610 results about "Failure analysis" patented technology

A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.

The invention relates to a pier damage early warning analysis method under the action of flood, and solves the technical problems of high disaster recognition cost, low bridge damage recognition precision, untimely early warning and alarming and the like based on monitoring equipment in the existing concrete bridge flood-disaster-resistant technology. According to the method, by means of an established plastic damage simulation model, expensive sensors do not need to be arranged on a bridge structure, only basic information of the bridge structure and flood information such as the flow speed, the water level and the scouring depth are obtained, and damage and failure analysis of the bridge pier structure under the action of different influence factors by the flood action can be studied; structural damage state qualitative analysis and modal integrity damage index quantitative analysis are combined to determine bridge pier structure damage grades and damage failure judgment criteria, and early warning values corresponding to different damage grades of a bridge pier are inverted, so that accurate damage identification and rapid grading early warning of the bridge pier structure under the flood effect are realized. The flood disaster resistance of the bridge structure is improved, safe operation of the bridge is guaranteed, and the service life of the bridge is prolonged.

This application relates to a test device applied to a coupled environment of stress, wear, and corrosion, and a quantitative evaluation method, and belongs to the field of failure analysis of materials. The test device includes a stress-loading mechanism configured to load a stress on a sample, a corrosion mechanism configured to conduct an electrochemical corrosion test on the sample, and a wear mechanism configured to wear a sample. The test device applied to a coupled environment of stress, wear, and corrosion of this application can establish a wear-stress-corrosion coupled action test process, and can investigate service behaviors of various metallic materials and metal matrix composites under conditions close to actual service conditions to obtain key behavior data under a multi-factor coupled action, quantitatively analyze a wear, a corrosion, a wear-corrosion interaction, a stress-wear interaction.

The invention discloses a digital chip fault detection method and system based on big data, and the method comprises the steps: collecting the historical hierarchy data of chip failure fault analysis, extracting the feature data of each stage, and building the multi-dimensional feature association of the failure fault analysis data based on the feature data; synchronizing a single fault positioning model of the chip failure fault established based on the single failure root cause and a multi-fault positioning model of the chip failure fault established based on the associated interaction failure multiple root causes; failure fault positioning is carried out through a single fault positioning model and a multi-fault positioning model, a failure fault positioning result is output, and finally, the fault positioning output results of the single fault positioning model and the multi-fault positioning model are verified and reproduced. According to the method, the corresponding fault positioning model is constructed by using the judgment result of the failure root cause, and the problem of low positioning precision caused by various influence factors and interaction of an existing single fault positioning method is solved.

The invention discloses a composite material microcosmic failure analysis method based on a micromechanical failure theory. The method comprises the following steps: obtaining performance parameters of each component of a composite material according to a composite material component performance test; establishing a finite element mesoscopic RVE model; the rigidity of the interface between the fiber and the matrix is reversely deduced through fitting of experimental data of a macroscopic experiment; applying a periodic boundary condition to the finite element mesoscopic RVE model; a load is applied to the finite element mesoscopic RVE model through ABAQUS finite element software, the failure behavior of the composite material in a complex stress state is simulated, and the stress-strain relation and the mesoscopic failure rule of the composite material in different loading modes are predicted. The method can be applied to composite material mesoscopic failure simulation analysis in axial, transverse and off-axis load, thermal load and composite load forms, can more accurately predict the mesoscopic failure rule of the composite material, and has important significance on research and application of the composite material.

The invention discloses an intelligent fault diagnosis method and system for an automatic numerical control machine tool, and relates to the technical field of data processing, and the method comprises the steps: interacting fault event records of a target numerical control machine tool; based on the fault event records, fault characteristic mining is carried out to construct a fault network, a fault characteristic network is constructed through graph theory analysis based on fault propagation, and a fault probability network is constructed through fault failure analysis; according to the fault network, diagnosis large model training and lightweight processing are carried out, a fault diagnosis module is built, and module building is carried out through two-step training based on a lightweight loss function; time-staggered sampling frequency is determined, monitoring management and control of a front-end sensor are carried out based on the time-staggered sampling frequency, and machine tool operation data are determined; and the machine tool operation data is transmitted back to the fault diagnosis module, fault occurrence judgment and failure level measurement are carried out, and a fault diagnosis result is determined. Therefore, the technical effects of improving the fault diagnosis accuracy and real-time performance and improving the deployment adaptability are achieved.

