1953 results about "Diagnostic technology" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention discloses a distribution line fault intelligent diagnosis method based on multi-source information fusion, and belongs to the technical field of power system fault detection and diagnosis, and the method comprises the steps: obtaining distribution line data and meteorological data; superposing the distribution line data with the meteorological data to generate a space-time fusion feature matrix based on the wire space displacement; dynamically adjusting a preset initial threshold value by using the space-time fusion feature matrix; and performing multi-round verification on the abnormal fluctuation of the electrical parameters of the line according to the fault judgment threshold, generating a fault positioning instruction, performing real-time matching with the topological structure of the line, correcting the priority queue of the fault section based on the switching action feedback data of the feeder terminal, and triggering the fault removal action matched with the priority. According to the method, spatial-temporal feature fusion of current, voltage, weather and spatial displacement data and a dynamic threshold adjustment mechanism are adopted, the fault type can be accurately identified, the fault section can be positioned in real time, and the diagnosis precision and the response speed in a complex environment are remarkably improved.

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of power system fault diagnosis, and discloses a power distribution network fault identification and positioning system based on a wide area measurement technology, and the system comprises a key measurement point optimization configuration module which determines the arrangement position of an optimal measurement point, and identifies a high-risk weak link in a power distribution network; the virtual-real fusion measurement network construction module adopts a generative adversarial network to generate blind area virtual measurement data conforming to a physical rule, and fuses actual measurement data and the virtual measurement data; the active fault excitation implementation module is used for implementing safe and controllable tiny disturbance injection and actively detecting response characteristics of weak links; the state estimation enhanced fault positioning module is used for constructing a health state evaluation model and calculating the deviation between a transfer function and a health baseline; the toughness fault positioning execution module is used for extracting key information dimensions and ensuring the reliability of fault positioning; through a virtual-real fusion measurement technology, efficient monitoring of the whole area of the power distribution network is realized on the basis of limited actual measurement equipment.

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention discloses an intelligent pavement disease diagnosis method based on image recognition, and relates to the technical field of pavement disease diagnosis, and the method comprises the steps: deploying an image collection device and a pavement monitoring sensor in a target pavement region, so as to obtain multi-source pavement data; preprocessing the multi-source road surface data, and performing feature extraction to obtain a road surface feature sequence; and based on a deep learning algorithm and in combination with the pavement feature sequence, learning features of different disease types, constructing a disease identification classification model, and identifying different types of pavement diseases. Through the high-definition camera and the image processing technology, various disease types such as cracks, pit slots and ruts can be quickly and accurately identified, and in combination with a deep learning algorithm, disease features can be automatically extracted, high-precision identification of road diseases is realized, the disease detection efficiency is remarkably improved, manual intervention is reduced, and the detection efficiency is improved. And timely and accurate data support is provided for road maintenance.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention relates to the technical field of fluid valve actuator equipment diagnosis, in particular to a fluid valve actuator monitoring and diagnosing system based on AI intelligence. According to the method, the target opening control instruction is generated through fluid medium characteristic data matching, and accurate regulation and control of the fluid valve are achieved; an opening degree change data set and a torque time sequence data set are captured in real time through a deployed sensor group, multi-dimensional dynamic fluctuation analysis is carried out, and intelligent recognition and screening of fluid valve abnormity are achieved; the medium temperature, the pipeline pressure and the medium flow velocity in the dynamic working condition parameters are substituted into a multiple regression linear model to generate reference torque and opening, and dynamic diagnosis of clamping stagnation abnormity is achieved by combining fusion calculation of a torque fluctuation coefficient and an opening deviation coefficient; through the correlation between the opening degree and the torque time sequence data and the traceability analysis of the current dynamic working condition parameters, the accurate positioning of the abnormal source is realized, and a systematic solution is provided for the state diagnosis and reliability evaluation of the fluid valve actuator.

