303 results about "Fault probability" patented technology

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention discloses a fault analysis method for vehicle-mounted automatic test equipment, and relates to the technical field of vehicle-mounted electronic equipment testing, and the method comprises the steps: collecting vehicle-mounted test data of the vehicle-mounted automatic test equipment, and storing the vehicle-mounted test data in a test database according to a time sequence; performing feature extraction on the test database through a deep learning model to generate a fault feature vector; inputting the fault feature vector into a fault diagnosis model, and generating a fault type judgment result and a fault probability value; and according to the fault type determination result and the fault probability values, screening corresponding fault recovery schemes from a preset fault recovery strategy library, and sorting the fault recovery schemes according to the fault probability values from high to low to form a fault processing report. According to the method, the fault processing report is generated based on the layered fault recovery strategy library and the priority grading mechanism, the technical problems that a traditional fault diagnosis method is insufficient in feature extraction and low in diagnosis precision are solved, and the accuracy and recovery efficiency of fault diagnosis are improved.

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 an air conditioner fault detection method and system, electronic equipment and a storage medium, and relates to the technical field of air conditioner fault detection. The method comprises the steps that parameter values corresponding to all dimensions during operation of the air conditioner are obtained, the operation state of the air conditioner is determined based on the parameter values corresponding to all the dimensions, and all the dimensions are at least one of pressure, temperature, current or rotating speed; when the operation state is a preliminary abnormality, determining an abnormal dimension and a fluctuation feature corresponding to each abnormal dimension based on a parameter value corresponding to each dimension; performing fusion calculation on the fluctuation characteristics to obtain a fault probability value of the air conditioner; and when the fault probability value is larger than a probability threshold value, fault positioning is conducted on the basis of all the fluctuation characteristics, and a fault detection result of the air conditioner is obtained. By implementing the technical scheme provided by the invention, the accuracy of air conditioner fault detection is 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 relates to a charging pile fault diagnosis method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the fault recognition of a part of a target charging pile based on fault feature data, and obtaining a potential fault part; based on the component atlas, carrying out atlas construction on the potential fault component to obtain a potential fault component association atlas; analyzing an influence relationship among the potential fault components based on the potential fault component association graph to obtain a fault propagation path; calculating the fault probability of the potential fault component based on the fault propagation path through a preset Bayesian network model; and taking the potential fault component corresponding to the maximum fault probability as a fault source, analyzing the fault type of the fault source through a preset classification algorithm, and formulating a maintenance strategy of the target charging pile based on the fault type, thereby solving the problem that the fault diagnosis of the charging pile mainly depends on manual inspection and user feedback, so that the maintenance efficiency is improved. And the technical problems of slow response and poor accuracy exist.

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 relates to the field of fault early warning, in particular to a fault early warning method for a tailing paste filling system. Comprising the steps of monitoring and storing parameters in real time to form a parameter matrix; a sliding time window technology is introduced, standardization processing is carried out on the parameters at each moment, and standardized parameter values are obtained; on the basis of the standardized parameter values, a dynamic feature extraction algorithm is adopted for processing to obtain feature values; based on the characteristic value, a fault probability value is calculated through a dynamic gain adjustment fault probability calculation algorithm, the fault probability value is compared with a fault threshold value, and a fault judgment result is obtained; according to the characteristic value and the fault judgment result, a saliency score is calculated through a fault positioning algorithm, and a fault source is determined according to the saliency score; and performing fault early warning based on the fault source. The technical problems that in a tailing paste filling system, multiple monitoring points exist, so that real-time data processing becomes complex, and unified comparison and analysis are difficult to conduct are solved.

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 an intelligent fault diagnosis method and system for an automatic numerical control machine tool, and relates to the technical field of data processing, and the method comprises the steps: interacting fault event records of a target numerical control machine tool; based on the fault event records, fault characteristic mining is carried out to construct a fault network, a fault characteristic network is constructed through graph theory analysis based on fault propagation, and a fault probability network is constructed through fault failure analysis; according to the fault network, diagnosis large model training and lightweight processing are carried out, a fault diagnosis module is built, and module building is carried out through two-step training based on a lightweight loss function; time-staggered sampling frequency is determined, monitoring management and control of a front-end sensor are carried out based on the time-staggered sampling frequency, and machine tool operation data are determined; and the machine tool operation data is transmitted back to the fault diagnosis module, fault occurrence judgment and failure level measurement are carried out, and a fault diagnosis result is determined. Therefore, the technical effects of improving the fault diagnosis accuracy and real-time performance and improving the deployment adaptability are achieved.

The invention discloses a special equipment operation fault detection method and device, and relates to the related field of data processing, and the method comprises the steps: carrying out the dynamic sensing of special equipment, obtaining an operation dynamic data set of the special equipment, and synchronizing the operation dynamic data set to a long-short-term memory network, determining a plurality of fault influence factors, performing fault joint detection in combination with the equipment use scene parameter set, generating a first fault detection result, activating a fault probabilistic reasoning model, and performing fault reasoning in combination with the operation dynamic data set, and generating a second fault detection result and performing automatic comprehensive analysis and detection on the operation fault of the special equipment in combination with the first fault detection result. The technical problems of low detection efficiency and high maintenance cost caused by difficulty in processing large-scale data sets and real-time monitoring requirements in traditional fault detection are solved, an intelligent and automatic fault detection method is introduced, the accuracy and timeliness of fault detection are improved, and the technical effect of reducing risks and losses caused by equipment operation faults is achieved.

