66 results about "Diagnostic algorithms" patented technology

The invention provides a power dispatching data asset full life cycle management method and system. The method comprises the following steps: firstly, collecting power grid real-time operation basic data, calculating a power grid risk grade by means of a power grid risk assessment model, formulating a matching collection strategy according to the grade, and collecting to obtain initial data assets; recognizing and processing abnormal data through a collaborative quality diagnosis algorithm in combination with the real-time state of the power grid, and screening effective data assets; and inputting into a power dispatching decision algorithm, and fusing risks and constraints to generate a power dispatching scheme. And then comprehensively evaluating the scheme by using the data asset utility index, analyzing and determining contribution data assets with remarkable scheduling contribution, and finally dynamically adjusting acquisition and decision algorithm parameters according to an evaluation result and the contribution data assets. According to the invention, full-process collaborative management and control of power dispatching data assets are realized, and the data utilization rate and the scientificity and reliability of dispatching decision are improved.

The invention discloses a historical building repairing method and system based on multi-source data fusion and AI diagnosis, and belongs to the technical field of historic building repairing. According to the method, the multi-source data of the historical building are integrated, the digital twinborn model of the historical building is constructed, defect identification and state evaluation are performed on the digital twinborn model based on the AI diagnosis algorithm, the repairing scheme of the historical building is generated, the repairing effect is fed back in real time through the digital twinborn model, the multi-source data cover full dimensions, and the repairing efficiency is improved. The model restores details of building components and can simulate structural deformation under the load effect and material attenuation caused by the environment, real-time data mapping enables the model to synchronously reflect the state of a solid building, defect danger coefficients are calculated through multi-parameter fusion, the types and positions of defects such as structural deformation and material degradation can be automatically recognized for generation of differentiated repairing schemes, and the method has the advantages of being high in practicability and easy to popularize. Hidden defects are prevented from being missed in manual detection, risk quantification is achieved by combining defect danger coefficients with weight distribution, and the generated scheme better fits the actual defect condition of historical buildings.

The invention relates to the field of electric reactor fault diagnosis, in particular to an electric reactor mechanical fault diagnosis method based on parameter adaptive optimization, which comprises the following steps: preprocessing vibration signals generated when an electric reactor has a mechanical fault through a wavelet threshold denoising method, extracting vibration signal characteristics through Fourier transform and wavelet packet transform, and calculating the mechanical fault of the electric reactor according to the vibration signal characteristics; according to the method, the high-voltage electric reactor is subjected to fault diagnosis, screening is carried out through a random forest method, fault diagnosis is mainly carried out through a convolutional neural network-Transform hybrid model (CNN-Transform), and accurate diagnosis of the fault of the high-voltage electric reactor is realized by utilizing the local and structured feature extraction capability of the CNN model and the global context and long-distance dependency relationship establishment capability of the Transform model. And an improved dung beetle optimization algorithm (MDBO) is introduced, internal parameters of the hybrid model are dynamically adjusted, parameter adaptive optimization is realized, and the diagnosis accuracy is improved.

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 provides a real-time diagnosis and life prediction method based on edge computing, and relates to the technical field of extreme charging, which comprises the following steps: collecting electrochemical characteristic data in the process of extreme charging of different types of batteries, extracting electrochemical difference characteristics, and constructing a battery type distinguishing model. Based on the battery type distinguishing model, the influence of dynamic working condition data on battery aging is analyzed to obtain the working condition influence. Based on the current recognition result, the basic health data is calculated by the health diagnosis algorithm, and the current health status is evaluated to obtain the health evaluation data. Based on the health evaluation data, a life prediction model is constructed to predict the current battery life and output the remaining life prediction value. The application improves the accuracy and safety of battery management in the extreme charging scene of electric two-wheeled vehicles, realizes adaptive real-time diagnosis of health status, and considers the influence of working conditions on the remaining life prediction.

The invention relates to a detection method of a driving motor for a new energy automobile in the technical field of detection of the driving motor for the new energy automobile. The detection method comprises the following steps: a dynamic excitation step: applying composite dynamic excitation to a detected driving motor; and a follow-up synchronous acquisition step: under the action of the composite dynamic excitation, controlling a detection execution mechanism to move relative to a shell of the detected driving motor according to a preset track, and synchronously acquiring a vibration signal, an acoustic signal and an electrical signal of the detected driving motor. According to the method, through sequential composite excitation and benefit from high efficiency of follow-up synchronous acquisition and rapid reasoning of a fusion diagnosis algorithm, the whole process from excitation to diagnosis report output can be strictly controlled within 90 seconds, and the problem that traditional subentry detection is low in efficiency is fundamentally solved.

