2248 results about "Fault recognition" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention provides a pump equipment state monitoring and fault diagnosis method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining a vibration signal of a target type of pump equipment based on a preset vibration sensor, and marking the vibration signal; extracting features of the vibration signal based on a preset dual-channel feature extraction model; iteratively training a preset basic fault diagnosis model based on the characteristics of the vibration signal until a preset training completion condition is reached; binding a preset number of fault diagnosis models to construct a pump equipment state reasoning model; acquiring an operation vibration signal of the pump equipment of the target category, inputting the operation vibration signal into the pump equipment state reasoning model, and outputting a fault category; through time-frequency dual-channel fusion and multi-scale perception, the fault identification precision is improved; the rationality and interpretability of the result are enhanced by using physical prior constraints; and through model integration optimization, the classification stability and reliability in a complex scene are improved.

The invention discloses a chemical process fault diagnosis method and system, and relates to the technical field of chemical process fault diagnos.The scheme aims at the diagnosis bottleneck of catalyst inactivation type progressive faults, fine parameter offset is captured in real time through a dynamic reference model, weak signals are accumulated and amplified in combination with an attenuation weighting mechanism, and the fault diagnosis accuracy is improved. The problem that a traditional method is not sensitive to slow drifting, and consequently report omission is caused is solved, and a fault recognition window is remarkably advanced. The dynamic threshold value is updated in real time based on mobile statistics, and raw material fluctuation and sensor noise can be self-adapted; during working condition switching, the model is automatically reset and the threshold value is relaxed, so that false alarm triggered by parameter mutation is avoided, and the stability of production scheduling is guaranteed; the design that fault half-life period weight and moving window length are associated with an inactivation period is introduced, so that the model autonomously adapts to different catalyst characteristics.

The invention discloses a photovoltaic power grid fault identification method and system based on circuit analysis, and relates to the technical field of fault identification, and the method comprises the following steps: obtaining the operation parameters of a photovoltaic power grid, and constructing a circuit analysis model; based on the circuit analysis model, equivalent response curves in different fault scenes are extracted, and the reference operation state is compared to generate a differential residual sequence; performing time-frequency joint decomposition on the differential residual sequence, and stripping photovoltaic output fluctuation from a load disturbance component to obtain a pure circuit characteristic component; based on the pure circuit characteristic component, a multi-dimensional characteristic coordinate space is formed, and the fault type is judged by using the dynamic bending rate of the fault response track; and mapping a fault type discrimination result back to the circuit analysis model, and positioning the position of a fault branch in combination with local disturbance distribution of the node impedance matrix. According to the method, pure circuit characteristic component extraction and multi-dimensional characteristic space dynamic analysis are combined, and accurate judgment of complex fault types and fault branch positioning are achieved.

The invention provides a wind turbine generator voiceprint fault recognition method, and relates to the technical field of wind turbine generator state monitoring and fault diagnosis, and the method comprises the steps: carrying out the noise reduction of an original audio signal through variational mode decomposition, screening a target mode of which the frequency, energy and kurtosis accord with features, and reconstructing the signal; extracting a Mel frequency cepstrum coefficient and a sensing noise robust coefficient, and generating multi-dimensional voiceprint data in combination with statistical characteristics such as a frequency spectrum gravity center, a spectrum entropy, energy, kurtosis and a zero-crossing rate; constructing a support set based on the prototype network, realizing small sample fault classification by calculating the Euclidean distance between the feature vector and the prototype vector, and outputting a preliminary result; judging whether the voiceprint is abnormal according to a preset threshold value, if so, storing the voiceprint into a dynamic abnormal voiceprint knowledge base; frequently occurring abnormal samples are manually labeled and added into a support set, the prototype network is retrained to update the model, and continuous optimization of the fault recognition capability is achieved.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

