" patented technology

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

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 discloses an aeronautical manufacturing field fault knowledge graph modeling method based on an OPC UA protocol and an improved CASREL model. The aeronautical manufacturing field fault knowledge graph modeling method specifically comprises the following steps that a sensor monitors and collects maintenance data; multi-source heterogeneous data are collected through an OPC UA data transmission protocol and are refined and stored according to types; preprocessing the data; carrying out data set division and model pre-training based on RoBERTa; performing triple extraction based on an improved CasRel model; constructing a knowledge graph; key entities are extracted through a BiLSTM-CRF model, and a second-step sub-graph of the knowledge graph is generated; performing knowledge question answering and reasoning based on a large language model, analyzing fault causes and generating a maintenance strategy; an online crawling mechanism is integrated, and improvement, optimization and self-learning of the knowledge base are achieved; the method can be widely applied to the fields of aviation equipment maintenance, fault processing and related decision support, the intelligent level and efficiency of fault management can be remarkably improved, and accurate and intelligent support is provided for fault diagnosis and maintenance decision of aviation equipment.

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 relates to the field of electric energy quality monitoring and diagnosis, in particular to a grid-connected point electric energy quality on-line monitoring and automatic diagnosis method. The method comprises the following steps: calculating effective values and power parameters of voltage and current, extracting the amplitude and phase of each harmonic, and calculating total harmonic distortion; solving a coupling electromagnetic field equation by using an electromagnetic field coupling effect model; based on a time-space evolution model and in combination with an electromagnetic field reflection coefficient, using an anomaly detection function to locate occurrence time and position of a problem; and constructing a multi-scale electromagnetic reverse analysis model, and constructing a fault diagnosis function by combining a dynamic environment compensation model and introducing an electromagnetic field reflection coefficient and frequency deviation. According to the grid-connected point electric energy quality online monitoring and automatic diagnosis method, through integrating a time-space evolution model, a multi-scale electromagnetic reverse analysis model, an anomaly detection function and a dynamic environment compensation model, accurate control and efficient fault management of a power grid operation state are realized.

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 relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

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.

Embodiments described herein provide methods and apparatuses for selecting Nnext Fault Management Models, FMMs, where Nnext is an integer value, to run at an edge site in a cloud system during an ith time period. A method in a Fault Management System, FMS includes: in a deployment agent, DA: selecting the Nnext FMMs from a first set of FMMs based at least in part on respective probability functions associated with each of the first set of FMMs, wherein each respective probability function indicates how likely a fault associated with the respective FMM is to occur as a function of time; and initiating running of the Nnext FMMs at the edge site during the ith time period.

The invention discloses a power distribution network fault management system and method with a rapid positioning function, and relates to the technical field of power distribution network management, and the system comprises a data collection module, a data analysis and processing module, a fault positioning module and a display and decision module. According to the power distribution network fault management system and method capable of achieving rapid positioning, the power distribution network fault management system is arranged, positioning errors caused by inaccurate data are reduced by means of high precision of a sensor and data preprocessing measures, multiple times of fault positioning are completed, the fault generation area is determined preferentially, and the fault management efficiency is improved. And then the node is combined to realize the specific judgment of the fault generation position, so that the exact positioning operation of the fault can be quickly completed, the interference of external factors is eliminated, the positioning accuracy is effectively improved, and meanwhile, the omnibearing management of each region of the power distribution network is facilitated.

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 fault managed power system (FMPS) and method monitors and detects fault currents in PoE, PFC, and other cables that indicate likely human contact with cable conductors. The level of current detected through the human body combined with a fast response time limits the energy to prevent a person from experiencing ventricular fibrillation, resulting in a so-called touch-safe level. For overload and short-circuit fault protection, the system automatically and immediately removes power from the cables. This limits the amount of energy provided into the fault, thereby maintaining touch-safe operation and also preventing electrical fires and system component protection. The system / method can accomplish this even at voltage levels considerably higher than existing touch-safe standards, for example. Class 2 (below 50 Vac) power supplies. Such a system / method allows the amount of power in applications like PoE and PFC to be safely increased to levels much greater than the current maximum (100 W).

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.

The invention discloses an intelligent cloud problem fault analysis method and system, and relates to the technical field of cloud computing, and the method comprises the steps: extracting data features, and carrying out the first extraction of fault features; carrying out fault prediction and positioning; and establishing a first alarm mechanism, creating a fault management knowledge base, and providing an automatic processing suggestion. According to the method, deep analysis and processing of fault data are achieved by extracting fault features, the accuracy and efficiency of fault prediction and positioning are improved, guarantee is provided for the stability and reliability of a cloud system, efficient recognition and processing of faults are achieved through fault prediction and positioning, and the fault prediction and positioning efficiency is improved. According to the invention, the method improves the accuracy and efficiency of fault processing, guarantees the stability and reliability of the cloud system, achieves the quick response and effective processing of faults through a fault alarm mechanism and a fault management knowledge base, and improves the operation and maintenance efficiency and the stability and reliability of the cloud system.

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.

The invention discloses a wireless network topology visualization and fault analysis system, and the system comprises a networking state sensing module which is used for collecting the communication state data of various types of wireless transmission equipment in a network environment; the network link visualization module is used for analyzing the data acquired by the networking state sensing module, completing statistics of equipment traffic in a link and completing visual display of an equipment topological graph and traffic use conditions; the network fault management module is used for dynamically monitoring a network node communication state to identify a network fault node and giving an alarm; and the network security visualization module is used for monitoring a network security state in real time, supporting custom configuration of a firewall, and performing alarm and log storage on network security abnormity. According to the method, the connection relation and the topological structure of the equipment in the network can be visually displayed, the fault source can be rapidly positioned, and capacity planning and flow optimization can be carried out.

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.