22 results about "Diagnostic algorithms" patented technology

The application discloses an out-of-hospital hierarchical diagnosis method based on IBS diseases, and relates to the technical field of medical diagnosis, and comprises the following steps: S1: acquiring multi-dimensional information related to IBS diseases provided by a patient in an out-of-hospital scene; S2: performing standardization preprocessing on the out-of-hospital multi-dimensional information to obtain feature data that can be input into an algorithm model; and S3: inputting the standardization feature data into a preset IBS hierarchical diagnosis algorithm model to output an IBS disease hierarchical diagnosis result of the patient. The out-of-hospital hierarchical diagnosis method based on IBS diseases integrates multi-dimensional out-of-hospital information, eliminates data errors by combining standardization preprocessing, adopts multi-feature screening and model optimization, references the Rome IV diagnostic standard to train the model, simultaneously judges the diagnosis reliability through the confidence, reduces the misdiagnosis and missed diagnosis rate, the model iteration updating mechanism can continuously improve the diagnosis accuracy, and adapts to the change of clinical requirements.

The application discloses a kind of T type three-level converter open-circuit fault diagnosis method with strong robustness, belong to the field of power electronic equipment fault diagnosis, to distinguish different switch similar open-circuit fault characteristics and accurately locate open-circuit fault tube.Considering different modulation strategies and modulation modes, the phase voltage residual is generated through the phase voltage model, so as to extract the fault characteristics of the converter under various open-circuit faults.Analyze the influence of factors such as parameter error, dead time and delay time of the system on the phase voltage vector residual, and the adaptive residual vector amplitude threshold based on uncertainty propagation theory.A hierarchical fault diagnosis scheme is proposed, which can locate the group-level open-circuit fault according to the voltage residual vector amplitude and angle, switch the three-level modulation mode of all phases of the converter to two-level mode, to further identify the specific fault switch from the fault group.A reasonable balance between robustness and speed can be achieved, and the complexity and adaptability of the diagnosis algorithm are fully considered.

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 present application relates to a kind of fuel cell quick fault diagnosis method and device, belong to fuel cell field.Therein, the method includes executing fuel cell initialization protocol and reading fuel cell operating characteristic parameter;Based on fuel cell operating characteristic parameter, construct fault feature identification system, fault feature identification system is used for thermodynamic boundary anomaly detection, air filter blockage fault feature identification, valve leakage diagnosis and air compressor performance attenuation diagnosis, wherein valve leakage diagnosis fusion temperature difference self-adaptive correction protocol, temperature difference self-adaptive correction protocol is triggered by compensating inlet medium flux.This application realizes through integrated monitoring system and intelligent diagnosis algorithm, realizes the quick, accurate judgment to the cause of failure, to improve the reliability and maintenance efficiency of fuel cell system.

This invention discloses a green building energy consumption intelligent monitoring and diagnosis system based on multi-source data fusion, belonging to the field of building energy management technology. It employs an improved Transformer-LSTM model and dynamic window alignment technology to solve the problems of time synchronization and missing values ​​in heterogeneous data, thus improving the input quality of the diagnostic algorithm. Through device-level energy consumption decomposition and multimodal root cause localization, it improves the accuracy of anomaly diagnosis and can accurately locate faulty equipment. Policy generation based on GNN behavior perception and PPO reinforcement learning reduces overall energy consumption while ensuring comfort. The system has data closed-loop management capabilities, achieving continuous self-learning through reverse annotation of diagnostic results and dynamic threshold optimization. Three-dimensional visualization and an interactive interface significantly improve operation and maintenance efficiency. This system significantly enhances the accuracy, automation level, and economic benefits of building energy consumption management.

The application discloses a kind of reactor special electrical equipment online monitoring method and system, it is related to monitoring system technical field, including obtaining temperature data, partial discharge data and pressure data generated when electrical penetration equipment operates;According to the data generated when operating, process and obtain corresponding temperature distribution chart, partial discharge trend chart, PRPD spectrum chart, PRPS spectrum chart and pressure change trend chart;Build the multi-modal fusion fault diagnosis algorithm including feature extraction module, temperature analysis module, pressure analysis module, partial discharge analysis module and comprehensive evaluation module;Temperature distribution chart, partial discharge trend chart, PRPD spectrum chart, PRPS spectrum chart and pressure change trend chart are analyzed using multi-modal fusion fault diagnosis algorithm, obtain the fault condition of electrical penetration equipment;For solving the problem that traditional monitoring function is single, and the accuracy of state diagnosis result is low.