The invention discloses a multi-modal dynamic fusion and incremental learning fault diagnosis method for deep vertical shaft equipment, which belongs to the technical field of industrial equipment fault diagnosis, and comprises the following four steps of: constructing a pre-training large model to perform feature extraction, and relying on a multi-layer Transformer encoder and a dual loss function, establishing a multi-modal dynamic fusion and incremental learning fault diagnosis model; mining cross-modal universal fault features from vibration, temperature and current multi-modal time sequence data; according to the method, multi-modal features are fused, multi-modal association is constructed, modal weights are dynamically adjusted through a modal gating unit and a time delay compensation attention mechanism to adapt to signal quality changes, and meanwhile time sequence deviation is corrected to achieve accurate association; incremental learning is realized by using a decoupling projection layer, and a lightweight projection module is designed for a newly added fault task to suppress disastrous forgetting; network training is optimized, pre-training loss, incremental learning loss and attention regularization loss are integrated through a multi-objective loss function, and model stability and diagnosis precision are improved. The method has the advantage that the model stability and the diagnosis precision are improved.

The invention provides a new energy power distribution fault diagnosis system and method based on multi-source data fusion, and relates to the technical field of power system fault diagnosis. The system comprises a data processing module, a fusion reasoning module, a graph structure diagnosis module and a control response module. The data processing module performs unified structured processing on the multi-source operation data to generate a feature tensor; the fusion reasoning module constructs a dynamic fusion weight based on the information entropy of each data source, and outputs a fusion confidence judgment result; the graph structure diagnosis module is combined with the distribution network topology to construct a graph structure model, and node-level fault positioning is realized through a graph neural network; and the control response module generates a control instruction based on the diagnosis result, and acquires and executes feedback to update graph structure attributes, thereby realizing a diagnosis closed loop. The method has the advantages of high diagnosis precision, strong adaptability, timely response and the like, and is suitable for intelligent fault identification and dynamic processing in a new energy power distribution scene.

The invention discloses a standardized centralized DTU multi-level fault diagnosis and self-healing method and system, and relates to the technical field of intelligent power grid fault diagnosis, and the method comprises the steps: building a dynamic topological graph according to a unified coding rule, extracting spatial-temporal characteristics through a graph convolution network, and obtaining a spatial-temporal characteristic power grid topological graph; building a power grid digital twin environment based on a multi-level standardized fault diagnosis result, generating a plurality of sets of self-healing strategy candidate sets by adopting a reinforcement learning algorithm, screening an optimal self-healing strategy, and converting the optimal self-healing strategy into a control instruction conforming to a substation automation standard protocol; the control instruction is sent to the DTU and analyzed, and the intelligent power switch cabinet is controlled to execute self-healing operation. By automatically correcting the abnormal diagnosis result, false alarm and missing alarm are reduced, and the accuracy of fault diagnosis is improved. The intelligent level of power grid management is obviously improved, the maintenance cost is reduced, and the operation efficiency and the service quality of a power system are optimized.

The invention provides an enterprise intelligent diagnosis method, system and device based on a large model and a medium, and belongs to the technical field of enterprise diagnos.The method comprises the steps that enterprise heterogeneous data are collected through a multi-source data interface, cleaning, feature extraction and cross-modal alignment fusion are carried out, and enterprise real-time data are obtained; constructing a knowledge graph through a graph attention network on the basis of an industry index to which an enterprise belongs, and updating association weights among nodes at regular time; inputting enterprise real-time data into the pre-trained multi-modal large model for preliminary analysis, and outputting a risk thermodynamic diagram; key abnormal indexes are identified from the risk thermodynamic diagram, sub-graphs related to the key abnormal indexes are extracted from the knowledge graph, structured prompt words are generated from the sub-graphs, then the structured prompt words and standardized enterprise real-time data are jointly input into a multi-modal large model for joint reasoning analysis, and then a visual diagnosis report is generated. Accurate identification and intelligent diagnosis of enterprise risks are realized, and decision-making efficiency is improved.