The invention relates to the technical field of deformation monitoring, in particular to a reservoir dam deformation monitoring method and system based on a sensor network, and the method comprises the steps: carrying out the calibration analysis of the failure degree of a corresponding position based on the basic engineering parameters of each position of a reservoir dam and a failure degree prediction result; and performing failure analysis on each position according to the predicted future water flow impact condition and the failure degree calibration analysis result of each position of the dam, and performing maintenance analysis resource allocation on the corresponding position based on the failure analysis result of each position. Failure analysis and maintenance resource allocation are carried out based on water flow conditions, deformation conditions, basic engineering parameters and failure degree prediction and calibration analysis of all positions of a reservoir dam, potential risks are found in time by collecting water flow and deformation data in real time, the maintenance priority of all areas is determined, it is ensured that high-risk areas are processed in time, and the maintenance efficiency is improved. And according to the real-time monitoring and prediction result, the maintenance plan is dynamically adjusted, and the flexibility and adaptability of resource allocation are improved.

A maintenance and monitoring infrastructure can collect real machine samples and train failure analysis models to identify operational anomalies, indicative of a machine failure. Failure analysis models can be trained and more robustly tested and developed by using artificial samples that can simulate what a real monitor would have measured had the real monitor been deployed in a set of selected circumstances (a scenario). A synthetic sample generator (SSG) can include a seed development cycle stage (SDCS) and a synthesis execution stage (SES). The SDCS can generate a signal, from which the SES can generate a multi-phenomena synthetic signal, simulating a scenario, where a monitor would have likely yielded the generated synthetic signal, or a similar signal. The synthetic signal data can be used to train failure analysis models.

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.

The invention relates to the technical field of metal strip degreasing production line on-line control, and particularly discloses a metal strip degreasing production line on-line control system and method, and the system comprises a degreasing state judgment module, a degreasing information collection module, a degreasing incomplete analysis module, a degreasing excessive analysis module, a database, and a production line degreasing effect feedback module. According to the method, the reason for incomplete degreasing is determined by judging the degreasing state corresponding to the target metal strip, the corresponding control scheme is matched according to the reason for incomplete degreasing, meanwhile, when the degreasing state is excessive degreasing, the PH value corresponding to the degreasing fluid in the degreasing tank in next batch of degreasing is adjusted, and the degreasing efficiency is improved. And finally, whether the degreasing effect corresponding to the target production line has a problem or not is judged, the overall performance of the product is improved, the service life of the product is prolonged, meanwhile, the problem can be accurately positioned, a clear direction is provided for subsequent control measures, and production stability and product quality consistency are improved.

The invention relates to the field of intelligent diagnosis of oil and gas field development, in particular to a fracturing wellhead device failure analysis method and system based on a large model and a knowledge graph. The system is helpful for solving the problem that the accuracy and timeliness of failure analysis and fault diagnosis of the fracturing wellhead device in the prior art are limited. The method comprises the steps of building a knowledge graph, setting a knowledge graph dynamic updating mechanism oriented to the fault evolution process, building an open source large model and introducing a closed loop feedback mechanism. According to the method, the large model, the knowledge graph and the RAG technology are applied to fracturing wellhead failure analysis, and the intelligent level of the oil and gas industry is remarkably improved; the problem that a large model is high in generalization but insufficient in specialty is optimized, and the accuracy of answer generation is improved.

The invention belongs to the technical field of tire performance simulation, and particularly relates to a tire durability simulation analysis system and method.The tire durability simulation analysis method comprises the steps that a tire three-dimensional finite element model containing cord-rubber interface details is used for dynamically recognizing a first-time debonding event by focusing a cord-rubber interface area in simulation; a debonding extension path and a failure evolution judgment mechanism are constructed through time-space accompanying monitoring of a newly added debonding area, so that tire durability evaluation starting from a microcosmic interface scale is realized, and the accuracy of fatigue life prediction is improved; meanwhile, road conditions are introduced under a microscopic failure analysis framework based on a tire three-dimensional finite element model, tire responses at different speeds are simulated by adopting a stepped speed-increasing loading sequence, and durability simulation results under different road condition and speed combinations are obtained and visually presented in an equivalent map form. And the fatigue performance distribution characteristics of the tire under a complex operation condition can be intuitively reflected.

