2771results about "Environmental/reliability tests" patented technology

The invention relates to the technical field of power fault diagnosis, in particular to a multi-modal data fusion-based power fault diagnosis method and system, which comprehensively collects and analyzes electrical, environmental and mechanical parameters of power equipment through a multi-modal data fusion technology, constructs a multi-modal parameter feature matrix, and performs multi-modal data fusion on the basis of the multi-modal parameter feature matrix. Dynamic anomaly detection and personalized monitoring strategy formulation are realized, the accuracy and timeliness of fault diagnosis are improved, false alarm and missing alarm are reduced, operation and maintenance resource allocation is optimized, and stable operation and efficient power supply of a power system are ensured; the method comprises the following steps: respectively acquiring multi-modal data information of all same power equipment in a transformer area; feature extraction is carried out on the multi-modal data information of each power device to obtain a multi-modal parameter set; and carrying out data integration on all the multi-modal parameter sets which are collected at the same time to obtain a multi-modal parameter characteristic matrix which can represent the operation states of all the power equipment of the same model in the transformer area.

The invention provides a remote fault diagnosis method and system for operation and maintenance of a power grid. The method belongs to the technical field of power system operation and maintenance, and comprises the following steps: collecting operation state data of power grid equipment in real time through a sensor network, and carrying out real-time processing and preliminary analysis on the operation state data through an edge computing node; uploading the preliminary diagnosis result of the edge computing node and the key sensor data to a block chain platform, and storing and verifying the preliminary diagnosis result and the key sensor data through a distributed account book of a block chain; the operation state data of the power grid equipment are collected in real time, and the edge computing nodes are used for preliminary analysis, so that potential fault signals can be quickly found.

The invention belongs to the technical field of cable service life evaluation, and particularly discloses a cable defect multi-parameter aging characteristic and service life evaluation method and system, and the method comprises the steps: obtaining a plurality of first parameters which can affect the aging of a cable, and a second parameter which changes when each first parameter affects the aging characterization of the cable; constructing a calculation model containing a first incidence relation between each second parameter and the corresponding first parameter; constructing a prediction model for calculating the service life of the cable according to the calculation model, and configuring a correction factor for representing the degree of influence on the defect of the cable on each calculation model; determining the numerical value of each correction factor by adopting an optimization algorithm, and updating the correction factors according to the numerical values to optimize the prediction model; calculating the service life of the cable through the optimized prediction model; the method has the advantages that cable aging is comprehensively analyzed, modeling is clear, influence factor relations are defined, an algorithm is optimized to adjust correction factors, and prediction accuracy is improved.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses an adjustment control method for power distribution network fault early warning and positioning, and relates to the technical field of power distribution network automation control, and the method comprises the steps: carrying out the context awareness analysis of a current state based on a multi-physical field data set in combination with historical data and the operation condition of a power distribution network, and recognizing a potential abnormality; for the identified potential anomaly, further calculating the specific position of the fault through fine-grained local change analysis, and generating fault position information; calling a deep reinforcement learning model by using the fault position information, and generating a fault adjustment control instruction according to the load distribution of the power distribution network and the equipment health state; and transmitting the fault regulation control instruction to automatic control equipment in a power grid, executing isolation and load redistribution of a fault area, and monitoring the state of the power grid in real time. According to the invention, rapid isolation and load redistribution of the power grid fault area are realized, the operation stability and self-healing capability of the power grid are enhanced, and the power failure range and time caused by faults are reduced.

The invention discloses a method for testing the dynamic response characteristic of a power supply by simulating the load change of the power supply, and relates to the technical field of power supply testing, and the method comprises the steps: generating a simulation load change sequence based on a preset load change mode, and enabling each load value to correspond to a specific time point; inputting the simulated load change sequence into the power supply to be tested, and controlling the power supply to be tested to work according to the simulated load change sequence by using the multi-channel synchronous test system so as to simulate the load change in the actual use process; a high-precision data acquisition module is used for acquiring and recording output voltage values, output current values and corresponding timestamps of the to-be-tested power supply under different load conditions at a sampling rate not lower than a first set value, and a dynamic response data set is formed; and processing the dynamic response data set by using a preset data calibration algorithm, and calculating dynamic response parameters of the to-be-tested power supply at each load change point. The motor power supply testing device has the effect of improving motor power supply testing precision.