The invention discloses a working condition self-adaption-based guide vane connecting rod stress trend diagnosis method, and relates to the technical field of intelligent diagnosis of hydroelectric generating sets, and the method comprises the steps: constructing an optical fiber stress monitoring system, setting an optical fiber grating sensor for a guide vane connecting rod, collecting a stress signal in real time, and collecting operation parameters; the method comprises the following steps: collecting multi-working-condition data within one month of normal operation after overhaul of a unit, calculating a correlation matrix between guide vane connecting rod stress and operation parameters, and establishing a stress working condition self-adaptive analysis model; and calculating a maximum value, a minimum value, an average value and a standard deviation of stress based on rolling statistics, determining a normal range of stress fluctuation, inputting real-time acquired data into a diagnosis algorithm, judging a stress deviation trend according to the reference model, outputting an alarm level, and updating the reference model. According to the method, the accuracy and timeliness of anomaly recognition are improved, the pertinence and the performability of maintenance decision making are enhanced, and the operation safety and the service life management effect of the guide vane connecting rod of the hydraulic unit are improved.

The invention relates to the technical field of water turbine maintenance, and particularly discloses a finite element intelligent maintenance scheduling system for a water turbine runner blade. According to the system, a stress-deformation coupling model of a runner blade is established through finite element numerical simulation, and a dynamic damage evaluation model is established in combination with real-time monitoring data; predicting a crack propagation trend by using an intelligent diagnosis algorithm, and generating a hierarchical maintenance decision; through a resource scheduling optimization module, maintenance personnel, spare parts and equipment resources are matched, a scheduling priority queue based on multi-dimensional constraints is established, and dynamic balance of maintenance tasks and resource allocation is realized. The system can effectively improve the crack detection precision, remarkably shorten the non-planned downtime and optimize the spare part inventory turnover rate, and has outstanding economic benefits and engineering application values.

The invention discloses an intelligent bearing state monitoring and self-maintenance device based on multi-sensor fusion, and relates to the technical field of industrial equipment state monitoring and fault diagnosis. Comprising a bearing body, a sensing module, a control module and an execution module, the bearing body comprises a bearing inner ring, a bearing outer ring, a rolling body and a retainer; the sensing module is integrated on the outer side face of a bearing outer ring or the inner wall of a bearing mounting hole of a robot joint shell. The control module is packaged in the oil-resistant miniature sealing shell and fixed to the inner wall of the robot joint shell. And the execution module is integrated in the sealing shell and is communicated with the raceway area of the bearing body through a conveying pipeline. A multi-source diagnosis algorithm fusing time domain kurtosis, frequency domain envelope spectrum characteristics and temperature trend is executed through a microprocessing unit, normal vibration fluctuation caused by sudden load change and real abnormity caused by lubrication failure or material stripping are effectively distinguished, and the problems of high false alarm rate and automatic lubricating oil compensation under complex working conditions are solved.

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 invention relates to the technical field of equipment state monitoring, in particular to a method and system for monitoring the running state of coal-based alcohol-ammonia co-production equipment, and the method comprises the steps: data collection: collecting multi-source data in the running process of the equipment in real time; preprocessing data, eliminating noise interference through weighted average and Kalman filtering algorithms, and constructing an equipment state feature vector; fault diagnosis: performing fault diagnosis on the feature vector based on an exclusive model library; and information pushing: pushing the information to an executor through the mobile terminal. The method breaks through the limitation of a traditional universal or general diagnosis model through a specific exclusive model library, optimizes a diagnosis algorithm for different equipment characteristics, solves the problem of high misdiagnosis rate of a single parameter, has the advantages of reducing non-planned shutdown, prolonging the service life of equipment, reducing the operation and maintenance cost and the like, remarkably improves the fault recognition precision, and improves the fault diagnosis efficiency. And meanwhile, the system can realize real-time monitoring of the equipment state and fault early warning, reduce the manual inspection frequency and reduce the labor intensity.

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 invention discloses an artificial intelligence detection system and method for computer system faults, and the system comprises a data collection layer, a data processing layer, a diagnosis algorithm layer, a knowledge management layer and a user interaction layer, and also comprises a causal reasoning module and a transfer learning adaptation layer. The causal reasoning module is used for constructing a dynamic causal logic chain of computer system faults, positioning fault root causes and generating visual explanation, and the causal reasoning module comprises a dynamic causal graph construction unit, a fault root cause positioning unit and an explanation generation unit; and the transfer learning adaptation layer is used for realizing model cross-system scene adaptation and new fault type rapid learning, and solving the problems of data scarcity and privacy protection. According to the method, the visual logic chain of the fault is constructed through the dynamic causal diagram, so that the diagnosis result is converted from black box output to traceable causal reasoning, operation and maintenance personnel can clearly master the complete logic of the fault root cause, the propagation path and the fault phenomenon, and the credibility of the diagnosis result is improved.