The invention belongs to the field of material mechanical property testing and strain measurement, and particularly relates to a local strain measurement method and system based on a four-point bending and digital image correlation (DIC) technology, and the method specifically comprises the following steps: cutting to prepare a sample, carrying out surface electrolytic polishing, preparing speckles on the surface of the sample to be tested, and carrying out a four-point bending experiment; speckle images of the lower surface of the sample are collected, displacement correction is conducted in combination with bending deflection calculation, the bending load and the strain of the upper surface of the sample are obtained in real time, the stress-strain relation and local strain distribution of the lower surface of the sample are obtained through calculation, and finally deformation and failure mechanism analysis is conducted in combination with surface deformation and crack morphology. The method provided by the invention combines four-point bending and DIC plane strain measurement technologies, can realize stretching, compression and dynamic fatigue loading on the surface of the sample, is suitable for performance test and failure analysis of tension, compression and fatigue deformation of brittle metal materials such as intermetallic compounds and amorphous materials, and has the characteristics of convenience, rapidness and low cost.

The invention relates to an aero-engine stator part crack critical dimension comprehensive determination method and system, and belongs to the technical field of aviation, the method comprises the following steps: obtaining an aero-engine typical hot end stator part geometric model and material parameters, and carrying out static strength analysis according to fluid-thermal-solid coupling load characteristics in actual working conditions; inserting an initial crack into a first principal stress plane of a dangerous part of the real component, carrying out crack propagation simulation to determine a crack propagation path, and calculating a critical crack size of a dangerous point; analyzing critical crack sizes under different failure modes, such as cooling failure and buckling failure; and integrating analysis results under the multi-failure mode, and determining a comprehensive critical dimension as a final judgment standard. According to the method, through combination of fracture failure and function failure analysis, accurate judgment of the critical dimension of the crack is realized, limitation of a traditional single fracture criterion is broken through, comprehensive function failure analysis is realized, the accuracy of a maintenance decision is remarkably improved, and a scientific basis is provided for condition-based maintenance decision.

The techniques disclosed herein provide a system for condensing large log files to enable interoperability with transformer-based large language models in automated failure analysis. In various contexts such as enterprise cloud services, log files can be large oftentimes containing millions of lexical units that can incur significant processing time and resource consumption. As such, the disclosed techniques provide a system for producing a directional knowledge graph representing a condensed form of a log file for automated failure analysis. Firstly, the log file is preprocessed to filter information that is irrelevant to the failure that previously occurred. The filtered lexical units are then used to generate a directional knowledge graph comprising a plurality of vertices which correspond to individual lexical units, and which are connected by edges representing the semantic connection between the lexical units. In this way, the directional knowledge graph significantly reduces processing times and improves resource consumption.

The invention discloses a failure analysis method and system for the electrical property of a gallium nitride integrated circuit, and the method comprises the following steps: carrying out the preliminary electrical property test of the gallium nitride integrated circuit, and collecting a first group of electrical property test data; secondly, preprocessing the data to improve the accuracy of subsequent analysis; analyzing the preprocessed first group of electrical test data by using a trained artificial intelligence algorithm, and identifying potential failure modes or abnormal features; performing infrared imaging on the gallium nitride integrated circuit to obtain a heat distribution image of the gallium nitride integrated circuit, and combining layout and circuit design information of the integrated circuit by analyzing hot spots or abnormal temperature areas in the heat distribution image; the method has the beneficial effects that artificial intelligence and an infrared imaging technology are applied to failure analysis of the gallium nitride integrated circuit for the first time, and technical fusion and innovation are realized; through intelligent analysis of an artificial intelligence algorithm and visual display of an infrared imaging technology, the efficiency and accuracy of failure analysis are remarkably improved.

The invention discloses a multi-axis multi-field coupling fatigue test device, and belongs to the technical field of material testing. The device comprises a rack, a multi-axis loading unit, an environment simulation unit, a dynamic sealing interface and a cooperative control system. The core of the device is that the test sample can simultaneously bear the coupling effect of multi-axis mechanical stress (pulling, pressing and twisting) and various environmental fields (such as high and low temperatures, corrosive media, illumination and electric fields) through a highly integrated design; and the dynamic sealing problem of the actuator rod in the environment box is solved through an innovative dynamic sealing interface (such as labyrinth-magnetofluid composite sealing). According to the invention, accurate linkage control of multiple channels is realized through the cooperative control system, the service environment of key parts under actual complex working conditions can be truly simulated, and a reliable test platform is provided for failure analysis and life prediction of products under multi-field coupling.