The invention relates to the technical field of cable monitoring and data analysis, in particular to an intelligent sensing method for monitoring key operating parameters of a cable, which comprises the following steps of: acquiring a first key operating parameter of the cable and first external environment data, and respectively preprocessing the first key operating parameter of the cable and the first external environment data; and respectively inputting the preprocessed first cable key operation parameters and the first external environment data into an environment influence model to generate environment influence dynamic data. According to the invention, through the self-learning perception model and the environment influence model, dynamic monitoring of the cable state is realized. According to the method, on the basis of a multi-level anomaly judgment and grading early warning mechanism, the threshold value can be dynamically adjusted according to the real-time change trend of the cable key operation parameters and the external environment data, the accuracy of cable operation state monitoring can be improved, risk assessment can be carried out on potential faults, and prevention measures can be taken in time.

The invention relates to the technical field of electromagnetic compatibility, in particular to a power application environment electromagnetic interference analysis and evaluation method and system, and the method comprises the following steps: recording the position coordinates of a plurality of power devices based on power grid operation data, collecting the current and voltage data of the operation of the devices, and measuring the electromagnetic field intensity data of a plurality of positions of a power grid; an electromagnetic environment data set is generated. According to the method, the operation data and the electromagnetic field intensity of the power equipment are recorded, the understanding ability of the electromagnetic environment is enhanced, the shielding effect of various materials is accurately evaluated by combining the analysis of the shielding materials, the design and operation of the power equipment are optimized, and the electromagnetic sensitivity of the equipment is evaluated. According to the method, the dynamic distribution of the electromagnetic field is simulated and predicted, the electromagnetic field mode causing a fault is identified, and risk equipment predicted through real-time monitoring and data analysis is combined, so that the stability and safety of a power system are improved, the anti-interference capability is enhanced, and the operation efficiency and function safety of power facilities are improved.

The invention belongs to the technical field of automobile wire harness terminal diagnosis, and discloses an automobile wire harness terminal aging diagnosis auxiliary system and method, and the method comprises the steps: collecting the state data of a wire harness terminal, and collecting the data of a working environment where the wire harness terminal is located; correcting the electrical parameter data according to the working environment data, and further correcting the physical state data to obtain an actual state data set; constructing an aging diagnosis model, setting the framework of the aging diagnosis model as a sharing layer and branch layers, setting the branch layers as a first branch layer and a second branch layer, reconstructing a loss function for the first branch layer and the second branch layer, and taking the actual state data set as input to train the aging diagnosis model; outputting the predicted aging trend and the remaining service life of the wire harness terminal based on the aging diagnosis model; and according to the predicted aging trend and the residual service life of the wire harness terminal, analyzing whether the wire harness terminal has potential abnormity, and sending a maintenance prompt to a user.

The invention discloses a new energy automobile charging cable aging performance test method and system, and relates to the technical field of cable aging detection, when the system operates, through comprehensive application of an environment acquisition module, a data processing module, a performance test module and an evaluation module, accurate evaluation and prediction of the cable aging performance are realized, and the cable aging performance is accurately evaluated and predicted. The environment acquisition module collects cable test data under temperature control, humidity control and water immersion conditions in real time, and the data processing module performs cleaning, standardization and feature extraction on the data to form a high-quality feature data set X. And the performance test module performs multi-model prediction by using a random forest, a gradient boosting tree and a deep neural network model, performs fitting through a meta-model, obtains a prediction result # imgabs0 # of a Stacking model, matches the prediction result # imgabs0 # with a preset evaluation threshold L, generates a cable aging performance test evaluation scheme, and performs cable aging performance test evaluation. The problems that a traditional method is slow in response, low in prediction precision and poor in adaptability are solved.

