1642results about "Environmental/reliability tests" patented technology

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 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 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 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 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 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 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 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.

The invention relates to an arc fault detection method and system based on a dynamic fuzzy threshold and a storage medium, and the method comprises the steps: collecting the current circuit data of to-be-detected electrical equipment, and carrying out the feature extraction of the current circuit data, and obtaining a current feature parameter; the method comprises the following steps: training historical circuit data by taking a decision tree algorithm as a model architecture, and in the training process, performing parameter updating through a double-sliding window mechanism and optimizing a fuzzy threshold band range through an information gain maximization principle to obtain a dynamic fuzzy threshold decision model; and performing arc fault analysis on the current characteristic parameters through the dynamic fuzzy threshold decision model to obtain a fault detection result, and outputting early warning information when the fault detection result is that an arc fault occurs. According to the method, the dynamic fuzzy threshold value band is constructed through the Gaussian mixture model, and the self-adaptive adjustment of the threshold value is realized in combination with a double-sliding-window online updating mechanism. According to the technical scheme, the accuracy of arc faults can be remarkably improved, and particularly the false alarm rate is reduced.

Disclosed in the present invention is a grid-related performance remote test method and system for a renewable energy station. The method comprises: acquiring an associated sub-power grid of the renewable energy station, performing active disturbance test on the associated sub-power grid, and monitoring and analyzing a disturbance degree evaluation index of the associated sub-power grid; on the basis of the disturbance degree evaluation index of the associated sub-power grid, analyzing the disturbance degree grade of the associated sub-power grid, and performing matching to obtain a grid-related performance evaluation ideal value of the renewable energy station; and on the basis of the grid-related performance evaluation ideal value of the renewable energy station, evaluating the grid-related performance of the renewable energy station to obtain a grid-related performance evaluation result of the renewable energy station for feedback. The present invention evaluates the disturbance degree of a power grid, accurately simulates possible various power grid conditions in a grid-connection process, evaluates the response performance and the disturbance suppression performance of the renewable energy station, and comprehensively analyzes the grid-related performance of the renewable energy station, thereby being conducive to maintaining the stability of the power grid.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The invention relates to a charge accumulation characteristic analysis method for a polyimide material, and the method comprises the steps: applying a multi-stress synergistic effect to the polyimide material based on the multi-stress state of the polyimide material in the electric field, temperature, mechanical stress and irradiation in a deep space environment; a dynamic charge migration model of the polyimide material under the multi-stress synergistic effect is constructed, and the charge accumulation characteristic of the polyimide material is analyzed. According to the invention, based on the multi-stress state of electric field, temperature, mechanical stress and irradiation of the polyimide material in a deep space environment, a multi-stress synergistic effect is applied to the polyimide material, so that the problem of distortion of traditional single-stress or pairwise combined stress measurement is solved; through a dynamic charge migration model, charge accumulation characteristics of the polyimide material under the action of an electric field, temperature, mechanical stress and irradiation are analyzed, and accurate prediction, regulation and control for researching the electrical performance of the polyimide material are realized.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The embodiment of the invention provides a key delay test method and system, and the method comprises the steps: capturing a hardware interrupt signal generated by each physical key response in real time through simulating a preset operation event, measuring first delay time, and evaluating the first delay time according to a preset performance threshold value, analyzing existing potential external interference factors and potential system internal anomalies, adjusting initial test environment parameters, obtaining an optimized test environment, repeatedly simulating operation events, capturing hardware interrupt signals generated by response of each physical key, and calculating average delay time as a response characteristic index of the physical key; predicting the use reliability and the aging effect of the physical key by responding to the change trend of the characteristic index, and generating a trend analysis report; according to the technical scheme provided by the embodiment of the invention, the test precision and reliability are improved, a user can take preventive measures in advance, and service interruption or equipment damage caused by key faults is avoided.

The invention relates to the technical field of power supply system health assessment, in particular to a health assessment method and system of a power supply system based on artificial intelligence. The method comprises the following steps: acquiring data of a power supply system, estimating initial performance parameters of the power supply system, evaluating an initial running state of the power supply system, and acquiring initial state data of the power supply system; performing power supply equipment load operation simulation based on the initial state data, and predicting a fault evolution trajectory; the transmission quality attenuation degree of the power supply system is estimated by analyzing the fault evolution trajectory of the power supply system, the loss increase trend of a transmission line is detected, and the performance decrease trend is evaluated in combination with the attenuation degree; evaluating health attenuation data of the power supply system according to the performance reduction trend; transmitting the data to an artificial intelligence model for deep learning training to obtain accurate power supply system health state evaluation data; according to the invention, health analysis is carried out on the power supply system, so that the power supply system is safer and more stable.

The invention discloses a radio frequency electromagnetic energy explosion testing device and method. The device comprises a closed gas cavity, a testing cable, a linear driving mechanism and a processing circuit. The airtight gas cavity is filled with explosive gas. The test cable comprises an inner conductor and an outer conductor, the outer conductor is coaxially arranged around the inner conductor, the inner conductor comprises a first inner conductor and a second inner conductor which are oppositely arranged in the axial direction, the end, away from the second inner conductor, of the first inner conductor is configured to receive radio frequency electromagnetic energy, and the power of the radio frequency electromagnetic energy tends to be increased. And the linear driving mechanism is connected with one end, far away from the first inner conductor, of the second inner conductor, and is configured to drive the second inner conductor to periodically reciprocate along the axial direction, so that the second inner conductor and the first inner conductor are periodically disconnected or connected. And the processing circuit is configured to take the power of the radio frequency electromagnetic energy corresponding to gas explosion as a power safety threshold. According to the invention, data support can be provided for safe operation of the coaxial cable in practical application.

