876 results about "Failure rate" patented technology

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention discloses a fault prediction method and system for deviation rectification of a laser die cutting and winding all-in-one machine, and belongs to the technical field of intelligent manufacturing and industrial equipment fault prediction.The method comprises the steps that multi-dimensional sensor data of a deviation rectification mechanism is collected and preprocessed; a sequence variability coefficient is calculated through a sliding window, and a high-risk abnormal time period is screened in combination with a dynamic threshold value; an improved Transform prediction model is constructed to carry out offset trajectory prediction; fault judgment is carried out, and the model is deployed in edge computing equipment or a server to realize efficient prediction; according to the fault prediction method and system for deviation correction of the laser die cutting and winding all-in-one machine, prediction errors can be reduced, the early warning advance and accuracy can be improved, redundant calculation can be reduced, and the model efficiency can be improved to adapt to industrial deployment; potential fault recognition and early warning are achieved, the equipment fault rate and the shutdown risk are reduced, and remarkable economic and social benefits are achieved.

The invention relates to the field of suturing systems of medical instruments, and discloses a suturing system. The suturing system includes a control handle, a tube, a receiving cartridge, and a needle. The control handle comprises a shell, a driving mechanism and a rotation indicating piece, the rotation indicating piece is rotatably connected to the shell, and a plurality of position marks are arranged on the periphery of the shell. An actuating shaft in the driving mechanism is coupled to a first gear, an actuating pipe is coupled to a second gear, and the first gear and the second gear are arranged in the longitudinal direction and rotate synchronously. When the rack moves longitudinally, the conversion gear can be driven to rotate so as to drive the first gear to rotate, and the first gear and the second gear respectively drive the actuating shaft and the actuating tube to do longitudinal reciprocating motion, so that the needle is alternately combined with the actuating tube and the receiving bin. The driving mechanism in the sewing system is reliable in structure, and the failure rate can be effectively reduced. In addition, the rotation indicating piece is used in cooperation with a position mark arranged on the shell so that the positions of the shell and the driving mechanism after rotation can be indicated, and operation is convenient.

The invention belongs to the technical field of bulk cargo wharf operation and maintenance situation awareness, and relates to a bulk cargo wharf operation and maintenance situation awareness method and system. The residual life of the equipment is calculated through the residual life probability distribution of the equipment predicted by the constructed residual life probability distribution prediction model, and the failure rate of the equipment is predicted through the constructed situation awareness model according to the health degree index of the equipment and the residual life of the equipment, so that equipment alarm signals of different grades are obtained. An auxiliary management scheme is generated and executed according to the equipment alarm signal, the equipment user maintenance time is reasonably planned, and the equipment utilization rate and the field production efficiency are improved. Health state monitoring and operation and maintenance situation awareness of the bulk cargo wharf are achieved, the equipment utilization rate and the field production efficiency are improved, and the turnover rate and turnover efficiency of wharf cargos are improved.

The invention discloses an efficient automatic paper box production system and a manufacturing method thereof. The manufacturing method comprises the steps that an automatic detection system comprehensively recognizes raw materials, checks thickness, humidity and texture parameters, adjusts production line parameters according to detection results and isolates unqualified materials; the production line automatically switches process configuration according to orders, cutting, folding and bonding parameters are adjusted, and a mechanical arm accurately adjusts a folding path; the system monitors the production link in real time, automatically optimizes and adjusts the production line, and corrects the folding precision deviation; in the quality detection link, the appearance, size and printing defects of the paper box are detected through image recognition, and unqualified products are automatically shunted; the production data is uploaded to the big data module in real time, and fault early warning and production strategy adjustment are carried out; according to the efficient automatic paper box production system and the manufacturing method thereof, intelligent control, flexible production switching and automatic quality detection technologies are integrated, existing defects are overcome, the production efficiency is improved, the fault rate is reduced, and the adaptability and flexibility of the system are enhanced.