The invention relates to the technical field of intelligent early warning, and discloses an aviation case electrical safety intelligent early warning method, device and equipment and a storage medium. The method comprises the following steps: carrying out voltage, current, temperature and vibration parameter acquisition on a wiring row position, an input wiring terminal position and a zero sequence current transformer installation position of the aviation intelligent distribution box to obtain an electrical parameter data set; executing dynamic adjustment of an electrical safety detection threshold to obtain an aviation environment adaptability threshold parameter; performing feature extraction on the electrical parameter data set to obtain electrical parameter abnormal features; performing electrical fault mode matching and positioning analysis on the electrical parameter abnormal characteristics to obtain fault type, position and severity information; and generating a target early warning response strategy of the aviation intelligent distribution box in combination with the flight stage characteristics of the aircraft. According to the invention, a complete electrical safety protection system is constructed, the false alarm rate and the missing report rate are significantly reduced, and the safety protection capability of the aviation intelligent distribution box electrical system is improved.

The invention relates to the field of temperature monitoring, and discloses a high-voltage passive induction temperature monitoring system, which comprises a plurality of temperature sensors, the plurality of temperature sensors are arranged in different cabinets and channels in a machine room; a control unit; the control unit is used for sending a detection instruction to each temperature sensor and receiving a feedback temperature signal; the control unit generates a temperature change curve of each monitoring point according to the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the whole machine room based on the temperature change curve of each monitoring point, identifies a potential hot spot area based on the three-dimensional temperature distribution model and predicts the temperature change trend of the potential hot spot area; a refrigeration unit; the refrigeration unit is used for adjusting the temperature of the located area; the refrigeration unit is communicated with the control unit; wherein the control unit adjusts the power of each refrigeration unit based on the identified potential hot spot areas.

The invention provides an automobile wire harness performance parameter test system, and relates to the technical field of automobile wire harnesses, the automobile wire harness performance parameter test system comprises a test assembly and a control module, the test assembly comprises a simulation test group and a mute test box, and the control module comprises an acquisition module, an analysis module, a judgment model unit and a visual feedback platform; a simulation system is constructed in the simulation test group, and when the automobile wire harness is installed in the simulation system for operation, operation state parameters of an automobile under different road conditions and environment conditions are simulated; the simulation system is constructed to simulate the running state parameters of the automobile under different road conditions and environment conditions, the change relevance between the running state parameters and the current parameters of the automobile wire harness is analyzed, the reference variable is obtained, the first performance of the automobile wire harness is comprehensively judged, the actual running requirements of the automobile wire harness are met, and the reliability of the automobile wire harness is improved. The sound of the automobile wire harness is synchronously recognized and collected through the mute test box, the abnormal type of the wire harness represented by the sound is recognized, the second performance of the automobile wire harness is judged, and the method is more comprehensive.

The invention relates to an intelligent power grid distribution line fault prediction analysis method, and the method comprises the steps: carrying out the synchronous collection and standardization processing of the real-time load, electrical parameters, environment information and component aging states of each sampling point of a distribution line through distributed sampling and precise space positioning; and based on multi-time scale dynamic feature and aging feature extraction, realizing automatic generation of a structured and layered feature library and scene labels. A label-driven historical model parameter migration and meta-learning mechanism is utilized to quickly adapt to new working conditions and finely adjust the weight of the model, multi-time scale features are fused to carry out contribution degree weighting, and finally the accuracy and robustness of fault prediction are improved. The system also continuously optimizes the model performance through real-time A / B comparison and automatic parameter switching, has the capabilities of data tracing and result interpretation, and significantly enhances the timeliness, reliability and intelligent level of power distribution network fault diagnosis.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

The invention belongs to the field of data processing, and particularly relates to an accelerated prediction method and device for the corona-resistant life of an electromagnetic wire insulating layer, and the method comprises the steps: collecting the conductance current density and applied electric field intensity of a test sample in the electromagnetic wire insulating layer at different temperatures; determining an electrical aging threshold value corresponding to the test sample based on the conductance current density and the applied electric field intensity at different temperatures; executing an accelerated corona resistance test based on the electrical aging threshold value to obtain short-time corona resistance life test values of the test sample in a plurality of test environments; constructing an accelerated prediction model based on the short-time corona-resistant life test value; and inputting the operating environment parameters of the target sample into the accelerated prediction model to obtain the corresponding target corona-resistant life. The accelerated prediction model is constructed through the short-time corona-resistant life test value, and the accelerated prediction model is adopted to predict the corresponding corona-resistant life in real time, so that the corona-resistant life prediction efficiency is improved, and the accuracy of the corona-resistant life prediction value is improved.