The invention relates to the technical field of building construction, in particular to a prefabricated building construction progress automatic monitoring method. The method comprises the following steps: obtaining a building construction design drawing; based on the building construction design, prefabricated building component design data is extracted; building a building construction three-dimensional model based on the prefabricated building component design data; building component transportation simulation is conducted according to the building construction three-dimensional model, and building component transportation data are obtained; identifying a component cracking degree based on the building component transportation data; performing connection failure analysis based on the component cracking degree to obtain component connection failure data; predicting a reinforcement corrosion risk based on the component connection failure data; evaluating assembly durability based on the reinforcement corrosion risk; and extracting hoisting design data of the building construction design drawing. Based on the building construction technology, the efficiency and accuracy of prefabricated building construction progress monitoring are improved, and the accuracy of construction plan execution and the resource scheduling efficiency are improved.

The invention discloses a method for predicting high-temperature degradation of a micro-copper pillar bump interconnection resistor. The method comprises the following steps: establishing a relationship between the net flux of Ni atoms and the thickness change of a Ni-Cu-Sn compound layer based on the thermal diffusion flux of the Ni atoms; establishing a relationship between the net flux of the Cu atoms and the thickness change of the Cu6Sn5 layer and the Cu3Sn layer based on the thermal diffusion flux of the Cu atoms; based on the thickness change relationship, establishing a thickness high-temperature growth model of the Ni-Cu-Sn compound layer, the Cu6Sns layer and the Cu3Sn layer; based on the thickness high-temperature growth model, establishing a thickness high-temperature consumption model of the Cu bonding pad, the Sn solder and the Ni layer; and establishing a resistance high-temperature degradation model based on the thickness high-temperature growth model and the thickness high-temperature consumption model. According to the method, an effective solution can be provided for predicting the high-temperature degradation condition of the copper pillar bump interconnection resistor plated with the Ni layer, and a large amount of service life and time and economic cost generated by failure analysis tests are greatly reduced or avoided.

A lithium ion battery polycrystal / single crystal positive electrode material multi-physics field coupling modeling and failure analysis method comprises the steps that a three-dimensional positive electrode particle structure model is constructed, a polycrystal structure is generated by randomly filling spherical boundaries with non-overlapped spherical crystal grains, and single crystals are complete spheres; importing the model into a finite element simulation platform, and setting a multi-physics field coupling interface; establishing a diffusion, migration and electrochemical reaction kinetic model; establishing a lithium concentration-strain-stress coupling relation and solving the lithium concentration-strain-stress coupling relation; setting a failure criterion based on stress distribution to identify a damaged area; and outputting a simulation result and carrying out structure-performance correlation analysis. The method can reveal the evolution behavior difference of the polycrystalline positive electrode and the monocrystalline positive electrode in the electrochemical-mechanical process, provides reliable theoretical and simulation support for electrode material design, optimization and life evaluation, and provides powerful technical support for optimization design of the high-performance lithium ion battery positive electrode material.

The invention relates to the technical field of submarine cable engineering equipment, and discloses a submarine cable bending limiter failure analysis method and system, and the method comprises the steps: constructing a large-scale wave water tank experiment system; a wave flow test is carried out in a large-scale wave water tank experiment system, and three-dimensional terrain evolution data of a scouring pit formed at the single-pile foundation model and dynamic strain response and displacement of the submarine cable bending limiter model are obtained; constructing a hydrodynamic force-sediment transportation-submarine cable motion coupling numerical model according to the three-dimensional terrain evolution data, the dynamic strain response and the displacement; and according to the hydrodynamic force-sediment transportation-submarine cable motion coupling numerical model and different hydrodynamic force conditions, the quantitative relation between the scour range extension and the failure threshold value of the submarine cable bending limiter is determined. According to the scheme, the tensile stress and bending response change rule of the submarine cable and the bending limiter can be quantitatively analyzed, and reference is provided for optimal design and protection of the offshore wind power pile foundation submarine cable bending limiter.

