23 results about "Fault probability" patented technology

The application relates to the technical field of fault diagnosis, and provides a high-frequency transformer fault diagnosis method and system, which comprises the following steps: collecting a target signal with a time stamp and extracting corresponding features, simultaneously relying on a transformer structure, material parameters and physical rules to build a digital twin model, simulating insulation and structure degradation equivalent working conditions, solving multi-physical field data and generating multi-physical field mechanism samples; then, the mechanism samples and field measured data are fused through a generative adversarial network to expand the fault sample data set and solve the sample scarcity problem; in the running stage, the digital twin model is updated in real time through parameter online inversion, a feature dynamic graph representing multi-physical coupling is constructed by combining the parameter deviation of internal mechanism degradation and the multi-source features of external working conditions, finally, the feature aggregation and time sequence reasoning are completed through the graph neural network and the time sequence neural network trained offline, the fault probability is output, and the optimal diagnosis result is determined; thereby, the fault recognition accuracy and the robustness in the running stage are improved.

This invention provides a fault identification method for test equipment based on diagnostic semantic adaptation, belonging to the field of test equipment operation status monitoring technology. It includes: S1, collecting multi-measurement channel sensor signals and image information generated during the test and performing preprocessing; S2, performing measurement channel mapping, unit conversion, range matching, and normalization processing on multi-source asynchronous data; S3, determining the fault probability of the test equipment, obtaining the fused fault confidence level, and generating a structured test equipment fault diagnosis record package; S4, analyzing the fault types of the test equipment and executing the test equipment fault identification alarm program. This invention uses an edge-side temporal fault discrimination network to perform real-time fault screening on a unified state vector, and employs a multimodal transfer diagnostic network for fault verification and incremental learning, forming a parameter optimization closed loop to improve the accuracy and robustness of fault identification.

The application provides a process micro-fault detection method based on stationary subspace integrated probability slow features, and belongs to the technical field of industrial process fault detection. The technical scheme is as follows: the application is composed of an offline stage and an online stage. The application fuses the stationary information of SSA and the time sequence dynamics of SFA to form complementary feature description, finally realizes fine division of non-stationary multivariate data through integrated learning, realizes effective extraction of micro-fault probability information, and greatly improves the micro-fault detection rate.

A fault prediction method, device, equipment, medium and product are disclosed. The method comprises: obtaining a target data set, the target data set comprising: a container running index, an application log text, a listening event and a link tracking index; performing fault prediction based on the target data set to obtain a fault prediction result, the fault prediction result comprising: a fault probability, a fault type and a prediction time. Through the technical solution of the present application, real-time analysis of multi-dimensional indexes during container running can be performed to realize early prediction of faults.

The disclosure provides a data processing method and device, electronic equipment and storage medium, and relates to the technical field of data processing. The data processing method comprises the following steps: monitoring state information of a storage device in a distributed data cluster; predicting potential fault information of a storage node according to the state information; in response to the potential fault probability being greater than or equal to a threshold value, isolating a first storage node with potential faults before reaching the estimated fault time; determining a target storage device where the first storage node is located, and repairing the data of the first storage node according to the potential fault type in the target storage device; isolating the fault storage node before the fault occurs; actively discovering the fault storage node; and recovering the data in the storage device at the fastest speed, so that the service can be continuously and stably provided.

This invention provides a fault prediction method, apparatus, electronic device, and storage medium. The method includes: performing fault prediction based on component attribute data of the product under test (BUT) during the design phase; applying a fault hierarchy model to infer the root cause of faults based on the predicted fault probability distribution; and obtaining the fault propagation path corresponding to the fault modes involved in the BUT. The fault hierarchy model is configured with a multi-layer topology from the product component level to the product whole-machine level, and the transmission relationship of faults in the multi-layer topology from the product component level to the product whole-machine level. This not only realizes the leap from qualitative experience analysis to quantitative data prediction, but also transforms isolated component risks into visualized fault propagation paths. This allows designers to clearly see the specific impact chain of underlying design defects on the reliability of the whole machine, thereby enabling targeted optimization before product manufacturing and greatly improving R&D efficiency and the final quality of the product.