The invention is based on the objective of creating a diagnostic tool by comparing algorithmic computational signals with sensor measurement signals to determine faults by calculating the difference. Furthermore, a method and arrangement for an active on-board diagnostic device are to be provided that, in the event of malfunctions in faulty components, switches off faulty measurement and control signals and replaces them with algorithmic computational values, thus enabling uninterrupted operation until maintenance can be carried out. Simultaneously, fault information is to be generated. The active diagnostic device consists of four sections: the sensor measurement circuits, the active diagnostic computer circuit, the control functions section, and the fault information section. The sensor measurement circuit includes sensors (1), signal converters (2), cable connectors (3), and adapters (4). The active diagnostic computer circuit includes adapters (5), a multiplexer (6), a diagnostic computer (7) with a diagnostic algorithm memory (8), a diagnostic comparison computer (9) with a computational signal adapter (12) connected to a switching register (11), and a switching control computer (10) with a computational signal adapter (12). The control functions section comprises a control unit (14), signal amplifiers (16), actuators (17), and control elements (18). The fault information section includes an information signal adapter (19), a synchronization switching module (20), a microcontroller (21), and a display (22).

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 utility model discloses a high-precision servo liquid level meter which comprises a shell, a servo motor, a measuring wheel, a measuring cable and a floater and further comprises a main encoder and an auxiliary encoder which are coaxially arranged, the main encoder is in rigid connection with an output shaft of the servo motor, and the auxiliary encoder is in transmission connection with the measuring wheel through a magnetic coupling. The signal output ends of the main encoder and the auxiliary encoder are connected with a difference compensation controller. According to the utility model, the main encoder, the auxiliary encoder and the laser-assisted positioning module are arranged, so that the measurement precision is improved; the electromagnetic compatibility is improved by arranging a three-layer composite shielding structure, and conducted interference is blocked through transmission of the magnetic coupling coupler; the spiral layer realizes temperature self-compensation; due to the combined design of the floater, the flow guide fins and the micro-grooves, the applicable medium viscosity is expanded; the transmission of the magnetic coupling coupler reduces mechanical wear, and the elastic tension adjusting mechanism automatically maintains the tension of the cable without manual maintenance; and a self-diagnosis algorithm is built in the difference compensation controller, so that the fault early warning accuracy is improved.

The present application belongs to the field of educational data mining, and provides a cognitive diagnosis method for long-period evaluation, comprising: (1) constructing a cognitive diagnosis framework for long-period evaluation; (2) fusing the extracted student features, test features, interaction features and time sequence features to obtain a final input representation vector; (3) using a neural network structure to model a diagnosis algorithm, taking the final input representation vector obtained in step (2) as the input of the network structure, and outputting the student answer result; the diagnosis algorithm is composed of a neural network structure and a loss function; (4) collecting a data set, training the network structure, and predicting the student answer response; (5) according to the specific application scene, designing a cognitive diagnosis system to obtain the student's diagnosis report. The present application method respectively meets the single education measurement demand without long-period evaluation data accumulation and the education diagnosis demand with long-period evaluation data accumulation, and better solves the new problems brought by the change of education data form.

The application discloses a kind of establishment methods of fuel cell fault embedding model, including fault function embedding, electrochemical model calculation, membrane state water content solution, liquid water content solution gas component concentration solution and temperature equation solution sub-item calculation.The model is embedded by fault function, realizes the accurate simulation under the working condition of pressure fault, humidity fault and flow fault.Under the condition of meeting the operation requirement of fuel cell, the complex heat and mass transfer process inside fuel cell under different fault working conditions and the dynamic response of multiple transient conditions can be understood.The complex physical field change inside fuel cell under fault working condition is solved by the mutual coupling of electrochemistry, fluid and heat.The problem of unbalanced fault data type and poor data quality in current fault diagnosis research is solved, the accumulation of various fault data is realized, the model support is provided for fuel cell fault diagnosis algorithm development, and the product development cycle and bench test cost are greatly shortened.

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 invention relates to the technical field of shear wall performance evaluation, and discloses a biaxial coupling anti-seismic performance evaluation method and system for a flanged shear wall, and the system comprises an acquisition and preprocessing module which is used for acquiring geometric, material and biaxial coupling load data of the flanged shear wall in real time; after processing, a parameter-performance association database is established in combination with historical data; the model building module performs modeling based on the database and introduces a pre-diagnosis algorithm to identify potential damage risk points; the analysis module inputs real-time data to calculate an anti-seismic performance index, judges a health level, analyzes a degradation rate, predicts a fault probability and generates a pre-diagnosis report; and the management and evaluation module generates a management strategy, outputs an evaluation report, feeds back a data iterative optimization model, and realizes dynamic evaluation of the anti-seismic performance of the shear wall. Scientific basis is provided for safety management of the shear wall, and the anti-seismic safety and operation and maintenance reasonability of the structure are effectively guaranteed.