The invention discloses a method and a system for monitoring the reliability of a photovoltaic converter in a special plateau environment. The method comprises the following steps: firstly, acquiring electrical quantity, temperature quantity, environment quantity and operation quantity, filtering abnormal data, and realizing accurate alignment of multi-frequency signals in combination with a dynamic time warping algorithm; secondly, constructing a plateau sensitive feature set, and optimizing feature quality through physical consistency check and three-stage feature selection; then, an IGBT thermal fatigue equation and a capacitance aging equation are fused to establish a health index evolution model, a Bayesian physical information neural network is used for prediction, and a high-reliability confidence interval is output through Monte Carlo sampling. And finally, dynamically correcting the residual life based on the comprehensive environment factor, and triggering a hierarchical maintenance decision according to the health index state, the residual life and the confidence interval width. The service life prediction precision of the photovoltaic converter in the plateau environment is remarkably improved, the operation and maintenance cost is effectively reduced, and the equipment operation reliability is enhanced.

The invention discloses an electronic wire harness intelligent high temperature resistance test method based on dynamic temperature monitoring, and the method comprises the steps: collecting multi-region temperature data through a sensor array, and dynamically adjusting the measurement point density and sampling frequency for a region with a higher temperature change rate; for a local high-temperature area, sampling points are increased, a high-temperature micro-change monitoring mechanism is triggered, and micro-change of the temperature is recorded in real time; according to a temperature gradient change trend, dynamic adjustment parameters of a key area are extracted, and density distribution of measuring points is optimized; meanwhile, optimal utilization of computing resources is also considered, and the density of measuring points in a temperature stable region is properly reduced; through continuous monitoring and algorithm parameter optimization, stable high temperature evaluation is finally realized, and the temperature evaluation error is controlled within 1 DEG C. According to the dynamic self-adaptive temperature monitoring method, the temperature monitoring precision and efficiency during operation of the electronic wire harness are remarkably improved.

The invention relates to the technical field of membrane switch detection, in particular to membrane switch performance detection equipment which comprises a limiting placement table, an elastic pushing and pressing device, a butt joint sealing tool, a continuous touch device, a simulation water conveying device and a simulation heating device. The elastic pushing and pressing device is installed at the top of the limiting placement table, the continuous touch device is installed at the movable end of the elastic pushing and pressing device and used for continuously triggering control keys of the membrane switch, and the butt-joint sealing tool is installed at the bottom of the continuous touch device and used for being attached to the surface of the membrane switch in a sealed mode. The simulation water conveying device is installed on the elastic pushing and pressing device, the output end of the simulation water conveying device is connected with the butt joint sealing tool, the simulation heating device is installed in the continuous contact device, and the reliability and stability of product production are effectively ensured.

The invention provides a charging pile safety detection method based on artificial intelligence, and the method comprises the steps: building a mathematical model between the insulation performance and environment parameters through a support vector machine algorithm according to the results of a high-voltage dielectric test and partial discharge detection, and predicting the insulation margins of a charging pile under different environment conditions through the model; the virtual reality technology is adopted, insulation performance test results of the charging pile under different environment conditions are visually displayed, and according to the insulation margin, the insulation state of the charging pile is marked with different colors in a virtual scene; and a deep learning algorithm is adopted to perform feature extraction and classification on the partial discharge signal of the charging pile, the type and severity of partial discharge are judged, and the insulation structure design of the charging pile is optimized according to a classification result.

The invention relates to the technical field of power battery testing, in particular to a BMS function testing system and method for a new energy automobile. The method comprises the following steps: constructing battery pack structure model data according to battery pack CAD design drawing data; collecting vehicle working condition charging and discharging sample data; carrying out adaptive current excitation sequence processing according to the vehicle working condition charging and discharging sample data, and generating multi-working condition disturbance pulse excitation sequence data; performing disturbance magnetic field simulation on the battery pack structure model data through the multi-working-condition disturbance pulse excitation sequence data to obtain working condition test magnetic disturbance response data; and performing battery pack BMS monitoring data acquisition based on the working condition test magnetic disturbance response data, performing BMS current distribution monitoring performance evaluation, and generating BMS current monitoring performance evaluation data. By simulating current excitation and magnetic field response under multiple working conditions, comprehensive evaluation of the BMS performance is realized, and the test accuracy and efficiency are improved.