The invention relates to the technical field of meteorological and ocean numerical forecasting, and aims to solve the problems of strong manual dependence, poor timeliness, high error risk and low resource utilization rate in coupling operation of a WRF atmosphere model and an ROMS ocean model. The method comprises the following steps of: storing an initial file into a date catalogue which is dynamically generated according to a YYYMMDD format after the initial file is subjected to integrity verification; the timed task checks nc.lock / nc.tone files every X minutes, a lock starting model is created when no lock exists and an initial file exists, and an ROMS needs to confirm nc.tone of a WRF result directory first; automatic exception processing is carried out when the model is overtime, and after completion of the automatic exception processing, a Done is created, a lock is deleted, resources are released, and computational nodes are dynamically allocated. According to the method, automatic scheduling is achieved through double-file marking, manual operation is shortened to be within one minute, model connection delay is shortened to be within five minutes, the failure rate is reduced, the resource utilization rate is improved, and a reliable scheme is provided for model business operation.

The invention provides a complex scene-oriented multi-modal AI Agent integrated development method and system, and the method comprises the steps: generating a layout graph G in an integrated development environment, and carrying out the consistency verification to obtain a constraint set C; a graph neural network is adopted to represent the arrangement graph to obtain graph embedding Z, and an environment state s is formed by combining key performance indexes in a sliding window; and in an action space AC limited by the C, jointly deciding computing power node selection, concurrent resource quota adjustment and semantic caching or semantic routing parameter configuration according to a reinforcement learning strategy pi, and executing actions to optimize throughput, tail delay, failure rate and resource contention degree. Through a closed loop of verification-constraint-learning-execution-monitoring-updating, cross-modal consistency and computing power scheduling are integrally optimized, training convergence and online stability are improved, long-tail time delay and the failure rate are remarkably reduced, the resource utilization rate is improved, and the method is suitable for large-scale popularization and application. The method is suitable for the fields of medical diagnosis, industrial quality inspection and equipment maintenance, retail recommendation and shopping guide and the like.

The invention relates to the field of industrial automatic quality control, and discloses an automatic production line real-time quality control system based on multi-sensor fusion. In the prior art, the problem of incomplete perception-decision-control closed loop mainly exists, the system can only realize quality detection and alarm generally, and parameter adjustment still needs manual intervention. According to the method, standardized data are obtained through the multi-source heterogeneous data sensing module; an intelligent fusion module is adopted to generate a quality state vector; the advanced prediction and intelligent decision-making of the quality trend are realized based on the prediction decision-making module; real-time closed-loop regulation and control of process parameters are completed through the precise execution module; and continuous optimization of the system is realized by means of a self-evolution optimization module. According to the invention, a complete closed loop from quality perception to control execution is established, the accuracy and automation level of quality control are obviously improved, and the reject ratio of products is effectively reduced.

The invention provides a method for predicting the working failure rate of a railway vehicle relay, and the method comprises the steps: classifying data according to a preset dimension to generate a structured feature set, constructing a time sequence feature matrix, and defining a multi-dimensional analysis coordinate system based on the classification dimension of the feature set; determining an initial prediction constraint condition according to the relay life distribution model and the fault mode library, executing dynamic prediction simulation on the feature set based on the time sequence feature matrix, simulating degeneration behaviors of the relay under various working conditions through a state transition simulation engine, generating an initial fault rate curve, and calculating the fault rate of the relay according to the initial fault rate curve. And finally, carrying out iterative correction on the initial fault rate curve by adopting an integrated optimization algorithm to generate a multi-dimensional fault rate prediction model. The invention further provides a system for predicting the working failure rate of the railway vehicle relay. According to the method, the adaptability and accuracy of prediction are greatly improved, and the degeneration behavior of the relay under the actual complex working condition can be simulated more truly.

The invention relates to the field of intelligent manufacturing and production scheduling, and discloses a heater assembly line flexible scheduling method based on production data, and the method comprises the steps: S1, obtaining the production data of a heater assembly line and corresponding environment parameters; initializing an assembly line scheduling constraint condition; wherein the assembly line scheduling constraint condition is used for defining a processing time range, an equipment load threshold value and a material supply period reference value allowed by each process in the heater assembly process; s2, if the environmental parameters exceed a preset safety threshold, dynamically correcting the real-time production data according to the environmental parameters to obtain corrected real-time production data; wherein the environment parameters comprise temperature, humidity and air cleanliness, the scheduling effect evaluation model and the association rule mining algorithm are introduced, the production efficiency improvement rate, the equipment failure rate reduction degree, the material waste reduction amount and the like can be quantitatively evaluated, and the influence relation of the key equipment operation parameters on the scheduling effect is mined.