The application relates to a test simulation verification platform for reusing on-board diagnosis algorithms, which comprises the following modules: a diagnosis program generation module, which automatically generates an on-board diagnosis program according to diagnosis knowledge; a diagnosis program simulation module, which loads the on-board diagnosis program, receives telemetry data and performs fault diagnosis; a data playback module, which plays back historical data records required for test verification; and a test verification module, which compares real and detected fault labels and analyzes diagnosis accuracy. The application provides a test simulation verification platform for reusing on-board diagnosis algorithms, realizes automatic updating and deployment to verification, and improves the test verification efficiency of on-board diagnosis programs.

The application discloses a multi-scale coupled heavy pollution weather forecasting and early warning method and system, and belongs to the technical field of weather forecasting and early warning; the method comprises the following steps: S1, receiving and standardizing multi-source heterogeneous data from each observation platform, fusing the multi-source heterogeneous data by using a data assimilation algorithm, and reconstructing a thermal-dynamic-pollution field three-dimensional analysis field; S2, based on the fused data and the analysis field, running a 'weather scale forcing W-mesoscale response M-microscale triggering mu' coupling diagnosis algorithm, quantifying key indexes of each scale, and establishing an early warning index library; S3, according to W-M-muChain concept model output, automatically generating a series of progressive early warning products from weather scale potential prediction to microscale near warning. Thus, accurate and seamless monitoring and early warning of the heavy pollution process are realized.

The application relates to the technical field of electromagnetic simulation, and discloses a resolver complex working condition signal simulation method, which can accurately simulate the output signals of a resolver under normal, static eccentricity, dynamic eccentricity and mixed eccentricity and other complex working conditions by establishing an air gap model, a magnetic guide model and a mutual inductance calculation model, noise analysis can be introduced, and the visualization of signal characteristics is realized; the application solves the problem that the existing resolver simulation technology cannot comprehensively simulate complex working conditions, provides accurate data sources for the development of a resolver fault diagnosis algorithm and performance testing, has the characteristics of high simulation accuracy, comprehensive working condition coverage and strong scalability, provides reliable simulation data sources for the development of a resolver fault diagnosis algorithm and performance testing, reduces experimental cost, and the application supports flexible configuration of parameters, and parameters such as eccentricity and noise level can be adjusted according to actual requirements.

This invention belongs to the field of industrial equipment condition monitoring and fault diagnosis technology, specifically a method and system for intelligent diagnosis and leak location of vacuum degree in a vacuum heat exchanger. It includes a heat exchanger shell, a data acquisition layer, an intelligent analysis layer, and an execution feedback layer. The data acquisition layer is used to collect absolute pressure signals, temperature distribution signals, and acoustic vibration signals. The intelligent analysis layer is used to run prediction models, diagnostic algorithms, and location algorithms. The execution feedback layer is used to perform maintenance compensation and alarm functions. The heat exchanger shell is equipped with a set of flange seats. Feedthrough flanges are connected to the flange seats. Sensing elements are fixedly connected to the feedthrough flanges. By integrating a sensor array and combining the prediction model, diagnostic algorithm, and location algorithm of the intelligent analysis layer, online real-time monitoring and intelligent diagnosis of the heat exchanger vacuum degree can be achieved. Accurate early warnings of minor leaks can be provided without equipment shutdown, distinguishing between normal pressure fluctuations and abnormal leaks.

The application discloses a power plant equipment real-time fault early warning method based on an AI diagnosis algorithm, relates to the technical field of power diagnosis, and comprises the following steps: collecting multi-source signals, interpolating and aligning the signals and filtering noise to form a unified operation data frame; extracting a change rate, a fluctuation amplitude and statistical characteristics based on the unified operation data frame, identifying a working condition and normalizing characteristics by using a finite state machine, and generating a structured characteristic frame; inputting the structured characteristic frame into a double-layer LSTM time sequence AI diagnosis model, performing online inference and order-preserving calibration, and outputting a predicted state set; positioning a potential fault mode through the predicted state set, combining a comprehensive verification score calculated based on resonance characteristics, and generating a disposal record set; and performing adaptive learning and parameter updating through the disposal record set. Through the design of the double-layer LSTM time sequence AI diagnosis model and the combination of the online order-preserving calibration algorithm, trend prediction and abnormal confidence adaptive quantization of key operation parameters of the power plant are realized.