The invention relates to the technical field of section analysis of printed circuit boards, in particular to a section analysis method for PCB positioning cutting, a target abnormal area is positioned on a PCB through an optical microscope, the optical microscope not only can observe the morphology of the surface layer of a sample, but also can observe the morphology within a certain range below the surface layer, and the accuracy of section analysis is improved. And meanwhile, the optical microscope is very sensitive and accurate in color identification, so that compared with the prior art in which the target abnormal area is positioned on the PCB through a scanning electron microscope, the target abnormal area can be more effectively positioned on the PCB, and the purpose of reducing the positioning difficulty of the target abnormal area is achieved. Besides, the positioning layer and the protection layer are deposited in the target abnormal area, so that the cross section sample can be prevented from being physically and chemically damaged in the cutting process on the basis of improving the cutting precision of the cross section sample, and the failure analysis precision of the PCB is improved.

The invention relates to the technical field of chip test vector intelligent generation scheme design, in particular to a failure analysis-oriented chip test vector intelligent generation method and system. The method comprises the following steps: acquiring a chip design netlist, a fault model and a known failure mode; analyzing potential design or manufacturing defects based on the information; and according to an analysis result, a targeted test vector capable of effectively exciting and isolating potential defects is automatically generated. The system comprises a corresponding input module, an analysis module and a generation module. According to the method, the design information, the fault model and the historical failure mode are intelligently fused, automatic and precise generation of the test vector is achieved, and the defects that a traditional method is insufficient in test coverage rate, low in failure positioning efficiency and limited in automation degree are overcome. According to the method, the test completeness and the failure analysis efficiency can be remarkably improved, the dependence on artificial experience is reduced, and an efficient technical means is provided for guaranteeing the reliability of the chip.

The utility model relates to the technical field of batteries, and discloses a pole piece powder removing device which comprises a powder removing mechanism, the powder removing mechanism comprises a vertical moving assembly and a powder removing assembly, the powder removing assembly comprises a powder removing brush, a first pressing ring and a first elastic piece, and the powder removing brush is arranged on the vertical moving assembly; the first pressing ring is elastically connected with the vertical moving assembly through a first elastic piece and arranged outside the powder removing brush in a sleeving mode, the vertical moving assembly is used for driving the powder removing assembly to move in the direction close to the pole piece, when the first pressing ring abuts against the pole piece, the vertical moving assembly continues to drive the powder removing assembly to move, and the first pressing ring is subjected to resistance to enable the first elastic piece to be compressed; and the powder removing brush abuts against the pole piece. The first pressing ring is elastically connected with the vertical moving assembly, the pole piece can be prevented from being damaged when pressed, the powder removing brush removes powder on the pole piece in the first pressing ring, the pole piece can be prevented from moving, all removed powder can be left in the first pressing ring, collection is convenient, the ICP detection requirement is met, and the accuracy of the test result of battery failure analysis is improved.

The invention provides a wind power blade girder layering damage failure analysis method based on body-shell coupling. The method comprises the steps that a full-size blade geometric model and a full-size blade shell finite element analysis model are established; selecting a full-size blade from a blade root pitch circle to a 1 / 3 blade length section as a shell analysis model; embedding the prefabricated layered defect into the full-size blade girder area by adopting a body-shell coupling method to obtain a body-shell coupling model for blade girder damage failure analysis; blade root fixing constraint is adopted, equivalent fatigue load is applied to the end of the shell analysis model through a multi-point constraint method, out-of-plane displacement of the center point of the upper surface of a sub-plate and the center point of the lower surface of the sub-plate is extracted, and a layered expansion failure process along with load action time is extracted; and carrying out layering failure research on the layering damage of the full-size blade. The method can solve the problem that there is no effective analysis method for the main beam layering failure mode and the expansion path of the wind power blade.

The invention relates to a power distribution network power failure monitoring method and system based on an artificial intelligence engine and an intelligent agent, and the method comprises the steps: S1, introducing a sensor intelligent fault detection model, monitoring the state of a sensor in real time, and recognizing and removing a fault sensor; s2, performing error compensation on the sensor data based on the sensor error compensation model, and fusing the data from different sensors to obtain a final fusion feature matrix; s3, based on a multi-path communication method, transmitting the sensor data subjected to fusion processing of the edge nodes to a cloud platform; s4, obtaining a fusion feature matrix of the power failure area, and analyzing a power failure reason based on the power failure analysis model; s5, based on the output of the power failure analysis model, positioning a fault point by using the power distribution network topological graph and the equipment state diagram; and S6, constructing a visual unit, and displaying the state and the monitoring result of the power distribution network in real time. The reliability and the safety of the power distribution network are improved, and the power failure time and the economic loss are reduced.