The invention discloses a novel power system supply and demand risk assessment method based on multiple spatial-temporal scales, and relates to the technical field of power system supply and demand risk assessment, and the method comprises the following steps: obtaining a time-varying fault rate set and a dynamic transmission limit set of power grid equipment based on extreme weather prediction data; fusing the time-varying fault rate set, the dynamic transmission limit set and power supply, load and new energy power generation data to obtain a risk scene library; screening the risk scene library to obtain a typical risk scene set; analyzing the typical risk scene set, and outputting a supply and demand risk index under each time-space unit; and performing regional division on the power system based on the supply and demand risk indexes, and generating corresponding early warning and management and control suggestions. According to the method, the problem that the evaluation result is not accurate due to the fact that the time-varying characteristics of the equipment state are ignored in extreme weather is solved, and accurate decision support is provided for disaster prevention and supply protection of a power system.

The invention relates to the technical field of intelligent control and predictive maintenance of heating water heaters, in particular to a wind pressure switch control system and method for a heating water heater. The method comprises the following steps of: extracting a starting pressure curve slope, a steady-state pressure fluctuation frequency spectrum entropy and a shutdown pressure decay time constant by collecting a time sequence pressure signal in a working period of a fan so as to generate a dynamic feature vector; comparing the vector with a preset reference health vector by the system, and resolving the health state confidence and the state deviation drift rate of the system in real time; when the health confidence coefficient is lower than a safety threshold value or the deviation drift rate is too high, the system executes forced safety locking; if the health degree is slightly reduced, the pre-purging time of the fan is actively prolonged as compensation; according to the method, conversion from fault response to predictive maintenance is achieved, early performance degradation symptoms such as fan abrasion and air duct dust deposition can be recognized in advance, post-maintenance is converted into beforehand intervention, and the fault rate and maintenance cost are effectively reduced.

The invention relates to the technical field of vehicle maintenance, and provides a vehicle part abrasion determination method and a vehicle. The method comprises the steps that vehicle driving data and external environment data in the current time period are obtained, and the abrasion influence coefficient of the external environment on a part is determined through the external environment data; the current wear value of the component is determined according to the wear influence coefficient and the vehicle running data, component loss prediction in combination with the external environment and the vehicle running state is achieved, the predicted component loss better conforms to the actual use condition, more accurate maintenance prompt can be achieved on the basis of the predicted component loss, the risk of misjudgment of maintenance is avoided, and the maintenance efficiency is improved. The purpose of on-demand adaptive maintenance is achieved, the problems of excessive maintenance and insufficient maintenance caused by periodic maintenance are solved, the maintenance cost and the failure rate of vehicle parts are reduced, and the active maintenance willingness of a user can be improved while the driving safety of the vehicle is guaranteed.

The utility model discloses a transfer type multi-arm caliper logging instrument which comprises a shell, vertical holes are formed in the outer wall of the shell at equal intervals, a vertical pipe is fixedly connected to the interior of the shell, electric push rods are installed on the outer side of the vertical pipe at equal intervals, sliding rails are fixedly connected to the outer wall of the vertical pipe at equal intervals, and the sliding rails correspond to the vertical holes. A moving seat is slidably connected to the outer wall of the sliding rail, the output end of the electric push rod is in clearance fit with the moving seat, a compression spring is arranged in the moving seat, and the two ends of the compression spring abut against the electric push rod and the moving seat respectively. The utility model relates to the technical field of oil well logging, and solves the problems that in the prior art, measuring arms of a multi-arm hole diameter logging instrument need to be unfolded or folded simultaneously, the complexity and failure rate of a mechanical structure are increased due to synchronous operation, the stability is poor, the maintenance cost is correspondingly increased, the measuring arms are prone to abrasion due to long-time friction with a well wall, and the measurement precision is high. And the measurement precision and the service life of the equipment are influenced.

The invention discloses a reliability index distribution method and system suitable for a ship power system. The objective of the invention is to overcome the defects of a traditional ship power system reliability evaluation method in the aspects of dynamic performance, real-time performance and adaptability. According to the method, real-time operation data, including temperature, pressure, vibration frequency, abrasion loss and other key parameters, of all subsystems in a ship power system are collected, and dynamic reliability influence factors are constructed based on the data. By introducing a deep reinforcement learning model, the system can adjust the weight coefficient of each subsystem according to the current dynamic reliability influence factor. In addition, in combination with historical fault data, a fault propagation path is modeled through a graph neural network (GNN), and a fault rate correction factor is calculated, so that the accuracy of reliability distribution is improved. And finally, verifying the rationality of a reliability distribution result through a Monte Carlo simulation method, and dynamically adjusting system parameters according to a simulation result to ensure stable operation of the ship power system.