The invention relates to the technical field of wire performance detection, and discloses a wire high temperature resistance detection method, device and equipment and a storage medium. The method comprises the following steps: arranging micro thermocouple array sensors on the surface, the bending part and the connecting point of an outer insulating layer of a to-be-measured wire rod to obtain a wire rod temperature monitoring network; placing the to-be-tested wire rod in a constant-temperature box to execute a high-temperature cyclic loading test, and collecting real-time temperature response data of the to-be-tested wire rod; carrying out temperature gradient analysis and heat distribution characteristic calculation to obtain a temperature change characteristic parameter set; executing temperature fluctuation compensation and thermal stress accumulation calculation to obtain thermal stress characteristic parameters; and carrying out aging characteristic analysis on the wire insulating material through the thermal stress attenuation network model to obtain wire predicted life and failure probability distribution of the to-be-tested wire in a high-temperature environment. The method can comprehensively capture the temperature change characteristics of each part of the wire rod, and achieves the precise prediction of the failure probability of the wire rod under different temperature conditions.

The invention relates to the field of battery car charger detection, in particular to a charger health state assessment method and system, and the method comprises a data collection step, a predicted electric quantity calculation step, a health state assessment step and a fault feature analysis step. A sliding time window algorithm is used for dynamically calculating electric quantity loss, a self-adaptive dynamic threshold system based on an operation state and an environment working condition is constructed, fault characteristics are analyzed in combination with a nonlinear fusion model and an equipment aging factor, and accurate evaluation of the health state of the charger and rapid fault positioning are achieved. And the evaluation comprehensiveness, accuracy, threshold dynamic nature and fault analysis precision are improved.

The invention discloses a cable optical fiber temperature measurement monitoring system based on big data AI fusion, particularly relates to the technical field of cable optical fiber temperature measurement monitoring, and aims to realize thermal-electric dynamic coupling monitoring through high-density distributed optical fiber temperature measurement and big data AI fusion modeling and introduction of current load synchronous acquisition, and realize high-density distributed optical fiber temperature measurement and big data AI fusion by means of time synchronization and space relocation processing. The method comprises the following steps: identifying a weak response signal of a thermal inertia hysteresis characteristic and an initial temperature rise stage, constructing a cross-scale time sequence recurrent neural network through multi-dimensional enhancement and vector fusion, outputting a continuous scoring sequence reflecting abnormal credibility, and constructing a risk factor group reflecting a fault evolution path in combination with a scoring fluctuation amplitude, a continuous period and a response delay characteristic, and carrying out positioning offset correction through correlation mapping of risk factors and along-cable temperature distribution and by utilizing a topological relation between optical fiber path coordinates and a cable space, and realizing space reverse projection of an abnormal peak value, thereby recovering an accurate position of a fault heat source and a latent hidden danger section.

The invention relates to a lithium battery charging system, and relates to the technical field of battery charging, the lithium battery charging system comprises a charging module, a detection module and a data processing module, and also comprises a battery health assessment module used for collecting battery cycle index, internal resistance and capacity attenuation parameters, and constructing a battery health state assessment model; the power grid interaction module is used for acquiring real-time electricity price data and a power grid load state; the environment sensing module comprises an environment temperature and humidity sensor and is used for collecting environment temperature and humidity parameters; and the AI prediction module is constructed based on a deep learning network and is used for predicting a battery state change trend. According to the invention, the battery health evaluation module is arranged to collect parameters such as cycle index, internal resistance and capacity fading rate to construct the SOH evaluation model, the problem of single dimension of battery health state evaluation in the prior art is solved, the aging degree of the battery can be accurately identified, and the risk of overcharge and undercharge of the aged battery due to improper power adaptation is avoided.