The application discloses a kind of unmanned mine car layered diagnosis method, it is related to unmanned mine car field, comprising: S1, construction fault classification and coding system, fault is classified according to source classification, generates only main fault code and sub fault code;According to fault severity, influence range and recoverability, establish multistage fault evaluation model, give grade to each fault;S2, main fault code, sub fault code, fault grade and corresponding processing strategy are written into configuration file, and the configuration is loaded when mine car starts;S3, real-time acquisition and fusion multi-source data, extract abnormal characteristics;S4, real-time data characteristics are matched with fault library, calculate fault probability, execute corresponding hierarchical control strategy;S5, execute closed-loop feedback optimization, upload the data of fault diagnosis and processing process to cloud, to optimize fault library and diagnostic algorithm model.The realization is to unmanned mine car fault from loop, site to reason Layered accurate positioning.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The application relates to the technical field of server fault prediction, and discloses a server fault prediction system and method. The method comprises the following steps: collecting running index data streams of server components through a monitoring agent to form an initial running data pool; performing multi-level cleaning and context association analysis on the data to construct a high-quality running data set; generating a server health state portrait and establishing a dynamic health state baseline model based on the set; constructing a knowledge graph capable of representing fault causal relationships by analyzing the deviation mode of real-time indexes and the baseline model; and driving a risk calculation engine by using the knowledge graph to output the fault probability of the components and a maintenance scheme. The method realizes more accurate and more forward-looking prediction of server faults by improving data quality and introducing knowledge reasoning, and effectively supports intelligent operation and maintenance decisions.

An active fault detection system for a heating, ventilation, and air conditioning system is disclosed. In some embodiments, the system comprises at least one processor; and memory storing instructions, when executed cause the system to: obtain system data related to operation of one or more components of the HVAC system; determine a fault probability based on the system data, the fault probability indicating a probability of a fault in the HVAC system; send a control signal to a component of the HVAC system to modify one or more control parameters of the component responsive to the fault probability being within a fault threshold range, the threshold range having an upper and lower threshold values; update the determined fault probability based on updated system data resulting from modifying the one or more control parameters; and generate a service schedule responsive to the updated fault probability being above the upper threshold value.

This invention provides a method and system for fault diagnosis of mining equipment using digital twins based on multi-source data fusion, belonging to the field of fault diagnosis technology. The method includes: uploading multiple multi-source heterogeneous sensing data to a mining twin platform; driving the equipment twin model to update its mapping state to collect multiple real-time feature vector streams; constructing a physical connection graph network based on physical connectivity; constructing a functional association graph network based on functional coupling; performing node-level fault probability prediction; obtaining the first-layer fault prediction result; performing equipment-level fault tracing and localization; and outputting fault diagnosis information covering the root cause node, fault type identification result, fault severity, and potential fault propagation path. This invention solves the technical problem that existing fault diagnosis methods often rely on single-type sensor data for fault analysis, failing to comprehensively reflect the true operating status of the equipment, leading to inaccurate fault diagnosis results.

The present application relates to the technical field of power generation equipment fault prediction, and discloses a digital twin fault prediction method and system for power generation equipment, a digital twin fault prediction method for power generation equipment, comprising the following steps: step S101, calculating and obtaining upper and lower sideband integral corridors; step S102, calculating and obtaining sideband energy asymmetry; step S103, calculating and obtaining sideband energy asymmetry after deviation elimination; step S104, calculating and obtaining a single contrast quantity; step S105, calculating and obtaining a risk driving quantity; and step S106, calculating and obtaining a cumulative fault probability. Through the construction of the sideband integral corridor and the setting of the exclusive environment control band, the present application can effectively strip environmental interference such as power grid fluctuation and aerodynamic load, and extract sideband energy asymmetry features related to faults; in combination with a gated convolution aligner, a dynamic health baseline suitable for time-varying working conditions is generated, and accurate identification of early weak faults is realized.

The application discloses a kind of diesel generator interturn short-circuit fault protection method and system based on multi-parameter feature, relating to the technical field of intelligent fault diagnosis of electric power equipment, including, feature extraction is carried out to operating state monitoring data, generates real-time feature sequence set according to time sequence integration, and real-time feature sequence set is encoded and fused with space-time feature, and generates multidimensional space-time feature map;Comprehensive matching score set is mapped into fault confidence according to linear relationship, obtains fault confidence sequence, and determines fault grade in combination with feature amplitude overrun condition, generates fault diagnosis result set;Based on fault diagnosis result set, hierarchical response is executed, and hierarchical protection action execution record is generated, and short-circuit fault protection report is generated in combination with fault diagnosis result set.The application realizes the quantitative similarity analysis of real-time feature and standard mode, accurately evaluates fault probability by multi-parameter weighted fusion mechanism, and improves the reliability and explainability of diagnosis result.