The invention discloses an on-load tap-changer vibration fault diagnosis algorithm based on tensor feature and adaptive weighted Stacking integration, relates to the technical field of on-load tap-changer fault diagnosis, and is used for improving the fault diagnosis precision. Comprising the following steps: S1, data acquisition; s2, feature extraction; the method comprises the following steps: extracting multi-scale time-frequency characteristics of an on-load tap-changer vibration signal by using wavelet scattering transform WST, and realizing low-rank decomposition and dimensionality reduction characterization of high-dimensional characteristics by combining a non-negative tensor decomposition model NTF; s3, fault diagnosis; a multi-base learner Stacking integration framework is adopted, and a prediction matrix is generated through K-fold cross validation; through a swarm intelligent optimization algorithm SRA, hyper-parameters and fusion weights of all base learners are adjusted, L2 regularization suppression over-fitting is introduced, and finally fault classification is realized by adopting a logic regression element learner with Softmax cross entropy. According to the invention, through fault diagnosis of multi-model adaptive fusion and optimization, the fault identification precision, stability and on-line monitoring capability are improved.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention discloses a power distribution network fault identification driving accurate isolation method based on an edge computing architecture, and relates to the technical field of power system automation, and the method comprises the following steps: S001, collecting voltage and current signals at each edge computing node of a power distribution network, analyzing the signal fluctuation amplitude and propagation time delay of different nodes based on the same fault event, and obtaining a fault event; and calculating a fault identification judgment difference degree between the nodes. According to the method, the identification credibility is quantified and abnormal node judgment is corrected through fault identification judgment difference analysis and topological constraint modeling; self-adaptive identification logic optimization is realized in combination with historical and real-time data; control priority and pre-simulation analysis are introduced, so that the feasibility and safety of the isolation action are improved; and finally, a closed-loop mechanism integrating recognition, simulation and feedback is constructed, and the fault response accuracy, coordination and operation stability of the power distribution network under a distributed architecture are remarkably improved.

The invention discloses a box-type transformer substation monitoring method based on unsupervised learning, and the method comprises the following steps: S1, collecting multi-source sensing data in the operation process of a box-type transformer, and generating a standardized input data set; s2, selecting a data sample in a normal operation state, constructing an unsupervised learning model, and obtaining a feature representation set; s3, inputting data, extracting current operation state characteristics, and recognizing a voiceprint abnormal state by combining outlier detection; s4, performing window sliding and statistical analysis on the time sequence data, and outputting a trend abnormal interval and an abnormal index type; s5, constructing a variable association graph structure, and identifying potential abnormal variables and propagation paths; s6, integrating the various types of abnormal information and the feature representation set, and constructing a voiceprint map library; and S7, integrating and processing the abnormal result and the voiceprint map database, and executing visual display and intelligent early warning. According to the invention, box transformer substation abnormity monitoring and early warning based on unsupervised learning are realized, and the fault identification accuracy and response efficiency are improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

The invention relates to a cable detection device which comprises a first clamping part, a load applying mechanism and a real-time on-off monitoring module, the first clamping part is used for fixing one end of a cable, and the load applying mechanism is arranged on the far side of the first clamping part and used for driving the other end of the cable to generate axial stretching displacement and axial torsion angular displacement in the testing process. The real-time on-off monitoring module is electrically connected with the wire harnesses at the two ends of the cable, and is used for continuously outputting a conduction state signal in the loading process. Through the above structure combination, the cable detection device can simulate the complex composite load borne by the cable in actual use, realizes line-by-line on-off real-time monitoring of each conductor in the cable, and effectively improves the working condition reduction degree and fault identification precision of detection.

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

The invention belongs to the technical field of low-voltage switch cabinet operation control, and particularly discloses an intelligent low-voltage switch cabinet system based on a building intelligent function, which comprises an operation sensing module, a spatial topology module, a fusion diagnosis module, a fault positioning module and a dynamic protection module, current and insulation resistance are synchronously detected at a circuit wiring terminal of the low-voltage switch cabinet, temperature and humidity distribution data of the surrounding environment are collected, and then coupling characteristics between the current and temperature rise are fused. And carrying out visual display of coupling and time sequence correlation in the built three-dimensional heat-electricity correlation topological graph in the switch cabinet by utilizing the time sequence correlation between the insulation resistance and the humidity change, so as to realize comprehensive identification of potential faults. According to the method, the sensitivity and the accuracy of fault identification are effectively improved, early fault symptoms such as poor contact and insulation degradation can be found earlier, the early warning capability of the system is enhanced, and the safety and the stability of operation of the switch cabinet are guaranteed.

