249 results about "Fault probability" patented technology

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention relates to a fault diagnosis method for a server. Efficient fault positioning and processing are achieved by constructing a multi-dimensional intelligent diagnosis system. Constructing a server virtual model at each node and establishing a connection relationship, and collecting various data and mapping the data to the virtual model; performing weighted fusion on the data through correlation analysis, and extracting fault features including time and space correlation by using a bidirectional long-short-term memory network; fault probability distribution is obtained by means of a dynamic fault knowledge graph and graph neural network reasoning, and a fault evaluation result is visualized in combination with a virtual model; and finally analyzing the behavior deviation through a neural network and generating a processing strategy. Real-time processing of multi-source data, intelligent extraction of fault features and dynamic deduction of fault propagation are achieved, the accuracy, the real-time performance and the automation level of server fault diagnosis are effectively improved, and predictive maintenance and intelligent decision making of a complex server system are achieved.

The invention belongs to the technical field of fault analysis and monitoring, and particularly relates to an intelligent monitoring and fault isolation method and system for a branch box, and the method comprises the steps: carrying out the multi-physical field data fusion collection of the branch box, and obtaining a spatial-temporal feature matrix; performing dynamic feature extraction on the multi-physical field data of the branch box, and outputting a fused dynamic feature vector of the branch box; obtaining a branch box topological graph structure, and obtaining a branch box node fault probability matrix; building a physically constrained fault propagation model based on the branch box node fault probability matrix, obtaining a pre-isolation set, and outputting a dynamic blocking instruction; taking the pre-isolation set as an initial solution space constraint, outputting a fault type and obtaining an isolation instruction; and setting a branch box topology reconstruction constraint, and performing closed-loop control on each branch box node in combination with the isolation instruction to obtain a switching action sequence of each branch box node and a reconstructed power supply path. Precise monitoring, rapid isolation and intelligent reconstruction of the fault of the branch box can be realized, and the operation and maintenance efficiency of the power distribution network is remarkably improved.

The invention discloses a fault tracing and positioning method in an FTU section, and belongs to the technical field of distribution automation fault positioning. The method comprises the following steps: synchronously acquiring current abrupt change signals of multiple FTU sections, reconstructing a transient waveform through EMD decomposition and cubic spline interpolation, extracting wavelet packet energy characteristics, and generating multi-dimensional transient characteristics in combination with wave head polarity and timestamps; fusing the power distribution network topology and traveling wave time delay to construct a space-time correlation graph, introducing virtual nodes to compensate communication interruption, dynamically assigning node attributes and marking a reflection path; based on graph neural network cooperative training, iteratively aggregating neighborhood information and dynamically optimizing edge weights, and generating candidate section fault probability distribution; and judging a conflict level by using information entropy, carrying out multi-level digestion in combination with polarity matching and time delay consistency, and outputting a high-confidence positioning result. According to the method, the problems of difficulty in multi-FTU cooperative positioning, poor communication interruption adaptability, inaccurate feature fusion and the like are solved, and the accuracy and robustness of power distribution network fault tracing are remarkably improved.

The invention discloses a power equipment fault prediction system based on big data analysis. The method comprises the following steps: acquiring initial equipment multi-dimensional data; constructing a dynamic topology network of the power equipment, including a dependency relationship between the equipment and a fault propagation path, performing embedded learning on the dynamic topology network by using a GNN graph neural network, and extracting equipment collaboration features in the initial equipment multi-dimensional data; a multi-task learning framework is constructed in combination with the equipment cooperation features to predict the equipment fault probability and the remaining service life, and an equipment health index is obtained; and acquiring environmental parameters, dynamically adjusting a fault judgment threshold based on the equipment health index and the environmental parameters, generating a prediction result, integrating the prediction result with an SCADA system, and triggering graded early warning. And the influence of environmental factors on the operation state of the equipment is fully considered. Under different environmental conditions, the equipment fault risk can be judged timely and accurately.