The application discloses a diagnosis system and method based on transformer oil state multi-parameter monitoring, which mainly utilizes a transformer oil state multi-parameter sensor array to monitor the following multi-parameters of the original signals of the transformer oil in the transformer in real time: oil level, oil temperature, characteristic gas concentration and air pressure, and sends the processed data to an edge computing terminal in a wireless or wired manner; the edge computing terminal performs real-time diagnosis and early warning on the sudden fault type and severity of the transformer based on a diagnosis algorithm engine, so that the precise prediction of the sudden fault state of the transformer is realized.

This application discloses a lossless self-healing method and system based on FTTR digital twins and distributed prediction. The method includes: maintaining digital twins on both the FTTR master gateway and FTTR sub-gateways, employing a synchronization mechanism combining periodic full-scale and event-triggered incremental synchronization; the FTTR sub-gateways using a lightweight fault prediction model to infer in real time, predicting software faults and reporting them; the master gateway coordinating the digital twins of the faulty gateway and its neighboring gateways to enter a virtual sandbox environment, where each twin instance runs diagnostic algorithms in parallel, reaching consensus on the root cause of the fault based on a majority voting mechanism, and using a virtual clock to accelerate the deduction of the optimal repair solution; the faulty gateway performing minimally invasive repair operations while maintaining uninterrupted data plane forwarding; and the cloud optimizing the model through federated learning and distributing it. This application achieves zero service interruption during the fault repair process, improving overall system performance and diagnostic accuracy.

This invention relates to the field of fault location technology, and more particularly to a fault location sensor for pole-mounted circuit breakers. The technical solution is as follows: A fault location sensor for pole-mounted circuit breakers includes: a sensor module for synchronously acquiring multi-physical quantity signals at the installation point, wherein the multi-physical quantity signals include at least two of high-frequency current signals, power frequency current / voltage signals, electric field / magnetic field signals, and temperature signals; and a processing module connected to the sensor module for performing on-site diagnosis and intelligent analysis of faults in the pole-mounted circuit breaker or its associated lines based on the synchronously acquired multi-physical quantity signals and by running a built-in diagnostic program. This invention effectively distinguishes various fault events by integrating multi-dimensional information such as high-frequency traveling waves, power frequency electrical quantities, electromagnetic fields, and temperature, and using a built-in intelligent diagnostic algorithm for comprehensive judgment, avoiding ineffective inspections and accurately guiding maintenance personnel to the actual fault point.

The application discloses a kind of railway positioning test method and system based on multi-source information, which comprises the following steps: multi-source test system plays analog satellite positioning signal, auxiliary positioning instrument terminal carries out positioning consistency test to train positioning, auxiliary positioning instrument terminal designs inertial navigation smoothing correction algorithm, corrects train inertial navigation information, completes initial positioning test, multi-source test system plays analog satellite positioning signal and spoof satellite positioning signal simultaneously, auxiliary positioning terminal designs spoof diagnosis algorithm, and completes final positioning test;The scheme realizes the positioning test of railway train by using multi-source information, judges the accuracy of railway train positioning by calculating consistency test coefficient, and designs inertial navigation smoothing correction algorithm to eliminate the deviation of inertial measurement unit. When performing final positioning test, a spoof diagnosis algorithm is designed to diagnose the spoof satellite positioning signal, improve the positioning accuracy of railway train, and further improve the accuracy of the entire positioning test method.

The application relates to the field of avionics fault diagnosis, and discloses a multi-parameter intelligent diagnosis algorithm for evaluating the quality of an aircraft power supply, which receives loop voltage and current and bus control instruction data in parallel, constructs time-aligned heterogeneous data streams based on a clock, and locks key load action events by using double trigger logic. By calculating equivalent Thevenin source impedance and constructing a background noise model, the adaptive reference waveform is generated by correcting the load standard model. The measured and reference waveforms are aligned by using dynamic time warping, the deformation cost is output, and the energy spectrum entropy is extracted by performing wavelet packet decomposition on the differential residual. The multi-dimensional feature vector is constructed by comprehensively considering impedance drift, deformation cost and energy spectrum entropy, and the mechanical fault, electrical contact degradation and line aging are decoupled by using hierarchical logic. The application effectively eliminates background interference and time jitter, and realizes accurate evaluation of the state of the power supply system.

