878 results about "Fault analysis" patented technology

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The embodiment of the invention provides an urban rail signal system fault diagnosis method based on a knowledge graph and a large model, and the method comprises the steps: collecting system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and standardization; based on historical fault data, a fault classification and prediction model is constructed through feature extraction and mode recognition, and automatic fault recognition and risk early warning are achieved; constructing a fault diagnosis knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large language model and a LoRA technology are adopted for efficient fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the knowledge graph and large model output are fused, real-time and historical data are combined, multi-path fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and technical support is provided for intelligent operation and maintenance management.

The invention belongs to the technical field of network communication, and particularly relates to an operation and maintenance network fault solving system and method.The operation and maintenance network fault solving method comprises the steps that a data collection module collects multi-source original network data of target network equipment and preprocesses the multi-source original network data to obtain multi-source standard network data; a semantic reconstruction module identifies non-business purpose fields in the multi-source standard network data and performs semantic reconstruction processing on the non-business purpose fields to obtain a target state set; the fault diagnosis module carries out fault analysis on the target state set and determines a fault type and a root position; and the execution module generates and executes a predictive isolation strategy according to the fault type and the root position, records a fault processing process and updates a dynamic coding rule and a diagnosis weight distribution strategy. According to the invention, the problems of low efficiency and insufficient accuracy caused by dependence on artificial experience and rule analysis in a network fault diagnosis method can be solved.

The invention relates to the technical field of power distribution network fault processing, in particular to a power distribution network fault automatic reconstruction control method of a power system. The method comprises the following steps: collecting operation state data of the power distribution network; dividing the power distribution network operation state data into power distribution network operation fault data and power distribution network operation state candidate data to be detected; performing disturbance interference potential state simulation analysis on the to-be-detected candidate data of the operation state of the power distribution network to generate disturbance interference potential state simulation data of the power distribution network; performing disturbance fault analysis through the power distribution network disturbance potential state simulation data to generate power distribution network disturbance fault data; based on the power distribution network operation fault data and the power distribution network disturbance fault data, intelligent reconstruction control decision design is carried out, and power distribution network intelligent reconstruction control decision data is generated; and power distribution network fault automatic reconstruction control operation is executed through the power distribution network intelligent reconstruction control decision data. According to the invention, efficient automatic reconstruction control is realized when the power distribution network fails.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention relates to the technical field of task scheduling management, in particular to an intelligent scheduling management method for water conservancy and hydropower engineering detection tasks, which comprises the following steps of: acquiring active power data of a unit, smoothing, differentially generating a rate and an acceleration sequence to construct a load characteristic matrix, comparing the load characteristic matrix with a steady-state threshold to generate a quasi-steady-state interval, and calculating a quasi-steady-state interval; calculating a load instruction deviation to generate a steady-state confirmation identifier, calculating a predicted steady-state window based on the identifier, if the window is longer than the minimum sampling duration, generating a trigger acquisition signal, starting vibration acquisition to generate an original waveform, and uploading the original waveform. According to the method, the load characteristic matrix is constructed through power data differential operation, load instruction deviation dual verification is combined to recognize the steady-state time period, the steady-state window length is pre-judged, and collection is triggered when the requirement is met, so that noise interference introduced by working condition fluctuation is effectively avoided, and it is ensured that original vibration waveform data originates from a stable working condition; and the data sample purity and the fault analysis value are obviously improved.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention discloses an engine fault diagnosis method, device and equipment and a storage medium, and relates to the technical field of fault diagnosis. The method comprises the following steps: synchronously acquiring a sound signal and a vibration signal of an engine in a normal operation state, and performing feature extraction on the sound signal and the vibration signal to obtain corresponding target features; the target features comprise time domain features, frequency domain features and time-frequency domain features; determining a statistical index of each target feature through statistical characteristic analysis, and performing feature fusion on all the target features according to a target fusion mode based on the statistical indexes to obtain a fusion index; and an early warning threshold value is determined according to the data statistical characteristics of the fusion index, so that fault monitoring is carried out on the engine by using the early warning threshold value. Fault diagnosis is carried out according to the early warning threshold value determined according to the sound vibration data of the engine in the normal operation state, high dependence on fault samples is avoided, and the comprehensiveness of fault analysis is improved.