The invention discloses a management method and system for improving the receiving rate of spare parts purchased in the engineering period of a nuclear power plant, and relates to the technical field of industrial automation and monitoring, and the method comprises the steps: extracting consumption records after commercial transportation for units of the same supplier in a group, generating a list of spare parts purchased in the engineering period, and removing low-failure-rate parts; high-frequency consumables are retained to improve pertinence. And secondly, for spare parts which are not present, not scheduled and wide in coverage, a framework protocol is established with a manufacturer to lock unit prices and agree on demand supply, so that inventory occupation caused by mass transfer is avoided. And finally, executing a four-consistency principle on the on-site spare parts, namely checking an EOMM manual, and finally confirming a list, spare part real objects and on-site installation real objects. When the model or the code is upgraded, the specification is upgraded and solidified in combination with the commitment function of the manufacturer and the EOMM manual; when differences occur, interchangeability argumentation is carried out through technical analysis and an item replacement process, spare parts which are argumented to be available are put in storage and updated to an equipment spare part BOM and a template work order, and verifiability, traceability and efficient receiving of the spare parts are achieved.

The invention provides an intelligent ERP (Enterprise Resource Planning) system data management method based on business and financial fusion. The method comprises the following steps: analyzing order priority and resource demand through historical load and real-time failure rate, determining a key order, analyzing emergency degree and delivery deadline identification priority, generating a production schedule, and adjusting task time and equipment distribution; analyzing the scheduling scheme, determining a resource demand change, determining a financial budget allocation proportion according to the resource demand change, and determining a production and financial connection time point according to the financial budget allocation proportion; and determining a fund examination and approval priority order according to the production and finance connection time point, correcting a production schedule in combination with a connection time point offset error determined by analyzing a production time table and material demand change, and generating a resource allocation scheme.

The invention relates to the field of operation and maintenance management of Internet of Things equipment, in particular to a periodic maintenance task automatic scheduling system and method, and the method comprises the steps: firstly collecting vibration, current and environment signals, and constructing a three-dimensional digital twin map; obtaining a health index and a remaining available life in a preset window through time sequence alignment convolution and an ordinary differential equation recursive network, generating a risk coefficient through index mapping, and driving a reaction-diffusion equation to form a continuous degradation field; the scheduling optimization unit obtains an initial solution through symplectic gradient Hamiltonian-Monte Carlo sampling, and maintenance scheduling data meeting resource constraints are generated through bose sampling, topological bose evolution and dual gaming; the mixed reality terminal displays a risk hotspot and an operation instruction on site, retest data is recorded after maintenance is completed, and reinforcement learning updates a risk assessment and scheduling model on line according to an execution result to form a closed loop; according to the method, accurate quantification of risks, global optimization of scheduling and adaptive feedback of execution are realized, and the fault rate and the maintenance cost can be remarkably reduced.

The invention relates to the technical field of gas pipe network safety monitoring, and discloses a gas risk intelligent monitoring method and system based on multi-source data, and the method comprises the steps: obtaining static attribute data, dynamic sensing data and future event data of a gas pipe network, and constructing a dynamic attribute graph; inputting the dynamic attribute graph into a time-space diagram neural network model, and predicting the physical failure probability of each node; calculating the time-varying failure influence of each node based on the future event data and the cost data; and calculating the value-at-risk of the node by combining the physical failure probability and the time-varying failure influence, and generating a maintenance decision through an optimization model based on the distribution of the value-at-risk. The system comprises a data acquisition module, a failure rate prediction module, a time-varying failure processing module, a value-at-risk calculation module and a maintenance decision generation module. According to the method, the time-varying influence function and the investment portfolio model are constructed, the risk is quantified prospectively, the maintenance decision is optimized, the problem of evaluation lag is solved, and the operation and maintenance efficiency and safety of the pipe network are improved.