The invention discloses a fault detection method and device and electronic equipment. The method comprises the steps that data information generated in the data processing process is acquired, multi-dimensional performance index data corresponding to the data information is determined, and the performance index data is multi-dimensional time sequence data containing timestamps; analyzing the performance index data through a long short-term memory network model to obtain a performance index predicted value; an anomaly detection result is determined according to the performance index predicted value and a preset threshold value, the preset threshold value comprises a plurality of performance index threshold values corresponding to the performance index data, and the anomaly detection result is used for reflecting the fault probability and the fault type of the data processing system; and determining a fault reason of the data processing system according to an abnormal detection result. According to the method and the device, the technical problems of relatively low anomaly detection precision and relatively poor fault positioning efficiency due to the fact that a fault detection method in related technologies mostly depends on manual monitoring and manual intervention are solved.

The invention discloses an electric energy meter detection assembly line fault diagnosis and prediction method based on multi-modal time sequence analysis. The method comprises the steps of collecting multi-modal data including electric energy meter visual data, time sequence sensor data and text log data in real time; performing cross-modal fusion of time sequence alignment on the multi-modal data to generate joint feature representation; performing joint optimization of fault diagnosis and prediction; performing fault diagnosis based on the joint feature representation, and outputting a current fault type and probability; predicting a future fault probability based on the equipment state continuous evolution model; in response to batch conduction characteristics which are output by the prediction model and reach a preset abnormal value, triggering recalculation of the associated modal data; correcting an initial condition of the equipment state continuous evolution model according to the fault type obtained through re-calculation; and dynamically adjusting a diagnosis decision threshold according to the prediction confidence output by the corrected equipment state continuous evolution model.

The invention discloses an elevator fault prediction method and system based on the big data technology, and the method comprises the steps: collecting and preprocessing elevator multi-source data, and generating a standardized data set; performing kernel function mapping and kernel principal component analysis dimensionality reduction to obtain a dimensionality-reduced feature sequence; the sequence is sent into a gating Transform in a segmented mode, and a time sequence modeling result is output; identifying a fault type based on a time sequence modeling result and predicting a future fault probability; the risk threshold is compared, the fault risk is judged, and early warning information is generated; and sending a judgment result and early warning information to an operation and maintenance end to assist in maintenance decision making. According to the method, efficient prediction and intelligent early warning of elevator faults are achieved by fusing kernel principal component analysis and gating Transform, and the operation and maintenance response efficiency and the equipment operation safety are improved.

The invention discloses a metering device fault early warning method, system and device based on the industrial Internet of Things, and relates to the technical field of metering device fault early warning methods. The invention provides a metering instrument fault early warning method based on industrial Internet of Things, and the method comprises the steps: judging whether a target instrument has abnormal data or not according to a first data set of the target instrument in a preset time period; if the target instrument has the abnormal data, acquiring a second data set of the target instrument in a preset time period; according to the abnormal data and the second data set, evaluating the fault probability of the fault condition of the target instrument; and when the fault probability is greater than a preset threshold value, sending early warning information to a target user.

The invention discloses an on-line monitoring and fault early warning system for the running state of a dynamic compression-shear testing machine, belongs to the technical field of fault diagnosis, and aims to solve the problems of poor adaptability to multiple motion modes, lagging fault early warning and fuzzy fault positioning in the prior art. According to the system, core collaborative link fault sensitive point monitoring parameters are matched according to a current motion mode, a dynamic threshold value is generated to construct a fault judgment threshold value system, real-time data subjected to cyclic division are collected and loaded to generate a time sequence data set, a reference candidate range state is judged based on the threshold value, and a fault judgment reference library is constructed; and executing deviation analysis prediction trend through the reference library, generating a compensation instruction, identifying potential faults in combination with a threshold value, an actual value and a prediction value, calculating a link fault probability, and completing diagnosis. According to the invention, monitoring adaptability and accuracy can be improved, compensation in advance and accurate fault early warning are realized, and stable operation and test accuracy of equipment are guaranteed.