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 provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

The invention provides a multidimensional predictable load balancing fault switching method and a multidimensional predictable load balancing fault switching system. The method comprises the following steps: constructing a machine learning model based on time sequence data of historical fault probabilities, and predicting the fault probabilities of fault granularities in a preset period of time in the future by adopting the machine learning model; and when the predicted fault probability exceeds a corresponding standard threshold value, triggering a preheating mechanism for disaster recovery switching, including operations of hot standby starting, state synchronization and standby cluster traffic undertaking. According to the technical scheme, through the multi-dimensional prediction and flexible switching mechanism, various faults in the load balancing system can be sensed and dealt with more timely and more accurately, the high availability and reliability of the load balancing system are remarkably improved, and stable operation of services and continuity of user experience are ensured.

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 relates to the field of industrial intellectualization, and provides a fault prediction and prevention method and system in an intellectualized equipment transformation technology, and the method comprises the steps: obtaining real-time data of intellectualized equipment, carrying out the filtering processing, screening an abnormal entropy value, extracting an abnormal parameter set, constructing a fault prediction frame based on the abnormal entropy value, and carrying out the fault prediction. The method comprises the following steps: predicting a fault probability, determining a residual operation life and calculating an equipment operation and maintenance period, then evaluating an operation state to obtain a state evaluation parameter, querying a fault type and analyzing occurrence time, constructing a preventive maintenance strategy in combination with a current state, generating a maintenance task list and querying a maintenance task, and analyzing a prevention target in the maintenance task. And identifying prevention obstruction factors, and finally generating an operation report based on the monitored operation feedback data. According to the invention, the operation stability of intelligent equipment can be improved.

The invention discloses a wind power equipment fault diagnosis optimization algorithm, and belongs to the field of wind power equipment management, and the optimization algorithm comprises the following specific steps: I, collecting and preprocessing various operation data of wind power equipment; iI, constructing an equipment fault map and carrying out feature extraction on the preprocessed data; iII, performing fault diagnosis and classification on the real-time data based on the equipment fault atlas; iV, optimizing a fault processing scheme according to the diagnosed fault and the type thereof; v, evaluating the probability of fault occurrence and formulating a corresponding early warning mechanism; according to the method, local optimum can be avoided, the effectiveness of a fault processing scheme is improved, the flexibility and adaptability of the scheme are enhanced, a feasible solution can be quickly generated, the method is suitable for large-scale data processing, the accuracy of fault probability evaluation is improved, comprehensive fault occurrence probability analysis is provided, targeted maintenance strategies are helped to be formulated, and the method is suitable for large-scale data processing. Resource configuration can be optimized, potential faults can be responded in time, the downtime of equipment is shortened, and the operation cost is reduced.

The invention provides a server fault prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining time sequence data corresponding to an operation index when a server operates, dividing the time sequence data corresponding to the operation index into a plurality of time windows, and obtaining a target dynamic feature corresponding to each time window; further predicting the fault probability of the server based on the target dynamic feature corresponding to each time window under the operation index, and outputting a server fault alarm indication when the fault probability is greater than a preset threshold value; according to the method, the fault probability of the server is predicted according to the current time, namely the server operation condition before the current time, so that the possible fault of the server is predicted before the fault occurs.

The invention discloses a distributed collaborative modularized rapid detection method and device for a box-type substation, and the method comprises the following steps: enabling each distributed module to judge whether the distributed module has a fault possibility or not based on collected operation data; the distributed module with the fault possibility initiates an abnormal signaling; after the other distributed modules receive the abnormal signaling, whether correction characteristic parameters of the other distributed modules are filled in corresponding positions in the abnormal signaling or not is confirmed, and the abnormal signaling is sequentially transmitted among all the other distributed modules; and summarizing all the obtained correction characteristic parameters, calculating a posterior fault probability by the distributed module which initiates the abnormal signaling, and judging that a fault exists when the posterior fault probability exceeds a set threshold value. According to the distributed architecture, the bottleneck of a centralized system is avoided, the modules are processed in parallel, and the detection efficiency is improved. Through the feature correlation model, interference of redundant information is avoided, and the accuracy of fault judgment is 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 provides a power equipment fault detection method and system based on a deep learning network, and the method comprises the steps: obtaining state parameter data of power equipment in an observation time period, forming a first multi-dimensional time sequence according to the state parameter data, inputting the first multi-dimensional time sequence to a deep learning model, obtaining a fault probability prediction value corresponding to each time node; obtaining a fault coefficient according to the fault probability predicted values of all the time nodes, and judging whether the power equipment has a fault according to the fault coefficient; and if the power equipment has a fault, inputting the fault probability prediction value corresponding to each time node into the fault classification model to obtain a fault category set corresponding to the observation time period, and obtaining the fault category of the current time node according to the fault category set. According to the method and the device, the condition of inaccurate diagnosis result caused by data exception due to fault judgment through single-dimension data can be avoided, and the fault diagnosis accuracy is improved.

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.