The utility model relates to an automatic feeding and discharging gripper device of a machine tool, which comprises a robot flange, a feeding bottom plate is arranged on the left side of the robot flange close to the top, a motor tightening device is arranged on the left side of the feeding bottom plate, a motor mounting plate is arranged on the left side of the motor tightening device, and a synchronous pulley driving device is arranged on one side of the motor mounting plate. The device has the advantages of being compact in structure, reasonable in transmission, comprehensive in function, easy and convenient to operate and the like. According to the automatic feeding and clamping device, a unique group feeding and clamping technology is adopted, and durability and safety are fully considered in structural design according to the automatic feeding and clamping requirements suitable for parts with certain size changes. By optimizing the structural design and the process manufacturing, the failure rate and the maintenance cost of the device are reduced.

The invention provides an engine VVT clamping stagnation processing method and system, and the method comprises the steps: obtaining the static characteristics and dynamic response characteristics of an engine during operation, detecting the operation state of the engine VVT, and judging whether the VVT clamping stagnation exists or not; if VVT clamping stagnation does not exist, according to the set period, the oscillation amplitude and the oscillation frequency, a battery valve is driven to enable a VVT actuator to do reciprocating motion within the stroke range; if VVT clamping stagnation exists, a corresponding voltage compensation coefficient is obtained according to the real-time storage battery voltage, and oscillation is performed by adopting a strong oscillation mode or a limit position recovery mode based on the voltage compensation coefficient; and detecting the triggering times of oscillation, judging whether the VVT clamping stagnation is solved or not when the triggering times reach a triggering times threshold value, if so, entering PID control, and if not, judging that the VVT has a fault. By means of the method, VVT clamping stagnation can be prevented and repaired in the early stage, and the VVT fault rate and engine performance fluctuation are reduced.

The invention provides a GRU-attention mechanism-based metering terminal meter fault rate prediction method, relates to the technical field of smart power grid and power equipment health management, and aims to solve the problems of insufficient feature capture and low prediction precision of complex time sequence data in the prior art. The metering terminal meter fault rate prediction method based on the GRU-attention mechanism comprises the following steps: constructing a multi-dimensional fault prediction index system based on equipment operation parameters, age limit parameters and environmental parameters, and preprocessing original data by adopting a median filtering method and Min-Max normalization; an attention mechanism is embedded into a GRU layer to form an end-to-end GRU-attention mixed prediction model, and the steps of model training and optimization, failure rate prediction, result analysis and the like are carried out. According to the method, through fusing the time sequence modeling capability of the GRU and the feature focusing capability of the attention mechanism, the accuracy of metering terminal meter fault rate prediction is remarkably improved, the early warning of the metering equipment fault rate is effectively realized, and an auxiliary decision basis is provided for operation and maintenance work.

The invention provides an intelligent inspection and remote diagnosis expert method and system for a fire pump station, and relates to the technical field of intelligent inspection, and the method comprises the steps: mapping the real-time operation data and historical maintenance records of the fire pump station to a domain knowledge graph, constructing a fault diagnosis reasoning chain, analyzing an abnormal state propagation path, generating maintenance suggestions, and retrieving historical cases. And evaluating scheme feasibility, constructing an optimal maintenance execution sequence and tracking a maintenance effect. The method can achieve the precise recognition of the abnormal state of the fire pump station, improves the fault diagnosis efficiency, optimizes the maintenance resource configuration, reduces the equipment fault rate, and guarantees the safe and reliable operation of the pump station.

The invention discloses a test fixture and a test method suitable for various circuit boards, and the test fixture comprises a bottom plate, the top of which is provided with at least one first positioning groove; each fixing assembly comprises a fixing plate and a clamping groove structure, and the bottom of each fixing plate is provided with a fixing rib in sliding fit with the corresponding first positioning groove; the test probe assembly is detachably installed on the first set of fixing assemblies, the test probe assembly comprises a PCB, test probes and a probe pressure adjusting structure, the probe pressure adjusting structure comprises an elastic support installed on the PCB and a driving mechanism driving the elastic support to stretch out and draw back, and the multiple test probes are installed on the elastic support; and the electric connection assembly is detachably mounted on the second group of fixing assemblies. The test fixture provided by the invention can adapt to the test requirements of different types of circuit boards, the test pressure of the probe can be freely adjusted, the test fixture is simple and reliable in structure, and the fault rate and the maintenance cost are effectively reduced.