The invention discloses a diesel generator turn-to-turn short circuit fault protection method and system based on multi-parameter characteristics, and relates to the technical field of intelligent fault diagnosis of power equipment, and the method comprises the steps: carrying out the feature extraction of operation state monitoring data, carrying out the integration according to a time sequence, and generating a real-time feature sequence set; performing spatio-temporal feature coding and fusion on the real-time feature sequence set to generate a multi-dimensional spatio-temporal feature map; mapping the comprehensive matching score set into fault confidence according to a linear relation, obtaining a fault confidence sequence, judging a fault level in combination with a characteristic amplitude overrun condition, and generating a fault diagnosis result set; and executing hierarchical response based on the fault diagnosis result set, generating a hierarchical protection action execution record, and generating a short-circuit fault protection report in combination with the fault diagnosis result set. According to the method, quantitative similarity analysis of real-time features and standard modes is realized, the fault probability is accurately evaluated through a multi-parameter weighted fusion mechanism, and the reliability and interpretability of a diagnosis result are improved.

The invention discloses a printing equipment fault detection method, equipment, a medium and a product, which are applied to the field of artificial intelligence, and comprise the following steps: collecting hardware parameters and software parameters of target printing equipment, and generating target equipment characteristics based on the hardware parameters and the software parameters; inputting the target equipment features into a pre-trained fault detection model to obtain a fault probability output by the fault detection model; and determining a target fault level according to the fault probability, and calling a target standard process in the maintenance strategy library based on the target fault level. By collecting parameters and generating target equipment features, equipment operation state information can be comprehensively captured, and multi-dimensional data support is provided for fault detection. The fault probability is determined through the fault detection model, quantitative evaluation of the fault probability can be realized by means of the advantage of double-model fusion, and the prediction accuracy is improved. By determining the target fault level and calling the corresponding standard process, hierarchical management and accurate operation and maintenance are realized, and the equipment maintenance efficiency and reliability are improved.

The invention relates to the technical field of charging pile fault protection, in particular to a charging pile fault protection method, system and device and a medium, and the method comprises the steps: continuously collecting the multi-modal data of a charging pile in real time, and carrying out the preprocessing; inputting the preprocessed multi-modal data into a pre-trained charging pile fault probability prediction model, outputting a fault probability value, comparing the fault probability value with a set threshold value, judging whether a fault risk exists or not, and if yes, giving out fault early warning; when a fault occurs, classifying and positioning the fault according to the multi-modal data, judging the fault level, executing hierarchical protection based on the fault level, and updating the charging pile fault probability prediction model by using the multi-modal data when the fault occurs; and after grading protection is completed, corresponding recovery measures are executed according to fault classification and positioning. According to the invention, early accurate early warning, flexible protection and rapid recovery of the fault of the charging pile can be realized, and the safety and operation and maintenance efficiency of the charging pile are comprehensively improved.

The invention belongs to the technical field of fault detection, and particularly relates to a wire drawing machine fault detection method based on the Internet of Things. Historical vibration and temperature and real-time data of operation of the wire drawing machine are collected through an Internet of Things sensor and are transmitted to a server through an Internet of Things protocol. After data are subjected to preprocessing denoising such as variational mode decomposition and a FastICA algorithm, a vibration anomaly judgment index (including effective anomaly time period judgment, vibration mode matching degree calculation and index synthesis) and a temperature anomaly judgment index (including correlation index calculation, heat conduction model establishment and index synthesis) are calculated, and then the two indexes are fused to obtain an anomaly index. A prediction model is constructed by using a neural network algorithm, after optimization of a genetic algorithm, a fault probability is output in combination with real-time data, and finally, a probability value and an abnormal index are normalized and averaged to obtain a fault score, so that fault detection and evaluation are realized. The method improves the fault detection accuracy and the equipment operation reliability, and reduces the maintenance cost.

The invention discloses a fault analysis method and system for an intelligent power distribution network, and relates to the technical field of power distribution network fault diagnosis, and the key points of the technical scheme comprise the following steps: determining a target region which needs fault analysis and comprises a target detection node in the intelligent power distribution network, collecting network topology data, environment parameters and historical fault data of the target area in the historical period; extracting a target detection node as well as a first adjacent node and a second adjacent node which are distributed adjacent to the target detection node from the target area; according to the network topology data, the environmental parameters and the historical fault data, extracting a regional association factor influencing the fault diffusion range of the first adjacent node and the second adjacent node; and predicting the operation condition of the target detection node in the current period according to the operation fluctuation condition of the target detection node in different historical periods to obtain a current state prediction value. The method has the effect of accurately analyzing the operation state and fault probability of the target detection node and the adjacent nodes.