The invention provides a power electronic device state electromagnetic voiceprint monitoring system and method, and relates to the technical field of power electronic device aging monitoring, and the system comprises an electromagnetic voiceprint collection device which comprises a resonant piezoelectric speed sensor, a voiceprint signal collection module, a self-energy-taking power supply module and a first wireless communication module; the system main control module comprises a second wireless communication module, a central processing unit, a voiceprint storage unit, a voiceprint display unit and an aging evaluation module, and the second wireless communication module is in communication connection with the first wireless communication module. With the adoption of the scheme, the early aging state in the device is monitored under the condition that the normal work of the power electronic device is not influenced; the monitoring system is low in invasiveness, higher in monitoring sensitivity and better in reliability.

The invention discloses a photovoltaic inverter test system and method, and the system comprises a photovoltaic simulator module which is used for simulating the output characteristics of a photovoltaic array; the adjustable load module is used for adjusting different types of loads; the data acquisition and monitoring module is used for monitoring and recording operation parameters of the photovoltaic inverter in real time; the control and protection module is used for controlling the test process and providing protection; the environment simulation module is used for simulating working states under different environment conditions; and the test report generation module is used for generating a test report and performing data analysis. Compared with the prior art, the photovoltaic inverter test system has the advantages that remarkable technical breakthroughs are realized in the aspects of improving test precision, enhancing load regulation capability, expanding environment simulation conditions and improving data acquisition and monitoring functions, various defects in the prior art are overcome, the quality and efficiency of photovoltaic inverter test are greatly improved, and the photovoltaic inverter test system has important application value and popularization prospect.

The invention discloses a self-sensing monitoring method, system and equipment based on a lightning arrester, and relates to the technical field of power equipment monitoring. The self-sensing monitoring method, system and equipment based on the lightning arrester comprises the following steps: S1, collecting lightning stroke data in real time through multi-sensing fusion, and performing data cleaning and standardization processing on the lightning stroke data; s2, adopting a three-mode sensing and three-source power supply cooperation mechanism to realize state-driven adaptive sensing and energy path switching; s3, automatically scheduling a communication path according to a communication type and link quality, dynamically analyzing and compensating transmitting power, and completing authentication encryption and multi-hop secure transmission; s4, lightning stroke characteristics are extracted, an equipment life prediction model is constructed, residual life prediction is realized, and equipment health grade division and operation strategy adjustment are carried out according to the residual life prediction model; and S5, calculating an electric leakage deviation value by reconstructing a current waveform, triggering graded early warning and linking a communication power and energy supply strategy. The problem that the operation of a power system is affected due to large leakage current monitoring errors is solved.

The invention relates to the technical field of power equipment monitoring, in particular to a power equipment state monitoring data acquisition method based on a Beidou navigation system, which comprises the following steps: S1, equipment state data acquisition: acquiring state data of equipment in real time; s2, establishing an inter-equipment collaborative learning mechanism: establishing an inter-power equipment collaborative learning mechanism; s3, data fusion and standardization: performing standardization processing and fusion on the state data acquired by each device; s4, performing equipment state correlation analysis: identifying state correlation and potential fault modes between the equipment; and S5, a state evaluation and feedback mechanism: according to an analysis result associated with the equipment state, evaluating the equipment state, and feeding back an evaluation result to an inter-equipment collaborative learning mechanism. According to the invention, equipment monitoring and maintenance strategies can be dynamically optimized, the accuracy and efficiency of equipment health management are improved, the risk of fault diffusion is reduced, and the service life of equipment is prolonged.

The invention discloses a fault detonation simulation test method and platform for an extra-high voltage converter transformer network side bushing, and the method comprises the steps: providing a critical value calculation method for primary physical explosion and secondary chemical explosion of the extra-high voltage converter transformer network side bushing under different arc fault energy conditions; and the critical conditions of primary physical explosion and secondary chemical explosion can be accurately judged. According to the invention, the whole dynamic evolution process from rapid cracking of transformer oil and sudden pressure increase caused by converter transformer fault arc to superposition of physical explosion and explosion chemistry can be truly simulated, a grid-side bushing fault detonation critical discrimination method is provided, and technical parameters and theoretical support are provided for the explosion-proof and pressure-relief design of the converter transformer.