The invention discloses a permanent magnet motor demagnetization fault diagnosis method based on deep learning, and relates to the technical field of permanent magnet motor fault diagnosis, and the method comprises the steps: collecting the operation state data of a permanent magnet synchronous motor and a fan in real time through a sensor network, and generating a basic variable; multi-modal features in the basic variables are extracted, a health index sequence is constructed, and a demagnetization risk score is generated; setting a self-adaptive diagnosis opportunity control strategy, and determining the opportunity of next diagnosis detection according to the current demagnetization risk score; establishing a demagnetization risk prediction model, predicting a demagnetization risk score at the next diagnosis time, generating a dynamic risk threshold value, and judging whether a demagnetization early warning decision is triggered or not; allocating a maintenance task priority according to the current demagnetization risk score and the historical mode; comprehensively calculating the overall health index of the fan system, and predicting the residual life of the fan permanent magnet motor. According to the method, the problem of resource waste caused by unreasonable diagnosis time in the demagnetization fault diagnosis of the permanent magnet motor is solved.

The invention discloses a server health state diagnosis method based on a GAT-LP algorithm, and relates to the technical field of server health diagnosis, and the method comprises the steps: collecting multi-dimensional operation state data of a server, transmitting the multi-dimensional operation state data to a data analysis platform, and carrying out the preprocessing, inputting the preprocessed historical operation state data of the server into a GAT-LP network for training to obtain a GAT-LP network model, evaluating health conditions of the server and the process and generating an overall health condition according to a health state diagnosis result of the server and health score features extracted based on the GAT-LP network model, converting an evaluation result into a visual health state, and displaying the visual health state in the GAT-LP network model. And triggering an alarm mechanism when the health state of the server is lower than a preset threshold value. By constructing a multi-dimensional health assessment index system, the running state of the server is visually displayed, potential risks are found in advance, and prevention measures are made.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention relates to the technical field of servo motor fault diagnosis, in particular to a servo motor fault diagnosis method and system based on a neural network. The method comprises four steps of data acquisition and preprocessing, feature analysis and mode construction, real-time monitoring and anomaly detection, and fault diagnosis and feedback optimization. Servo motor operation data are synchronously collected through multiple sensors, features are extracted through a neural network model, and a fault diagnosis model is constructed. The state of the servo motor is monitored in real time, abnormity is recognized, the diagnosis result is optimized based on historical data, and the fault prediction accuracy is improved. The system comprises a data acquisition module, a mode training module, a monitoring and diagnosis module and a feedback optimization module which work cooperatively to realize intelligent fault diagnosis. The fault detection precision can be improved, the downtime of equipment is reduced, and the industrial production efficiency is improved.

The invention relates to the technical field of diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

The invention discloses a thermal power plant powder manufacturing equipment state remote diagnosis system, and relates to the technical field of powder manufacturing equipment state diagnosis, and the system comprises a data collection module which obtains the real-time state information of equipment; the edge calculation module is used for carrying out edge real-time analysis and detecting abnormal conditions; the data processing and analyzing module is used for identifying a fault mode and trend and dynamically adjusting a self-adaptive threshold value; the remote monitoring and diagnosis module is used for displaying equipment state, alarm and fault information; the maintenance and decision support module is used for providing maintenance and repair suggestions; and the security and authority management module is used for setting different levels of user authorities and carrying out data encryption. Through data acquisition and edge calculation, the system can monitor the equipment state in real time, identify and alarm abnormity in time, improve the safety and reliability of equipment operation, and utilize deep learning and time sequence analysis, the system can identify potential fault modes, optimize maintenance decisions, and reduce downtime and maintenance cost.