This invention discloses an intelligent analysis method for the impact of oxygen flux on the coal combustion process and its judgment indicators. The method includes: collecting real-time dynamic data of oxygen flux, coal quality characteristic parameters, combustion environment parameters, and correlation parameters of the judgment indicators, and constructing a multi-dimensional data acquisition matrix; subsequently, using a dedicated kinetic model to establish a dynamic correlation between oxygen flux and combustion reaction rate and mass transfer efficiency; achieving quantitative analysis of oxygen efficiency through a multi-factor collaborative algorithm; identifying combustion state parameters based on an intelligent diagnostic algorithm; and then integrating the aforementioned analysis results using a multi-characteristic calculation system to complete the quantitative calculation of the impact of oxygen flux on combustion judgment indicators. Finally, a nonlinear correlation model is constructed to achieve comprehensive intelligent analysis. This invention comprehensively improves the energy utilization efficiency and scientific regulation of coal combustion systems, adapting to the actual needs of industrial intelligent transformation and energy conservation and emission reduction.

The application discloses a vanadium-doped graphene / molybdenum ditelluride heterojunction-based sulfur hexafluoride decomposition gas sensing chip and system, and relates to the technical field of nanomaterials and smart grid state sensing. The core sensitive material of the sensor is a heterojunction formed by a vanadium-doped graphene layer and molybdenum ditelluride (MoTe2), which can specifically identify and quantify four key decomposition products of sulfur hexafluoride generated under electric heating faults: sulfur dioxide (SO2), hydrogen sulfide (H2S), fluorosulfinyl (SOF2) and fluorosulfonyl (SO2F2). The active sites introduced by vanadium doping and the heterojunction interface synergistic effect significantly improve the gas molecule adsorption and charge transfer efficiency, realizing room temperature high-sensitivity detection of trace decomposition components. Combined with intelligent diagnosis algorithm, the system can accurately identify different fault types such as discharge and overheating inside the equipment according to the type and concentration distribution of the decomposition gas, and issue a graded warning.

The application relates to the field of artificial intelligence medicine, in particular to a chronic precancerous lesion recognition method based on a topological multi-scale graph neural network, which first collects a multi-spectral endoscope image under a gastroscope and carries out pretreatment to construct a multi-channel feature map; subsequently, instance segmentation is carried out on glandular structures to form a gland point cloud set, a topological persistent graph is constructed by using a persistent homology theory, and topological invariant features are calculated; on this basis, a multi-scale gland topological graph is constructed, feature aggregation and updating are carried out through a graph attention mechanism; finally, related features are fused to input a classifier, and a lesion risk grade and positioning result are output, and a loss function is used for optimization. By fusing the persistent homology theory in algebraic topology and the multi-scale graph neural network, a gastroscope auxiliary diagnosis algorithm conforming to biological pathological features is constructed, and accurate recognition and risk grading of chronic precancerous lesions are realized.

The application relates to an elevator bus diagnosis system and method based on firmware reconfiguration, and relates to the field of elevator bus diagnosis, and comprises a three-layer architecture composed of a mobile interaction layer, a firmware service layer and a reconfigurable diagnosis layer; the mobile interaction layer provides a graphical diagnosis interface and firmware loading control; the firmware service layer is used for warehouse management, version maintenance and security check of diagnosis firmware; and the reconfigurable diagnosis layer dynamically reconfigures special diagnosis equipment of multiple bus types by loading different diagnosis firmware. The application can realize one machine with multiple uses through firmware reconfiguration, supports on-site wireless updating of diagnosis algorithms, effectively solves the technical problems of strong coupling between diagnosis equipment and bus types and difficulty in updating diagnosis algorithms in the prior art, and improves the practicality, environmental adaptability, operation stability and adaptation capability of elevator bus diagnosis.

This invention discloses a transformer winding fault simulation device and decoupling diagnosis method based on a multidimensional deformation manipulator, belonging to the field of power equipment condition monitoring technology. The device utilizes servo micro-motion excitation technology, applying static deformation to the winding while simultaneously superimposing dynamic micro-disturbances via a multidimensional manipulator, physically reproducing permanent structural damage and potential mechanical loosening; simultaneously, it constructs inter-turn short circuits using an electrical matrix. Based on this device, this invention proposes an electromechanical parameter decoupling diagnosis algorithm: by acquiring the oscillation wave response of the winding under micro-motion disturbances, it extracts the static corrected Hausdorff distance (MHD) characterizing geometric distortion and the dynamic phase space breathing rate (HDR) characterizing structural stability; it introduces a sensitivity decoupling matrix based on physical truth calibration, inverting the mixed features into the equivalent inductance change ΔL and the equivalent resistance change ΔR. This invention effectively solves the problem of traditional methods' difficulty in distinguishing between mechanical loosening, permanent deformation, and electrical short circuits, achieving accurate quantitative diagnosis of composite faults.