The invention discloses a micro-copper pillar bump interconnection resistor electromigration degradation prediction method. The method comprises the following steps: obtaining the net flux of Cu atoms in a Cu6Sn5 layer and a Cu3Sn layer and the net flux of Ni atoms in a Ni-Cu-Sn compound layer under forward current and reverse current; establishing a relationship between the net flux of Ni atoms and the thickness change of the Ni-Cu-Sn compound layer and a relationship between the net flux of Cu atoms and the thickness change of the Cu6Sn5 layer and the Cu3Sn layer; establishing a thickness polarity growth model of the Ni-Cu-Sn compound, the Cu6Sn5 layer and the Cu3Sn layer; establishing a thickness polarity consumption model of the Cu bonding pad, the Sn solder and the Ni layer; and establishing a resistance electromigration degradation model of micro-copper pillar bump interconnection. According to the method, the electromigration degradation prediction of the interconnection resistor of the copper pillar bump plated with the Ni layer can be realized, and a large amount of service life and time and economic cost generated by a failure analysis test are greatly reduced or avoided.

The invention provides an MLCC short-circuit product defect position analysis method, and belongs to the technical field of multilayer ceramic capacitor failure analysis. The method comprises the following steps: carrying out rapid leakage current detection on MLCC products for 0.5-2 seconds, and screening out short-circuit products of which the leakage current is greater than a first preset threshold value; products with crack defects are eliminated through an acoustic scanning microscope; gradually applying increased direct-current voltage to the screened crack-free short-circuit product, monitoring leakage current in real time, and terminating the test when the sudden change amplitude of the current exceeds a second preset threshold value; then acoustic scanning is carried out on the protective cover surface and the cutting surface, and two-dimensional coordinates of defect points on the two surfaces are extracted; and calculating the three-dimensional coordinates of the defect in the MLCC through image registration and a spatial inversion algorithm. According to the method, the internal hidden short circuit defect can be identified and positioned in a non-destructive manner, the positioning precision and the analysis efficiency are improved, and the method is suitable for batch failure analysis and manufacturing process optimization.

The invention relates to the technical field of electrical protection, and discloses an electrical equipment protection device with a fault early warning function, which comprises a shell, a circuit board, a sensor group, an alarm component, a main control unit, an early warning execution module and a protection execution module. A cover plate with a visible glass window is arranged in front of the shell, and a display and control buttons are arranged on the circuit board. The sensor group collects operation parameters of electrical equipment, and the main control unit compares a preset threshold value, analyzes historical data, identifies abnormity, predicts fault risks and generates an early warning or protection instruction. And the early warning execution module triggers sound-light alarm and remote communication according to the instruction, and the protection execution module executes operations such as power failure. The electrical equipment protection device with the fault early warning function has the advantages that the operation parameters of the electrical equipment are monitored in real time, the advanced fault analysis algorithm is used for carrying out risk pre-judgment, and an early warning and protection mechanism is triggered in time.

The invention provides an inductor aging failure analysis and prediction method and system based on edge computing, and relates to the technical field of Internet of Things and edge computing, and the method comprises the steps: collecting the physical state and electrical parameter data of an inductor in real time through a multi-source sensor group; carrying out data fusion processing on the edge computing node, and extracting a dynamic feature vector related to aging failure; a dynamic failure threshold value is generated based on historical data, and real-time matching and early warning are achieved; updating a lightweight analysis model by adopting an online learning mechanism to adapt to a nonlinear aging process; the abnormal data and the feature vectors are uploaded to a cloud for failure root cause analysis and global model optimization, and optimization parameters are fed back to edge nodes; and dynamically distributing calculation tasks according to the edge node calculation load. According to the method, the problems of multi-source data fusion, dynamic threshold setting, model lightweight deployment, global optimization and the like are effectively solved, and the accuracy and the real-time performance of aging failure prediction of the inductor device are improved.