The invention discloses a photovoltaic inverter service life prediction method. The method comprises the following steps: acquiring operation waveform data and device structure parameters of the photovoltaic inverter; on the basis of transient electrical events contained in the operation waveform data, physical state parameters representing internal characteristics of the power device are determined through inversion in combination with device structure parameters, and a corrected temperature history is generated; according to the corrected temperature history and the physical state parameter, updating a damage state which represents a cumulative effect and has path dependence; based on the damage state with path dependency, a critical stability margin of the power module is evaluated and a module remaining life is determined. According to the method, the millisecond transient state is associated with physical state changes such as power redistribution and thermal path degradation in a device through a physical closed-loop inverse problem, and traceable and early-warning accurate evaluation from a transient event to the service life of a module is realized by adopting an incremental damage kernel with memory and a system-level critical gain criterion.

The invention provides a capacitor health state assessment method and device, a storage medium and computer equipment. The method comprises the steps of collecting and preprocessing a voltage signal and a current signal of a capacitor; extracting real-time multi-frequency characteristics of the capacitor from the voltage signal and the current signal, and generating a real-time multi-frequency characteristic curve according to the real-time multi-frequency characteristics; the multi-frequency characteristic curve offset between a reference multi-frequency characteristic curve corresponding to the real-time multi-frequency characteristics and the real-time multi-frequency characteristic curve is calculated, the offset amplitude is determined according to the multi-frequency characteristic curve offset, and the reference multi-frequency characteristic curve is used for representing that the capacitor is in a healthy state; and calculating a health state evaluation index of the capacitor according to the offset amplitude, and predicting the residual life of the capacitor according to the health state evaluation index of the capacitor. Therefore, online real-time monitoring is realized, and different degradation states of the capacitor are comprehensively reflected.

The invention relates to the technical field of charging equipment, and discloses a charging pile fault remote diagnosis and early warning system, which comprises the following steps: step 1, multi-source data acquisition, step 2, data preprocessing, step 3, feature extraction engine, step 4, fault diagnosis and early warning, and step 5, fault diagnosis and early warning. 5, dynamic early warning judgment; 6, early warning information generation; 7, diagnosis result storage; and 8, man-machine interaction feedback. Through multi-source data fusion acquisition, standardized processing, multi-dimensional feature extraction, cloud collaborative diagnosis and dynamic early warning mechanism design, early accurate recognition and active protection of faults are realized, discrete data are converted into actionable early warning information, operation and maintenance response delay is greatly reduced, and data traceability is ensured through block chain evidence storage.

The invention provides a power electronic component thermal resistance transient overload test method and system, and the method comprises the following steps: building a test platform, carrying out the initialization calibration of a test environment, and enabling the test platform to be integrated with a power drive module; the high-frequency sampling and dynamic adjusting technology is adopted, temperature, voltage and current data are collected at the sampling rate not lower than 10 kHz, the sampling interval is adjusted according to the thermal time constant, the advanced sensor and calibration technology is combined, coupling changes of temperature and electrical parameters are accurately captured, and the testing precision is improved; meanwhile, by means of a hardware synchronous clock and a rapid data processing technology, full-time tracking of rapid fluctuation of thermal resistance in the transient overload process is achieved, key characteristic parameters such as a thermal resistance peak value and response time are accurately extracted, and a reliable basis is provided for performance evaluation of components; in addition, various transient power waveforms and combinations thereof are supported, abundant transient overload working conditions are preset, a complex overload scene is simulated, and the practicability and reliability of the test are enhanced.

The invention discloses a multi-dimensional environment coupling aging test box and a self-adaptive control method thereof.The multi-dimensional environment coupling aging test box comprises a box body, a multi-dimensional environment coupling system and a self-adaptive control system.The box body is internally provided with a test cavity, the multi-dimensional environment coupling system is arranged in the test cavity, and the self-adaptive control system is arranged in the test cavity; and the multi-dimensional environment coupling system is connected with the adaptive control system. A multi-physics coupling environment is constructed by integrating four environment simulation modules of temperature and humidity, illumination, vibration and gas. The sensor group acquires environmental parameters (temperature and humidity, irradiance, acceleration and gas concentration) and state data of a test object in real time, and transmits the environmental parameters and the state data to the data processing unit. Based on a multi-physics field coupling model (such as finite element analysis or an empirical formula), the contribution degree of each environmental factor to material aging is calculated, a dynamic control instruction is generated, output parameters of each module are synchronously adjusted through an execution mechanism, and closed-loop control over environmental conditions is achieved.

The invention discloses a permanent magnet synchronous motor failure monitoring method, system and equipment for a space environment, and relates to the technical field of motor fault monitoring, and the method comprises the steps: constructing a simulation space environment experiment module, carrying out the accelerated aging experiment of a permanent magnet synchronous motor, and obtaining multi-dimensional degradation data; performing material performance evaluation on the permanent magnet synchronous motor, determining a material performance degradation result, performing failure analysis, and constructing a space environment failure feature library; and monitoring a motor operation parameter set of the permanent magnet synchronous motor in real time, determining an abnormal mode, and making a failure maintenance suggestion. The technical problems that in the prior art, the failure condition of the permanent magnet synchronous motor in the space environment cannot be accurately monitored, the reliability of the motor in the space environment is poor, and the operation stability is insufficient are solved, the failure condition of the permanent magnet synchronous motor is accurately monitored, and the reliability of the motor is improved. And the reliability and the operation stability of the permanent magnet synchronous motor in a space environment are improved.