The invention provides an aircraft system key parameter anomaly detection method and system based on an auto-encoder, and belongs to the field of aviation fault analysis. The method comprises the following steps: S1, preprocessing key parameters of an aircraft system, and dividing the key parameters into a training set, a verification set and a test set; s2, constructing abnormal data for simulating different forms of key parameter anomalies of the aircraft system; s3, implanting a plurality of anomalies; s4, constructing a composite loss function and training the auto-encoder; and S5, determining an exception threshold and performing exception detection, adaptively determining an exception judgment threshold based on a reconstruction residual statistical result of the verification set sample, and performing exception detection on the test set sample according to the exception judgment threshold. According to the method, the accuracy and robustness of unknown anomaly detection can be remarkably improved, the false alarm rate caused by noise and slight disturbance is reduced, and the method is suitable for high-dimensional aircraft system key parameter monitoring and anomaly early warning of aircraft environment control, flight control, hydraulic and other key systems.

The invention relates to the field of machine learning, particularly discloses an artificial intelligence-based power system fault analysis and diagnosis method, and effectively solves the problem of information loss caused by neglecting a key waveform form in a transient signal in the prior art through a local feature extraction and serialization module. An original signal is converted into a local feature sequence with more characterization significance. Aiming at the averaging bottleneck of an existing model in an information aggregation stage, a traditional feature compression method is abandoned, and a sequence information aggregation and decision-making mechanism is provided. According to the mechanism, a context sensing sequence is regarded as a probability event, and modeling is carried out on the sequence from three orthogonal dimensions of a content center, time sequence dispersion and distribution uncertainty by calculating feature expectation, time sequence variance and information entropy of the context sensing sequence. The method can deeply insight and quantify the essential difference of different events in the time sequence dynamic evolution mode, thereby fundamentally solving the problem of misjudgment caused by feature confusion.

The invention discloses a voltage transformer state influence factor analysis method, system and device and a medium, and belongs to the technical field of voltage fault analysis, and the method comprises the steps: collecting state parameters of a voltage transformer, carrying out the preprocessing of the state parameters, and constructing a nonlinear coupling relation between multidimensional feature tensor capture features; analyzing voltage fluctuation characteristics of the voltage transformer, and automatically correcting the insulation state evaluation model; and inputting abnormal feature vectors obtained by monitoring into a recursive attribution decision tree model, carrying out multi-dimensional abnormal clustering and causal analysis, generating a tracing report, and carrying out multi-dimensional evaluation and verification on an analysis result. According to the invention, continuous dynamic full-coverage monitoring of the equipment insulation health condition is realized, the accuracy and robustness of anomaly detection are improved, the phenomena of missing detection and false alarm are reduced, the scientificity of anomaly tracing analysis and the interpretation of early warning decision are improved, and the reliability of the system is improved. And the intelligent level of the monitoring and early warning system is improved by continuously adapting to new abnormal types and complex operation environments.

The invention provides a system-level fault analyzing and tracing method and system based on multi-layer causal diagram extraction, and belongs to the technical field of fault diagnosis. Using a multi-level convolutional neural network to convert the time sequence monitoring data features into a feature matrix; by introducing a hierarchical adjacency pruning algorithm and an elastic network regularization constraint, sparse modeling of a multi-level causal matrix is realized, and a causal matrix graph, namely a prediction contribution matrix graph, is obtained; according to a proposed score quantization algorithm, direct propagation and indirect propagation effects are comprehensively considered, prediction information provided by each variable is quantified, a reason score is provided, and a fault reason variable is determined. According to the method, multi-dimensional feature information of the system-level fault can be compared, all useful information is fully utilized, the contribution degree of the system variable fault is accurately evaluated, and the high-level fault reason detection rate is obtained.

The invention discloses a transformer monitoring method and system based on a multi-dimensional signal, and relates to the technical field of transformer monitoring, and the method comprises the steps: obtaining the multi-dimensional signal and working condition parameters of a transformer, carrying out the time domain feature extraction of the multi-dimensional signal, obtaining a corresponding time domain correlation feature, carrying out the frequency domain feature extraction of the multi-dimensional signal, obtaining a corresponding frequency domain correlation feature, and carrying out the time domain feature extraction of the multi-dimensional signal; fault analysis is carried out according to the working condition parameters and the frequency domain correlation characteristics, and a corresponding initial fault detection result and an electromagnetic coupling residual value are obtained; and if so, carrying out fuzzy evaluation on the time domain correlation feature, the frequency domain correlation feature, the electromagnetic coupling residual value and the multi-dimensional signal based on a pre-trained fault detection model to obtain a corresponding fault monitoring result. The technical problems that a traditional transformer monitoring method mainly depends on electrical signal analysis, but key parameters such as oil temperature are not included in a monitoring system, so that potential fault hidden dangers cannot be found in time, and the operation reliability of the transformer is reduced are solved.