The invention discloses a relay protection device defect intelligent positioning method based on a knowledge graph, and relates to the technical field of intelligent power equipment fault diagnosis. The method comprises the following steps: data collection: collecting various data related to a relay protection device; a knowledge graph is constructed, a dynamic knowledge graph is constructed on the basis of collected data, the graph represents equipment information, defect types, maintenance records and the like in a node and edge mode, and when new data enter, the knowledge graph can be automatically adjusted and updated; and constructing an intelligent reasoning mechanism, automatically identifying possible defect reasons by analyzing information in the knowledge graph, and generating a defect positioning suggestion. By constructing the dynamic knowledge graph, the real-time monitoring data, the historical maintenance record and the environmental information of the sensor can be effectively integrated. The fusion of the multi-source data enables the system to comprehensively understand the operation state and potential problems of the equipment, thereby achieving higher-precision fault recognition.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention relates to the technical field of industrial equipment fault diagnosis, and provides an industrial equipment real-time fault diagnosis method and system based on a generative model, and the method comprises the following steps: collecting the original operation data of industrial equipment in real time through a plurality of sensors, carrying out the preprocessing of the original operation data, obtaining the real-time operation data, and carrying out the calculation of the real-time operation data; the preprocessing comprises abnormal value detection and standardization; constructing a generative model based on the real-time operation data, generating a normal data sample of the industrial equipment in a normal state through the generative model, and integrating the real-time operation data and the normal data sample to obtain a multi-dimensional data sample; performing feature extraction on the multi-dimensional data sample by using a deep learning model to obtain a fault diagnosis vector; performing fault mode identification on the fault diagnosis vector through a classification model to obtain a fault diagnosis result, and performing state judgment on the fault diagnosis result according to a preset threshold to obtain an industrial equipment state; and the state and the fault diagnosis result of the industrial equipment are displayed in real time through a dynamic monitoring interface. According to the method, the real-time performance and the overall efficiency of fault diagnosis are improved, and the loss caused by equipment faults can be effectively reduced.

A natural gas pipeline multi-working-condition fault diagnosis method and system based on Bayesian adversarial attack and single-source domain transfer, relating to the technical field of mechanical fault detection and diagnosis. The core of the method is using the transfer learning technology to solve the problem of insufficient generalization ability of existing deep reasoning models when processing pipeline fault diagnosis tasks under different working conditions. The method mainly comprises the following steps: constructing an attack sample generator on the basis of a Bayesian network, wherein the attack sample generator is used for generating, by adding delicately designed tiny disturbance into an input sample, an attack sample that can cause an reasoning model to make an incorrect decision, so as to mine and analyze a defect of the reasoning model; constructing a domain discriminator on the basis of the Bayesian network, wherein the domain discriminator is used for assist in generating a high-concealment attack sample by means of adversarial learning between the domain discriminator and the generator, that is, there is almost no visible difference between the high-concealment attack sample and an original sample; and constructing a classifier on the basis of the Bayesian network, and by expanding the distance between the attack sample and an original decision boundary of the reasoning model, constraining the posterior distribution of network parameters of the reasoning model to be adjusted towards a higher score of the attack sample, thereby enhancing the adaptability and robustness of the model when facing disturbance in different domains. By means of the steps, the present invention effectively solves the problem of missing reporting and false reporting risk improvement caused by poor generalization ability of traditional deep learning models under different working conditions.

The invention relates to the technical field of numerical control machine tool fault diagnosis, in particular to a numerical control machine tool fault diagnosis and active early warning system. The system comprises a data acquisition unit which is used for acquiring multi-dimensional original data generated in the operation process of the numerical control machine tool; the data processing and feature extraction unit performs feature extraction on the collected multi-dimensional original data based on an interactive feature construction algorithm; and the fault diagnosis unit constructs a classifier for distinguishing different fault types based on a random forest model, and realizes the judgment of the fault types through the random forest model based on the interactive feature vector # imgabs0 #. An interactive feature construction algorithm is adopted to perform interactive feature analysis on the preprocessed multi-dimensional original data, the problem of feature distortion caused by neglecting wear factors is avoided by introducing a real-time updated wear index, interference of external factors on feature analysis is eliminated by introducing an environment temperature correction item, and the accuracy of feature analysis is improved. And the actual reaction capability on the operation state of the equipment is improved.

The method is mainly applied to the technical field of power system fault diagnosis. The invention discloses an intelligent fault diagnosis method, device and equipment for a circuit breaker and a medium, and the method comprises the steps: updating a preset fault recognition model according to a historical data set, so as to enhance the incidence relation between data features and fault features; acquiring real-time operation data of the target circuit breaker, and performing feature extraction on the real-time operation data to obtain a plurality of data features; key data features are screened out from the multiple data features, and fault features corresponding to the key data features are determined based on the association relationship between the enhanced data features and the fault features; outputting a recognition result corresponding to the fault feature through the updated fault recognition model, wherein the recognition result comprises each fault type and a confidence coefficient corresponding to each fault type; and when the confidence coefficient of any fault type is greater than a preset threshold value corresponding to the fault type, sending out an alarm notification. According to the invention, the precision and efficiency of circuit breaker fault detection can be improved.