The invention relates to the technical field of stamping dies, and provides an automatic replacement control method and system for a stamping head of a stamping die. The method comprises the following steps: constructing a multi-dimensional sensing network, and collecting a multi-dimensional working data flow of a stamping head; an RFID tag is embedded in the stamping head, and the model and use data of the stamping head are read; according to the sensing network and the model data, establishing an anomaly identification three-channel, mapping and identifying a data stream, and outputting an anomaly feature set; and the working state is predicted based on the use data and the abnormal feature set, and an automatic replacement mechanism is triggered through the predicted state to control replacement of the punching head. According to the method, the technical problem that the production efficiency and the service life of equipment are affected due to untimely replacement or misjudgment caused by the fact that a traditional stamping die depends on manual experience to judge the replacement time of the stamping head is solved, and the aim of accurately monitoring and predicting the working state of the stamping head in real time through multi-dimensional perception and data fusion is achieved; and the timeliness and accuracy of automatic replacement are improved, the equipment failure rate is reduced, and the technical effects of production continuity are guaranteed.

The invention provides a converter valve failure rate evaluation method, a storage medium and a direct-current power transmission system. In one working voltage period of the IGCT device of the converter valve, working voltages of the IGCT device in each of a plurality of preset time intervals are obtained, and a plurality of working voltages are obtained; the instantaneous failure rate of the IGCT device corresponding to each working voltage is determined according to the relationship between the failure rate and the voltage, the relationship between the failure rate and the voltage represents the corresponding relationship between the instantaneous failure rate of the IGCT device and the working voltage of the IGCT device, and the relationship between the failure rate and the voltage is constructed based on environmental parameters; determining the average failure rate of the IGCT device in the working voltage period based on the instantaneous failure rate and the working voltage period; and determining the failure rate of the converter valve based on the average failure rate and valve group configuration parameters of the converter valve, wherein the valve group configuration parameters comprise the number of single valves of the converter valve and the series number of IGCT devices in each single valve. The problem that the accuracy of converter valve failure rate evaluation is low is solved.

The invention discloses a protection device reliability evaluation method considering random change of environment temperature. The reliability evaluation method comprises the following steps: step 1, considering randomness and seasonal fluctuation of temperature change, and adopting a random process to describe an environment temperature random change process; 2, according to the failure rate, using an Arrhenius model and an exponential distribution function to describe life distribution of the protection device at the static absolute temperature, namely a reliability function of the protection device; step 3, combining the step 1, the step 2 and an accumulated loss principle to obtain a reliability function and a failure rate function under the dynamic temperature; and step 4, calculating the change conditions of the reliability and the failure rate of the protection device along with time under different random conditions, thereby forming the evaluation of the reliability of the protection device, and providing a more accurate method for the reliability evaluation of the protection device.

This application discloses a method and system for online condition assessment of permanent magnet motors based on multi-scale fuzzy entropy, relating to the technical field of permanent magnet motor fault diagnosis and reliability index analysis. It solves the technical problems of existing technologies that often rely on macroscopic parameters such as failure rate, making it difficult to quantify the degree of motor performance degradation and lacking consideration for real-time online condition assessment of permanent magnet motors, resulting in low accuracy and real-time performance of permanent magnet motor condition assessment. The method generates a radial air gap magnetic flux density difference signal based on the radial air gap magnetic flux density signal, and uses this to generate a multi-scale fuzzy entropy and a permanent magnet demagnetization assessment model. It generates the permanent magnet condition result based on the multi-scale fuzzy entropy corresponding to the online radial air gap magnetic flux density signal, and eliminates magnetic flux density background interference through the radial air gap magnetic flux density difference signal. By characterizing the subtle differences in signal complexity under different demagnetization states through multi-scale fuzzy entropy, it achieves a quantitative description of the degree of permanent magnet degradation, improving the real-time performance and accuracy of condition assessment.

The application relates to the field of electronic component manufacturing, in particular to a forming process of an inductor coil, which comprises the following steps: step one: using a first bending jig to preliminarily bend the end part of a double-layer coil; step two: using a second bending jig to further bend the preliminarily bent end part of the coil to reach a preset bending shape; and step three: using a detection and correction jig to correct the bent end part of the coil to ensure that the shape meets the forming requirements. The application solves the problems that in the prior art, the bending shape and size of the end part of the inductor coil are difficult to accurately control, the electromagnetic performance and connection stability are affected, and the lack of effective detection means leads to high product failure rate and increased production cost.