The invention discloses a series-parallel system transient stability analysis and fault ride-through control method considering current limiting, and belongs to the field of power fault analysis. In order to reveal an action mechanism of a current amplitude limiting and converter coupling effect on the transient synchronization stability of the hybrid system, the method comprises the following steps: firstly, considering the influence of control mode switching of a constructed network type converter, and establishing a transient analysis model of the hybrid system considering a current amplitude limiting link; then, based on a phase plane method, analyzing an action rule of converter control parameters on system transient stability, determining an optimal active power reference value and a saturation current phase angle of the grid-forming converter during a fault period, and proposing a fault ride-through control strategy according to the optimal active power reference value and the saturation current phase angle; and finally, verifying the correctness of theoretical analysis and the effectiveness of the proposed control strategy through multi-working-condition simulation.

The invention discloses a thinking chain-based power distribution and utilization fault research, judgment and analysis method and system. The method comprises the following steps: acquiring a user demand, analyzing the user demand, and matching in a preset power distribution and utilization business process template library; designing a specific task execution path and a subtask sequence, and calling a pre-constructed model library and a knowledge library to execute corresponding tasks so as to obtain an execution result; wherein multi-modal knowledge in the field of power distribution and utilization operation analysis is stored in the knowledge base, and index construction and retrieval of the multi-modal knowledge are realized; models for realizing fault research and judgment and fault analysis based on a plurality of business scenes in the field of power distribution and utilization operation analysis are stored in the model library; and integrating and forming explainable and traceable power distribution and utilization operation analysis output results based on execution results. According to the method, an advanced artificial intelligence method is introduced into the service of the power distribution and utilization field, and meanwhile power distribution and utilization fault research, judgment and analysis which are easy to use and high in interpretation are achieved.

The invention discloses an electric power system fault protection system and method based on multi-modal data fusion, and relates to the technical field of electric power systems.The method comprises the steps that an electric power equipment fault analysis model is constructed, and historical electric power fault data is collected to train the electric power equipment fault analysis model; inputting the electrical measurement abnormal condition, the equipment state abnormal condition and the environment meteorological abnormal condition into the power equipment fault analysis model to judge abnormal equipment, positioning a fault point of the abnormal equipment, analyzing the abnormal condition of a module corresponding to the fault point based on the electrical measurement data of the fault point of the abnormal equipment, and determining the abnormal condition of the module. Meanwhile, the influence degree of equipment outage caused by the fault of the fault point is evaluated, the equipment fault severity degree is analyzed based on the abnormal condition of the module corresponding to the fault point and the influence degree of equipment outage caused by the fault of the fault point, and an equipment first-aid repair sequence is generated based on the equipment fault severity degree; the accuracy of power failure degree judgment and the emergency response capability of a power system are improved.

The invention relates to the technical field of line fault analysis, in particular to a high-voltage line fault identification method based on phasor analysis, which comprises the following steps of: performing multi-time window nesting decomposition on voltage phasors and current phasors acquired in the operation process of a high-voltage line, extracting a phasor dynamic response sequence, and calculating the phasor dynamic response sequence; identifying a main disturbance mechanism characteristic triggered by a fault, and constructing a main disturbance mechanism type label; a driving response dominant phasor disturbance factor is identified, a phasor factor mapping graph is constructed, factor stripping operation is executed, and a dominant purification phasor set is generated; and performing phase evolution analysis on the dominant purification phasor set, and determining a fault initial phase transition boundary and a cross-phase offset trend. According to the method, the stability and the physical consistency of identification are ensured, and the finally output fault type and phase structured result not only has clear logic traceability, but also highly meets the requirements of dispatching automation and quick response of a protection system.

The invention discloses a water-cooled permanent magnet coupler heat dissipation fault diagnosis method based on a neural network, relates to the field of coupler heat dissipation fault diagnosis, and obtains a three-dimensional temperature field with a fault tag and a fault sensitive tensor through multi-physical field coupling and fault tag creation analysis based on an initial three-dimensional temperature distribution field and discrete electromagnetic field data. And performing multi-field fusion and space-time compression on the three-dimensional temperature field with the fault tag and the fault sensitive tensor to obtain a feature vector of concentrated fault key information, and performing physical constraint dimension reduction, fault prototype learning and parameter inversion on the three-dimensional temperature field with the fault tag and the feature vector of the concentrated fault key information to obtain fault parameter estimation. Bidirectional recursive fault analysis is performed based on fault parameter estimation to obtain a propagation state, gradient analysis and adversarial diagnosis are performed on the propagation state to obtain a fault type corresponding to the maximum probability, and misjudgment under complex working conditions can be greatly reduced.