The invention discloses a multi-module collaborative rapid electrocardiogram automatic diagnosis method and system, and belongs to the technical field of automatic diagnosis. The method comprises the following steps: preprocessing an original ECG signal; the preprocessed ECG signals serve as input, and after the preprocessed ECG signals are sequentially processed by a lightweight convolution sub-module, a deformation convolution sub-module and a non-local attention sub-module, multi-level features containing local details and global time sequence dependency are output; fusing the multi-level features, and sending the fused multi-level features into a classifier to complete prediction of heart rhythm types to obtain prediction probabilities of all categories; a sliding window mode is adopted, the ECG signal fragments with the fixed length are sent to the diagnosis model for reasoning, the diagnosis model continuously outputs diagnosis results, and alarm or diagnosis prompt information is output in time according to the diagnosis results. According to the method, by designing a lightweight network structure, the parameter quantity and calculation complexity of a diagnosis model are effectively reduced, and real-time online ECG signal automatic diagnosis is realized on the premise of ensuring high diagnosis accuracy.

The invention discloses a current transformer real-time state diagnosis system based on Internet of Things, which relates to the technical field of electrical equipment state diagnosis and comprises a reference construction module, a disturbance excitation module, a modeling identification module, a driving unwrapping module, a self-adaptive setting module and a closed-loop control module, in the operation process of a secondary winding loop of the current transformer, a nanosecond time synchronization reference is established, a phase reference baseline is locked, current vector trajectories are continuously collected based on the time synchronization reference and the phase reference baseline, and a discrete phase residual image is constructed. According to the method, through high-precision time synchronization, phase disturbance excitation, sparse modeling identification, driving unwrapping and self-adaptive setting control, hysteresis oscillation of the secondary circuit of the current transformer is accurately identified and suppressed, the fault diagnosis accuracy and the system stability are remarkably improved, and the limitation of a traditional method in the aspects of identification sensitivity and response capability is broken through.

The invention provides a gas turbine exhaust temperature sensor fault diagnosis system and method, and relates to the technical field of industrial equipment state monitoring and diagnosis. The system comprises a sensor signal acquisition module for acquiring an original temperature signal; the signal preprocessing module is used for acquiring and preprocessing original signals and working condition parameters; the feature extraction and windowing module is used for acquiring data and calculating features; the data driving diagnosis module is used for receiving the time sequence characteristics and evaluating the health state; and the decision fusion module is used for comprehensively analyzing the multi-source information and making a final fault diagnosis judgment. According to the system, the accuracy and the reliability of fault diagnosis can be remarkably improved, the false alarm rate and the missing report rate are reduced, early warning and accurate identification of early weak faults of the sensor are realized, the adaptability and the robustness of the diagnosis system to variable working conditions of the gas turbine are improved, and the safety, the economical efficiency and the operation and maintenance efficiency of operation of the gas turbine are improved.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention discloses a method for diagnosing an IGBT open-circuit fault and a current sensor fault of a T-type three-level inverter, belongs to the technical field of inverter fault diagnosis, and is used for solving the problems of diagnosis and positioning when a three-level T-type inverter IGBT switch tube and a sensor have faults. Comprising the following steps: classifying and marking and numbering fault types of an open-circuit fault of a T-type three-level inverter IGBT and a fault of a current sensor; fault samples are collected and divided into a training set and a test set according to the proportion of 7: 3; a TCN-LSTM-SelfAction fault diagnosis model is constructed, and model training is carried out by using the training set; the trained model is used for evaluating the accuracy of fault type diagnosis of the training set and the test set. According to the diagnosis method, the fault types and the fault positions of the IGBT open-circuit fault and the current sensor fault can be diagnosed at the same time, the TCN and the LSTM are selected to be combined, local information and global relation of fault features can be captured at the same time, and meanwhile, the performance of a model is improved by combining a self-attention mechanism in the LSTM, and the accuracy is improved.