The invention relates to the technical field of intelligent diagnosis of power equipment, and provides a multi-mode fusion extra-high voltage converter transformer fault diagnosis method and system, and the method comprises the steps: collecting a multi-mode signal from a sensor of an extra-high voltage converter transformer, and classifying the signal into a plurality of modes; performing intra-modal feature reinforcement learning on the multi-modal data by adopting a SimCLR framework to obtain a discriminative representation feature vector zm of each modal; inputting the zm into a Transform branch encoder of a corresponding mode, and obtaining context feature vector enhancement representation hm of each mode; mapping the hm of different modalities to the same dimension in a unified manner, and performing weighted attention fusion to obtain feature vectors zf of all modalities after fusion; and inputting a multi-layer perceptron classifier (MLP) to obtain a fault category prediction result of the extra-high voltage converter transformer. According to the method, the multi-mode signals are processed and fused in parallel, and the fault recognition capability of the model on the extra-high voltage converter transformer in the complex operation state is improved.

The embodiment of the invention provides a service exception root cause positioning method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time node topological graph corresponding to a target service in response to the detection of the exception of the target service, the real-time node topological graph at least comprising a plurality of nodes; acquiring a real-time node weight corresponding to each node; positioning a first candidate node from the nodes based on the real-time node weight; performing reverse traversal along the topological edge of the first candidate node to obtain a first fault propagation path; the root cause analysis is performed according to the first fault propagation path to obtain the abnormal root cause corresponding to the target service, the limitation of single service is broken through based on the total service calling topology, the accuracy of cross-layer fault identification is improved, the omission rate is reduced, the node weight is introduced, processing is performed according to the real-time node weight of the node, and the fault identification efficiency is improved. Therefore, the fault positioning accuracy is improved, and the fault identification efficiency is improved.

The invention discloses an optical fiber vibration signal time identification and classification method, system and device based on a Mam-YOLOv10 neural network and a medium, and belongs to the technical field of optical fiber communication, and the method comprises the steps: obtaining OTDR signals of a plurality of targets based on an optical fiber vibration signal collection system, and carrying out the denoising of the OTDR signals; performing short-time Fourier transform on the de-noised OTDR signal to obtain a two-dimensional time-frequency diagram of the vibration signal; based on the two-dimensional time-frequency diagram, multi-scale features of the optical fiber vibration signals are extracted; and inputting the multi-scale features into a Mam-YOLOv10 neural network containing a Mama attention block to complete identification and classification of the optical fiber vibration signals. According to the method, the influence of interference fading on the pattern recognition accuracy is effectively reduced, meanwhile, the hardware complexity is reduced, the attention weight is automatically adjusted according to different fault scenes and data characteristics, and the accuracy of optical fiber fault recognition and classification is improved.

The invention relates to the technical field of battery management, and discloses a high-voltage lithium battery cluster voltage balance control method and system for rail transit. The method comprises the following steps: acquiring battery voltage and tunnel environment parameters, and obtaining environment characteristic data through Kalman filtering; predicting a voltage deviation trend through LSTM in combination with a UPS load rule; calculating a dynamic equilibrium threshold according to the predicted value; when the voltage difference value exceeds a threshold value, the bidirectional energy transmission equalization circuit is started; and monitoring the battery state change in the equalization process, and outputting a maintenance instruction through a fault recognition algorithm. The problem that battery balance control in a rail transit UPS system cannot adapt to complex environments and dynamic load changes is solved, and the precision and the intelligent level of battery cluster voltage balance control are improved.