The invention relates to the technical field of controller hardware fault detection, and discloses a real-time fault detection and diagnosis system for intelligent controller hardware, which comprises a hardware state monitoring module, a fault detection module, a fault diagnosis and processing module, a fault prediction module and a data storage module. The hardware state monitoring module collects assembly operation data and calculates an environment interference coefficient; the fault detection module compares real-time and normal operation parameters to screen fault hidden danger components; the fault diagnosis and processing module analyzes and processes fault types and severity; the fault prediction module predicts a fault probability based on machine learning; and the data storage module stores various data. The system can accurately monitor fault hidden dangers in real time, accurately diagnose faults, reasonably evaluate severity, process the faults by comprehensively considering environmental factors and task priorities, predict the faults in advance and give an early warning, provide data support for system optimization, effectively improve the hardware reliability and stability of the intelligent controller, and reduce fault loss.

The invention relates to the technical field of cableway fault monitoring, solves the technical problems that in the prior art, the difference between a fault prediction result and the reality is large, nonlinear data is difficult to predict accurately, and the prediction result is inaccurate, and particularly relates to a cableway monitoring management method and system based on artificial intelligence and edge calculation. The method comprises the following steps: S1, obtaining original data of a cableway monitoring system, preprocessing the original data to obtain alignment data, capturing dynamic features through time sequence dependence, providing feature importance interpretation, organically combining time sequence prediction and feature contribution by a weighted fusion mechanism to generate health indexes, and obtaining health indexes of the cableway monitoring system; and the continuous health state can be mapped into a quantifiable fault probability, and finally real-time early warning is realized through threshold triggering, so that the interpretability of a model output result is improved, independent optimization and fault traceability are supported, and the prediction precision and the reliability of operation and maintenance decision are remarkably improved.

The invention discloses a fault prediction and self-repairing method and device, electronic equipment and a storage medium, and relates to the technical field of computers. According to the method, the function of dynamically monitoring various data of processor hardware for the target node can be realized, the dynamically monitored hardware state data is input, the fault probability is predicted by a method of weighting and combining key indexes through a bidirectional long-short-term memory model and an attention mechanism, the possible faults are intelligently predicted, and the fault prediction efficiency is improved. And when the target node has a test fault in advance, the target node is repaired in a gradual load reduction mode, so that the effects of real-time monitoring, accurate prediction and rapid self-regulation are achieved. Manual intervention is reduced, the intelligent decision-making capability is achieved, operation and maintenance automation and intelligentization are achieved, resource self-adaptive repairing can be integrated, the self-adaptive capability is improved, and the complex scene fault sensing capability is improved.

The invention relates to the technical field of power distribution network fault monitoring, in particular to a fault recognition method and system based on an intelligent fusion terminal, and the method comprises the steps: obtaining an instantaneous multi-dimensional electrical data set at each moment, and constructing an input sample with the current moment as an end point, inputting the input sample into the trained long-short-term memory model to calculate a first fault probability of the input sample; inputting the input sample into a trained optimal fuzzy clustering model to calculate a second fault probability of the input sample; and distributing respective weights for an output result of the long and short term memory model and an output result of the optimal fuzzy clustering model, carrying out weighted fusion on the first fault probability and the second fault probability to obtain a comprehensive fault probability of the input sample, and judging whether the power distribution network has a fault according to the comprehensive fault probability. According to the invention, through multi-source information fusion, model collaborative optimization and dynamic weight distribution, the fault identification precision and response speed of the power distribution network under complex conditions are effectively improved.

The invention discloses a fault detection method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data processing. The method comprises the steps that under the condition that a target system breaks down, fault warning information and a target knowledge graph of the target system are acquired, and the target knowledge graph comprises a plurality of nodes, potential fault probabilities of the nodes, fault propagation probabilities and edges between the nodes; determining a fault sub-graph of the target system according to the fault alarm information and the target knowledge graph, wherein the fault sub-graph is a sub-graph composed of abnormal nodes in a plurality of nodes in the target knowledge graph; and determining a root cause node in the plurality of abnormal nodes according to the association relationship of the plurality of abnormal nodes in the fault sub-graph, the potential fault probability and the fault propagation probability, and generating and displaying a fault root cause analysis result according to the root cause node and the target fault root cause probability thereof so as to efficiently and accurately determine the fault root cause.