The application relates to the technical field of fault diagnosis, and provides a high-frequency transformer fault diagnosis method and system, which comprises the following steps: collecting a target signal with a time stamp and extracting corresponding features, simultaneously relying on a transformer structure, material parameters and physical rules to build a digital twin model, simulating insulation and structure degradation equivalent working conditions, solving multi-physical field data and generating multi-physical field mechanism samples; then, the mechanism samples and field measured data are fused through a generative adversarial network to expand the fault sample data set and solve the sample scarcity problem; in the running stage, the digital twin model is updated in real time through parameter online inversion, a feature dynamic graph representing multi-physical coupling is constructed by combining the parameter deviation of internal mechanism degradation and the multi-source features of external working conditions, finally, the feature aggregation and time sequence reasoning are completed through the graph neural network and the time sequence neural network trained offline, the fault probability is output, and the optimal diagnosis result is determined; thereby, the fault recognition accuracy and the robustness in the running stage are improved.

The application provides a fault detection method and device, electronic equipment and storage medium. The method comprises: obtaining running data corresponding to a plurality of detection points of a target device; inputting the plurality of running data into a pre-trained fault detection model, wherein the fault detection model comprises at least an input layer, a multi-head attention mechanism layer, a plurality of feature extraction layers, a full connection layer and an output layer according to the transmission order of the running data; calculating the weight of each running data through the multi-head attention mechanism layer, and fusing the plurality of running data based on the weight; and extracting and classifying the fused running data through the plurality of feature extraction layers and the full connection layer, and outputting the fault probability of the target device through the output layer. The application solves the technical problem of excessive model parameters of the fault detection model in the prior art, and reduces the complexity of the fault detection model.

This application provides a deep learning-based predictive maintenance and fault detection system for power equipment, relating to the field of power systems and their automated monitoring. It addresses the problems of existing technologies, such as poor accuracy in predicting low-probability, high-risk events, weak generalization ability with small samples, insufficient adaptive adjustment across operating conditions, and a lack of deep integration between data-driven and physical mechanisms. The system includes: a data acquisition module, which collects time-series data of operating status, physical parameter sensor data, and environmental parameter data; a dual-modal heterogeneous inference module, comprising a data-driven subnetwork that outputs fault probabilities, a physical simulation subnetwork that generates simulation state variables and calculates physical residuals, and a cross-attention bridging module that calculates the difference between the two to obtain a consistency score; an environmental adaptive meta-controller that dynamically adjusts the fusion weights and corrects the physical simulation boundary conditions; and an output and decision module that generates prediction results, anomaly alarms, predictive maintenance strategies, and fault detection reports based on fault probabilities, residual vectors, and consistency scores.

This application relates to a method, apparatus, electronic device, and storage medium for self-healing fault detection in secure atomic capabilities, applied in the field of system security technology. The method includes: acquiring indicator data of a target instance; analyzing the indicator data to determine current fault information; inputting the indicator data into a risk prediction model for fault prediction to obtain predicted fault information, the predicted fault information including fault probability and risk indicators; if the current fault information includes anomaly indicators and / or the fault probability is greater than a preset fault probability, then determining a target fault type based on the anomaly indicators and / or the risk indicators; determining a fault level based on the target fault type; and determining a fault self-healing strategy based on the fault level. This application improves the timeliness and accuracy of self-healing fault detection in secure atomic capabilities.

The present application relates to the field of electric meter remote monitoring, more particularly, the present application relates to the electric meter remote fault diagnosis method and system based on internet of things, the method comprises: obtaining all historical fault sequences corresponding to each fault type; the period of each historical fault sequence is calculated and the probability of each period appearing, and based on the historical fault sequence corresponding to a single period, the template sequence of the period is generated; the real-time parameter time sequence of the electric meter is collected, the similarity of the real-time parameter time sequence and the template sequence of each period corresponding to the fault type is calculated, the calculated similarity is weighted and summed according to the probability of each period appearing, and the real-time fault probability of the fault type is obtained; when the real-time fault probability is greater than the preset fault threshold, the diagnosis result of the corresponding fault type of the electric meter is output. The present application can more comprehensively and accurately describe the complex evolution law of the fault, and realize the remote and timely diagnosis of the electric meter fault.