The invention discloses an unmanned aerial vehicle inspection system and method for a super-large-scale photovoltaic power station, and relates to the technical field of substation inspection, and the system comprises a multi-rotor unmanned aerial vehicle group deployment module, a honeycomb scanning network construction and three-dimensional radiation thermograph generation module, an inspection path dynamic planning module, and a photovoltaic panel inclination angle optimization path optimization module. The fault probability analysis module is used for collecting data through electroluminescence and infrared thermal imaging and generating a fault distribution diagram by fusing a historical defect feature library, the hidden fault recognition module is used for dynamically recognizing fault types in combination with multi-source data and flight instructions, and the intelligent charging docking module is used for carrying out intelligent charging. And adjusting a return flight threshold according to the fault level and battery attenuation and triggering a charging mechanism. The technical problems that in the prior art, a large-scale photovoltaic power station is low in inspection efficiency, insufficient in fault detection precision and poor in environmental adaptability are solved, and the technical effects of improving the inspection efficiency, the fault detection precision and the environmental adaptability through cooperative inspection of the unmanned aerial vehicle group are achieved.

The invention relates to the technical field of obstacle prediction, and provides a vehicle fault real-time diagnosis method based on multi-source heterogeneous data and a dynamic stream processing system, and the method comprises the steps: obtaining multi-source heterogeneous operation data from a vehicle sensor, a vehicle-mounted bus and an external environment; based on historical fault samples, determining a parameter critical value interval of each fault for each historical fault sample through reverse reasoning, and constructing a fault critical value library; performing dynamic streaming processing on the multi-source heterogeneous operation data, extracting time sequence features, comparing the time sequence features with a fault critical value library in real time, and outputting early warning through a multi-source voting mechanism; and when the operation data is close to or exceeds the corresponding critical value interval, outputting a fault prediction result, and triggering a dynamic updating mechanism to correct the critical value library. According to the method, the accuracy, timeliness and interpretability of fault identification are improved, meanwhile, the system resource consumption and real-time requirements are considered, and the method is suitable for long-term health state monitoring of large-scale vehicles.

The invention relates to the technical field of switch cabinet fault monitoring, and discloses a switch cabinet partial discharge intelligent monitoring system for fault identification. The system comprises a signal dynamic capture layer, a feature reconstruction mapping layer, a heterogeneous data collaboration layer and a self-adaptive diagnosis decision layer. The signal dynamic capture layer obtains a multi-source partial discharge signal based on a spatial perception mechanism, and generates a full-dimensional discharge intensity distribution spectrogram; the feature reconstruction mapping layer deploys a distributed electromagnetic sensing array according to a high-risk area, and generates an insulation defect three-dimensional positioning map through a directional detection pulse and phase analysis algorithm; the heterogeneous data collaboration layer establishes space-time association, and after time reference is aligned, comprehensive risk confidence is generated through a multi-channel fusion network; and the adaptive diagnosis decision layer converts the comprehensive risk confidence into an executable monitoring instruction set, and issues the executable monitoring instruction set to an edge computing unit through an industrial internet of things protocol. According to the system, omnibearing monitoring and accurate diagnosis of partial discharge of the switch cabinet are realized, and the fault identification and response capability is improved.

The invention relates to a helicopter rotor crack fault identification method and device based on video semantic segmentation. The method comprises the steps that video collection and denoising and stability enhancement processing are carried out through an unmanned aerial vehicle; through designing a global-local feature interaction double-branch network, parallel computing and bidirectional fusion are carried out on context global feature extraction branches and detail mining branches, so that rotor wing crack feature extraction is realized; designing a hierarchical crack feature reconstruction decoder to realize cross-scale fusion and spatial precise alignment of crack features; and designing a multi-frequency-domain boundary sensing enhanced training head, and realizing multi-scale feature extraction and dynamic weight distribution of the crack edge through the synergistic effect of a multi-stage wavelet frequency domain decomposition module and a self-adaptive boundary weighting supervision module. Through the innovative modules, high-precision and robust crack detection is realized aiming at the problems of view limitation, global-local feature fusion difficulty, detail loss caused by down-sampling, edge blur, class imbalance and the like in helicopter rotor crack detection.