The invention discloses a component fault prediction method and device, a computer program product and a storage medium, belongs to the field of servers, is used for predicting a target component fault based on a plurality of associated sensing features, and solves the problem of poor component fault prediction precision. Considering that part faults of a server may be reflected through a plurality of associated sensing features, the method responds to a prediction instruction for target firmware faults at a future target moment, and firstly determines a prediction value and a numerical level of the prediction value by integrating the plurality of associated sensing features; and then a fault probability prediction result is obtained according to a weighted result of the numerical level, so that part fault prediction through single sensing data is avoided, and multi-dimensional data is fully utilized to predict the fault of the target part, so that the prediction accuracy of the part fault can be improved, and stable operation of a server is ensured.

The invention discloses an equipment fault diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The pre-training joint reasoning model is used for comprehensively analyzing the multi-dimensional data to generate an equipment fault probability prediction result, a composite fault mode can be effectively recognized, dynamic working condition changes can be adapted, meanwhile, tracing analysis is conducted on a fault propagation path in combination with the graph neural network, the effectiveness of feature extraction and modeling is improved, and therefore the fault probability prediction result is obtained. The technical problems that in an existing equipment fault diagnosis system, a threshold model is difficult to recognize a composite fault, a physical model cannot adapt to a dynamic working condition, and the fault traceability of a correlation analysis model is limited can be solved, and the purposes of improving the accuracy and adaptability of equipment fault diagnosis, enhancing the fault traceability and improving the reliability of equipment fault diagnosis are achieved. And the technical effect of promoting predictive maintenance to develop to higher-level intellectualization is achieved.

The invention relates to the related technical field of robot fault detection, in particular to an industrial robot real-time fault detection method and system, and the method comprises the steps: analyzing the operation state of an industrial robot based on a communication state signal, determining a motion domain and sensing domain synchronization factor, configuring a cross-domain feature vector and a performance feature vector, and carrying out the fusion, the deep belief network is used for drawing up the fault probability graph and identifying the feature identifier to achieve fault reminding, the technical problems that a fault response strategy is fixed, complex and changeable industrial scene requirements are difficult to adapt, and fault reminding cannot be effectively conducted in time are solved, cross-domain feature vectors and performance feature vectors are constructed, and the fault reminding efficiency is improved. The method has the technical effects that the serial control sequence is dynamically verified, multi-scale decomposition is combined, signal delay fluctuation and retransmission frequency are accurately extracted, a deep belief network is used for fusing time sequence and spatial features to identify fault types, fault reminding is carried out through a fault probability distribution map, and the operation safety of the industrial robot is guaranteed.

The invention provides a charging fault real-time diagnosis system based on edge calculation, and relates to the technical field of charging fault diagnosis. The method comprises the following steps: constructing a three-dimensional space model of a target charging area, and dividing the target area into M monitoring sub-areas; collecting charging fault data of each monitoring sub-region; preprocessing the charging fault data; constructing a fault risk assessment model by using an echo state network, and optimizing model hyper-parameters by using a pollen propagation algorithm; deploying a fixed edge node in a high-risk area in the fault risk level of each monitoring sub-area for continuous monitoring, planning an inspection path of a mobile edge node, and executing monitoring according to a dynamic period; receiving monitoring data of fixed and mobile edge nodes in real time, and constructing a fault prediction model based on a support vector machine to obtain a fault probability value; and the fault probability value is compared with a preset multi-level early warning threshold value, the fault level is judged, and the early warning information is output, so that the safety and the reliability of the charging process are ensured.