The invention discloses an ultra-high voltage direct current transmission system fault intelligent analysis method and system based on a domestic artificial intelligence large model framework, and a storage medium, and belongs to the field of electric power system artificial intelligence fault diagnosis. According to the method, multi-source heterogeneous fault data are accessed and standardized and aligned, the processed data are input into a domestic artificial intelligence large model adapted by knowledge in the electric power field, and the model is utilized to perform multi-modal feature fusion and intelligent analysis, so that automatic identification and accurate positioning of fault types are realized; and an analysis report and a disposal strategy are automatically generated. The system comprises a data input module, an intelligent analysis module and a decision output module, and the intelligent analysis module performs feature extraction and recognition by adopting a CNN-Transform hybrid neural network and realizes fault positioning based on a knowledge graph and a graph neural network. According to the method, full-process automatic intelligent analysis from data access to decision output is realized, and the efficiency, the accuracy and the engineering landing capability of fault diagnosis are remarkably improved.

The invention discloses an energy storage power station operation and maintenance management system based on big data, and relates to the technical field of power station operation and maintenance management, and the system comprises an operation and maintenance platform which is in communication connection with a multi-source data processing module, a health state evaluation module, a fault analysis module and an optimization module. The multi-source data processing module is used for collecting energy storage associated data of a plurality of dimension types in the energy storage power station, allocating a time sequence operation window for the energy storage associated data of each dimension type for time sequence allocation association, performing feature quantization on the energy storage associated data under each time sequence to obtain a time sequence feature coefficient under the corresponding time sequence, and outputting the time sequence feature coefficient to the energy storage power station; summarizing into a time sequence feature set; the health state evaluation module is used for evaluating the energy storage state of the energy storage power station according to the time sequence feature set; the fault analysis module is used for carrying out fault analysis on the energy storage power stations of which the energy storage states do not reach the standard to obtain corresponding energy storage fault detail information; and the optimization module is used for establishing a multi-target constraint optimization model and completing energy storage optimization of the energy storage power station.

The invention relates to the technical field of monitoring video processing, and discloses a coal mine shaft fault monitoring method and system. Comprising the steps of collecting video streams of key positions in a coal mine shaft in real time; splicing the original image frames in the video stream of each key position according to a space-time relationship to obtain a panoramic monitoring picture at each moment; segmenting the panoramic monitoring picture at each moment according to the target monitoring object to obtain a segmented image frame corresponding to each target monitoring object; performing fault analysis on the segmented image frames corresponding to the target monitoring objects to obtain monitoring results of the target monitoring objects; and obtaining a monitoring result of the coal mine shaft based on the monitoring results of all the target monitoring objects. The comprehensive monitoring result of the whole coal mine shaft can be automatically generated, intelligent, precise and efficient monitoring of underground potential safety hazards is achieved, and the safety of the monitoring process can be remarkably improved.

The invention provides a risk propagation analysis method and system based on power grid fault chain evolution simulation, and relates to the technical field of power grid fault analysis, and the method comprises the steps: constructing a power grid digital twinborn model, injecting an initial fault event into the power grid digital twinborn model, building an event propagation chain node set, and building a cross-layer fault triggering relation. According to a cross-layer fault triggering relation, risk coupling transmission analysis of an event propagation chain node set is carried out, an event propagation chain is established, evolution simulation of the event propagation chain is executed, a time sequence fault propagation atlas is generated, a propagation risk degree is calculated in combination with node consequence weights, a risk degree identifier of a fault chain is established, and risk propagation early warning is generated. The technical problem that dynamic propagation characteristics of power grid faults are difficult to comprehensively and accurately reflect in the prior art is solved. The technical effects of comprehensively and accurately reflecting the dynamic propagation characteristics of the power grid fault and improving the timeliness of fault diagnosis and the accuracy of risk assessment are achieved.

The application provides a CPU platform fault log recording method and device, comprising: receiving the running log data stream output by the CPU debugging serial port in real time; collecting the sampling value of the key power supply voltage and monitoring the hardware signal line to obtain the restart event signal and the shutdown event signal; packaging into a structured log recording unit; and writing the compressed log data block back to the flash memory; in response to the external reading instruction, reading the log data block from the specified log area of the flash memory and transmitting to the host end diagnostic tool; reorganizing and displaying in time sequence for fault analysis. The application can construct a log recording channel completely independent of the main CPU and operating system without increasing any additional hardware cost, ensuring that even in the most extreme system failure scenario, the complete context information at the fault moment can be reliably saved, thereby greatly improving the diagnosis efficiency and accuracy of the occasional fault.