The utility model discloses a fault bearing test platform, comprising a signal acquisition device which comprises a bottom plate, and a force application driver, a bearing installation seat and a rotation driver which are successively arranged on the bottom plate, the bearing installation seat comprises an outer ring seat and an inner ring seat which are coaxially sleeved from outside to inside, and a fault bearing is installed between the outer ring seat and the inner ring seat; the outer ring seat is rotatably arranged, the driving end of the rotary driver is connected with the outer ring seat and is used for driving the outer ring seat to rotate, the driving end of the force application driver is coaxially connected with the inner ring seat and is used for applying an axial load to a fault bearing, and a sensor is arranged in the inner ring seat. The fault bearing test platform can simulate the real use condition of a fault bearing, can provide effective data support for a bearing diagnosis algorithm, and also can verify the reliability of a sensor and a cable thereof under the real use condition.

The application discloses a substation defect scene generation and fusion simulation method and device, and relates to the technical field of substations.The method comprises the following steps: collecting on-site images of a substation, constructing a time-space correlation scene library, collecting defect images and defect characteristic parameters of historical faults of the substation, constructing a defect working condition tracing correlation library, obtaining multi-dimensional defect generation constraint conditions, generating virtual defect images and virtual defect parameters of the substation by means of artificial intelligence content generation technology, adaptively fusing the virtual defect images and the time-space correlation scene library, constructing a substation defect simulation scene, diagnosing the substation defect simulation scene by means of a defect scene diagnosis algorithm, and generating scene diagnosis data.The application can realize accurate generation and simulation of a substation defect scene, solve the problems of single traditional simulation scene and poor environmental adaptability, overcome the defect that virtual defects lack logical constraints, improve the fidelity of the simulation scene, and guarantee the quality of the scene.

The invention provides a fully mechanized coal mining face sensor fault diagnosis method and device and electronic equipment, and the method comprises the steps: obtaining the current working stage of a target hydraulic support, and obtaining the time period sensing data of a to-be-diagnosed sensor on the target hydraulic support at the current working stage; according to the current working stage, a target diagnosis path is determined, and the target diagnosis path is formed by combining one or more diagnosis algorithms; and obtaining a fault diagnosis result of the to-be-diagnosed sensor according to a target diagnosis path and the time period sensing data. According to the method, the adaptive target diagnosis path is dynamically selected according to different working stages of the target hydraulic support, and analysis is performed in combination with the corresponding time period sensing data, so that the problem of false alarm or missing detection caused by mismatching of working conditions in a traditional static diagnosis method is effectively solved, the accuracy and robustness of sensor fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The low false alarm rate, the low omission rate and the high operation and maintenance efficiency are achieved.

The invention discloses an unmanned aerial vehicle engine fault diagnosis and health management integrated system, and belongs to the technical field of unmanned aerial vehicle engine fault diagnosis, the system comprises an engine digital twin model, and the engine digital twin model comprises a mechanism digital twin model and a data driving digital twin model of an unmanned aerial vehicle engine; the state monitoring module integrates a vibration sensor, a temperature sensor and a pressure sensor, collects operation parameters of an unmanned aerial vehicle engine in real time, and is used for constructing a baseline model of the unmanned aerial vehicle engine in a standard working state. And the data processing server comprises a fault database and a diagnosis algorithm library, and is used for storing historical fault data and a preset operation analysis algorithm, and comparing the constructed engine digital twin model with the baseline model. According to the method, the operation parameters of the engine are collected, and the digital twin model is used for real-time analysis and comparison, so that the automation of state interpretation is realized, the detection efficiency is remarkably improved, and manual subjective errors are eliminated.

The application discloses a kind of transformer area line loss abnormal diagnosis and loss reduction strategy intelligent decision method and system.The method of the present application comprises: collecting the meter data of each node in transformer area, and calculating the line loss rate of each time scale of transformer area;The 96-point time-sharing line loss data of transformer area in the past year is obtained by the time series anomaly detection algorithm based on adaptive k-nearest neighbor, and the line loss abnormal time series of the transformer area is obtained;Establish transformer area line loss abnormal reason diagnosis algorithm library, and construct targeted diagnostic algorithm for each type of abnormality that may occur;Loss reduction strategy knowledge base is constructed, loss reduction strategy is associated, and line loss abnormal event management strategy is obtained by using knowledge base matching;At the same time, each line loss abnormal event management strategy is divided.The present application can accurately detect line loss abnormal time series and calculate the amount of loss reduction power under the condition that the operation mode of transformer area changes greatly, and realizes the automation of transformer area line loss abnormal monitoring, reason diagnosis and strategy generation.