The invention discloses a disk fault prediction method and device, and relates to the technical field of data processing, and the method comprises the steps: obtaining the target feature data of a target disk, and predicting the fault probability of the target disk in a future target time period through a preset disk fault prediction model according to the target feature data of the target disk, outputting the target prediction probability, predicting the fault probability of the target disk in the future target time period, and determining the feature contribution degree of each feature data based on a preset disk fault prediction model and the target feature data when the target prediction probability is determined to be greater than a first preset threshold value; furthermore, a preset fault analysis model is adopted to determine a target fault reason according to the target feature data and the feature contribution degree of each feature data, and the target fault reason is sent to the client, so that a user can grasp the running state of the target disk and the fault reason of which the fault probability exceeds a threshold value in advance, and the interpretability of the disk fault is improved.

The invention provides a cabin-type intelligent substation risk early warning method, and relates to the technical field of substation fault diagnosis, and the method comprises the steps: obtaining the historical fault information of a substation; clustering the environment data by adopting a clustering algorithm, and constructing an environment category-fault type-fault probability mapping relation table; obtaining current environment data of the transformer substation, and determining fault categories and fault probabilities corresponding to the fault categories; determining a target device based on the fault category, and determining a monitoring frequency of the target device based on a fault probability corresponding to the fault category and an importance coefficient of the target device; acquiring operation parameters of the target equipment based on the monitoring frequency; comparing the operation parameter with a standard parameter, and determining a parameter variation of the target equipment; taking the parameter variation as the input of a pre-constructed fault diagnosis model, and outputting the fault probability of the target equipment; and when the fault probability is greater than a preset threshold value, generating and sending early warning information. And the system load is reduced.

The invention discloses a hard disk fault determination method and device applied to a periodic rule. The method comprises the following steps: determining a feature item of which the contribution degree is greater than a contribution degree threshold value in operation data of a target hard disk as a target feature item; inputting the target feature item into a fault determination model, and processing the target feature item by using the fault determination model to obtain a fault probability that the target hard disk has a fault; and when the fault probability is greater than a preset probability value, determining that the target hard disk has a fault, or when the fault probability is not greater than the preset probability value, determining that the target hard disk does not have a fault. According to the method and the device, the technical problems that the features selected by a traditional feature selection method are not representative and cannot be effectively combined with a model to accurately detect the hard disk fault when the hard disk fault is determined in the related technology are solved.

The invention provides a multi-modal process fault detection method based on SKDPC-RS-MSDAE, which is used for solving the problems that multi-modal division depends on experience knowledge, the detection precision of tiny faults is low and the like. The method comprises the following steps: firstly, collecting original data of a TE multi-mode process, and mining deep information of the original data by using ResNet; secondly, a DPC modal identification method based on path selection is designed, local density and relative distance are reconstructed through path selection and kNN, an optimal clustering number judgment criterion based on a contour coefficient is constructed, and a DPC based on the contour coefficient and path selection is designed; secondly, establishing an MSDAE detection model for each steady state, and obtaining a corresponding fault probability; and finally, designing a Bayesian reasoning probability index BIP based on multi-probability fusion. Experimental verification is carried out through the TE process, compared with other methods, the overall fault detection rate is improved, and the detection performance of tiny faults is improved.

The invention discloses a method and device for determining the fault probability of a fan system, a storage medium and an electronic device.The method comprises the steps that first fault data are obtained from historical operation records of the fan system, and second fault data from a target object are received, the first fault data represents data of a fault event of the fan system, and the second fault data represents data of risk assessment of the fault event of the fan system by the target object; determining a fault reason of the fault event from the first fault data and the second fault data, and determining a risk factor of the fan system according to the fault reason; and determining the fault probability according to the prior probability of the risk factor and the objective weight of the risk factor. By adopting the technical scheme, the problem that the fault probability of the fan system cannot be accurately determined is solved.

The invention discloses a fault diagnosis method, system and device, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: obtaining the operation state data of a target node; based on the operation state data, training a machine learning model corresponding to the target node, and uploading a training result to a federal aggregation server; receiving model updating parameters fed back by the federal aggregation server, and updating the machine learning model based on the model updating parameters; predicting a fault probability of a target node by using the updated machine learning model, and when the fault probability is greater than a preset threshold, broadcasting a fault message to an adjacent node; the fault message is used for triggering the adjacent node to determine the fault type of the target node by using a machine learning model corresponding to the adjacent node and report fault type information to the federal aggregation server. Through the technical scheme provided by the invention, the fault diagnosis efficiency can be effectively improved.