This invention provides a comprehensive digital and intelligent operation and maintenance management method and system for power distribution networks, relating to the field of computer data processing technology. The method includes: S1, obtaining pole and tower information collected by drones during line inspections, performing defect mining and analysis on the pole and tower information, and integrating the analyzed defect information with the pole and tower information to form a pole and tower information collection table; S2, selecting and extracting corresponding data from the pole and tower information collection table, and visualizing the data on a GIS map containing the lines obtained from the pole and tower information collection table using preset markers to generate an inspection status map; S3, statistically classifying the analyzed defect information and matching it through a big data platform to generate a defect repair and management summary table; S4, analyzing the faults using an AI model and a big data platform, combining the inspection status map and the defect repair and management summary table, obtaining priority-ranked repair tasks, and proposing corresponding solutions.

The invention is suitable for the technical field of electric power communication, and provides an electric power communication transmission network fault self-repairing processing method, which comprises the following steps: carrying out fault analysis based on an identified alarm type, starting a self-repairing processing flow based on a generated fault detection result, extracting attribute information of an affected service to distinguish service priorities, and carrying out self-repairing processing according to the service priorities. The N-1 redundancy capability of the service is verified; if a verification result needs to start a temporary channel, executing resource verification, and migrating service flow to a temporary circuit or triggering protection channel switching after verification is passed; after the fault of the original optical path is repaired, performance acquisition and service flow test are carried out on the repaired original optical path; if the test is passed, the service flow is migrated back to the original optical path in a double-channel parallel mode, and resources occupied by the temporary channel are released; after the temporary circuit is released, generating a fault repair file, and archiving the performance data to generate an optimization suggestion; and the fault processing efficiency of the transmission network, the reliability of the network and the self-healing level are improved.

The invention discloses an intelligent operation and maintenance alarm causal relationship analysis method based on BERT. According to the method, the semantic similarity of core switch link interruption and access layer equipment offline is improved by 40% through semantic modeling upgrading and joint coding of equipment types and alarm contents, and compared with an independent coding scheme, the misjudgment rate is remarkably reduced. Through organic combination of deep semantic modeling and dynamic time sequence analysis, the crossing of the intelligent operation and maintenance alarm causal relationship from rule driving to data intelligent driving is realized, and an efficient analysis tool is provided for intelligent operation and maintenance of a complex system. According to the method, accurate causal modeling of complex system alarm data is realized through semantic similarity calculation and multi-dimensional weight dynamic fusion under time sequence constraint, and the method is widely applied to large-scale distributed system fault analysis and root cause positioning scenes in industries such as power systems, communication networks, internet platforms, cloud computing infrastructures and industrial Internet of Things.

The invention discloses an electric power system fault diagnosis method and system based on multi-sensor fusion, and relates to the technical field of electric power fault diagnosis, electric power data of an electric power system are collected based on multi-array sensors, and a health data set of the electric power system is generated by adjusting element parameters of the electric power system; constructing an electric power digital twinborn model of the electric power system by using the element parameters and the health data, and inserting a distributed fault analysis unit into the electric power digital twinborn model to generate a parallel fault analysis model; real-time electric power data of the electric power system are collected in real time, when a fault trigger signal is detected, a driving data flow is generated, the driving data flow is introduced into the parallel fault analysis model to position a fault source, and a fault report is output; according to the method, the high-fidelity digital twinborn model and a parallel fault analysis mechanism are constructed, so that rapid, accurate and automatic positioning and diagnosis of the faults of the power system are realized.

The invention discloses a fault analysis system and method for ship equipment, and the system comprises a multi-source sensing synchronization module which is configured to carry out the phase synchronization of the operation data of multiple equipment through a hardware phase locking mechanism; the multi-physical field modeling module is coupled to the multi-source sensing synchronization module, and is configured to construct a device virtual state field based on the multi-device operation data after phase synchronization, and generate a dynamic parameter representing a multi-device state time sequence imbalance degree; the adaptive diagnostic analysis module comprises a deep learning fault analysis model, and recessive feature extraction parameters and fault evolution time sequence modeling parameters of the deep learning fault analysis model are adaptively adjusted in real time according to dynamic parameters so as to output system-level fault characterization parameters; and the predictive decision-making module is configured to generate decision-making information containing fault development trend prediction and a preventive maintenance window based on the system-level fault characterization parameters. And high-precision correlation diagnosis and predictive maintenance decision-making of early faults of multiple devices of the ship can be realized.