This invention relates to the field of power operation and maintenance monitoring technology, specifically a power system fault prediction method based on digital twins. The method includes: collecting synchronous electrical measurement data from each monitoring node of the target power system within a continuous time window to form an original measurement sequence; and building a digital twin model that operates synchronously with the physical power grid based on the original measurement sequence. Within the model, multi-timescale dynamic state estimation is performed on the measurement sequence to generate a feature set of the current operating state of the power system. The feature set is then imported into a pre-trained temporal convolutional network, which outputs a fault probability distribution vector. Components exceeding a threshold are selected to locate fault prediction nodes and generate fault warning indicators. This approach can deeply mine the correlation features of power time-series data, accurately depict the system's operating status, and achieve accurate prediction and node location of potential power system faults.

The application discloses a drilling processing fault prediction method and system. The method comprises the following steps: obtaining standardized time series data; constructing a multi-scale feature vector, wherein the multi-scale feature vector comprises data-driven features and physical theory benchmark values; constructing a hybrid neural network model comprising a physical constraint layer; establishing a joint loss function and using the joint loss function to guide the parameter training of the hybrid neural network model; inputting real-time features to be tested into the trained hybrid neural network model, obtaining fault probability and confidence evaluation information, identifying fault physical causes based on real-time physical constraint violation degrees, and generating maintenance suggestions. The scheme of the application significantly improves the robustness and accuracy of drilling processing fault prediction under complex working conditions, effectively alleviates the black box drawbacks of the deep learning model, and enhances the reliability of the prediction results.

This invention discloses a method, system, and medium for open-set domain generalized bearing fault diagnosis under unknown operating conditions, belonging to the field of bearing fault diagnosis technology. The method includes: utilizing source domain bearing vibration data; pre-training a fault diagnosis model based on subdomain constraints and domain adversarial mechanisms; constructing class centers based on the average activation vectors of known fault categories in the source domain; acquiring target domain bearing vibration signals in real time and inputting them into the pre-trained fault diagnosis model to obtain test set sample scores; classifying the test set samples into known faults and unknown faults based on the distance and distance threshold of each sample score to the class centers; differentially adjusting the test set sample scores corresponding to known and unknown faults; and determining the probabilities of known and unknown faults based on the adjusted scores. This invention can effectively improve the accuracy of open-set fault diagnosis for rotating machinery under unknown operating conditions.

The application is suitable for the technical field of electrical equipment state monitoring, and provides a smart substation fault detection method and system, which comprises the following steps: collecting oil temperature, oil chromatographic data and electric parameter signals of a target electrical equipment in real time; based on a method of fusing physical mechanism and data driving, the oil chromatographic data is adaptively corrected according to the oil temperature to obtain corrected gas concentration data; a dynamic multi-dimensional fault feature set is constructed according to the corrected gas concentration data, the oil temperature and the electric parameter signals; based on a preset fault prediction model, the dynamic multi-dimensional fault feature set is taken as input, and a fault probability prediction result is output; based on a preset dynamic threshold, fault early warning information is generated according to the fault probability prediction result. Through the construction of the dynamic multi-dimensional fault feature set containing electric-thermal correlation features and the like, the application can sensitively capture early fault weak signs, thereby facilitating timely maintenance of electrical equipment of a substation.

The application provides a teller machine fault prediction system and method based on multi-dimensional digital twinning, and relates to the technical field of teller machine fault detection. The method collects multiple types of original data of key parts of the teller machine at the data acquisition end, and transmits the preprocessed data to a computing power all-in-one machine for encryption. The computing power all-in-one machine deploys a multi-dimensional digital twinning model and a long short-term memory fault prediction model. The twinning model simulates the original data in geometric, physical and behavioral dimensions to generate multi-dimensional simulation data. The prediction model predicts the fault probability based on the simulation data and generates early warning and maintenance strategies according to the fault probability and sends them to designated maintenance personnel. The scheme effectively solves the problems of traditional teller machine maintenance, such as dependence on manual inspection, lagging fault response and low prediction accuracy, and realizes real-time monitoring of equipment operation status, early fault accurate warning and intelligent maintenance, shortens fault response and repair time, reduces operation and maintenance cost, and improves equipment operation reliability.