170 results about "Fault rate" patented technology

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 technical field of data processing, and particularly provides an automatic fault tracing method, system and device based on topological coding and a medium, and the method comprises the steps: distributing a unique topological code reflecting the hierarchical position of physical equipment for the physical equipment in a factory power distribution system; the method comprises the following steps: collecting real-time operation data through an edge collection terminal, and binding topological codes to form a real-time data stream with a label; constructing a topology dependency degree model, and defining the inherent failure rate of each node and the dependency degree of each node on a superior node; when the fault of the target equipment is detected, reversely traversing upstream nodes along a topological path based on the model, and calculating the fault suspicion probability of each upstream node by adopting a probability propagation algorithm; and carrying out normalization processing on the probabilities, fusing real-time state data, carrying out weighted correction, and finally generating a fault diagnosis report sorted according to the probabilities. According to the invention, by introducing a multi-level topological coding system and a probability propagation algorithm, rapid and accurate positioning and intelligent diagnosis of faults are realized.

The invention provides an anomaly analysis method combining a fault propagation path and a topological structure, and the method comprises the steps: calculating an overall recovery time estimation value under different topological structures according to the business importance and a recovery time target, and obtaining a candidate topology scheme set meeting the recovery time requirement; for the candidate topology scheme set, key nodes and key links are extracted, a fault scene is simulated based on component fault rate and link fault rate parameters, a fault propagation path is analyzed, and the influence of faults on service quality recovery time is evaluated; obtaining an optimal topology scheme considering the recovery time and the dependency strength constraint through the service recovery time output by the service recovery time model; in the solving process, load balancing capacity and fault-tolerant backup capacity are introduced as constraint conditions.

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 technical field of model training, in particular to a power transmission line fault rate prediction model training method and system based on machine learning. According to the method, an improved inverse distance weighted interpolation algorithm is adopted, timeliness weight, reliability weight and terrain constraint weight are introduced, regional meteorological grid point data are mapped to a tower coordinate position, and a time sequence is aligned; carrying out statistics on occurrence frequencies of meteorological factors in the fault samples and the non-fault samples, and calculating a single-factor association weight and a multi-factor collaborative association weight; combining the meteorological factor feature vector, the association weight and the fault tag to form a training data set; and constructing a neural network model containing an attention layer, calculating an attention weight based on a single-factor association weight, and training the model. According to the method, the attention mechanism is guided through the association weight, so that the model preferentially pays attention to the high-association-degree meteorological factors, and the prediction precision is effectively improved.

The invention provides a knowledge graph construction method and device and a fault diagnosis method and device.The fault diagnosis method comprises the steps that problem information which is input by a user and associated with a family bandwidth fault is expanded; performing entity extraction from the expanded problem information based on the trained pointer network model; the extracted entities are mapped to a constructed knowledge graph to screen out an entity list, the entity list comprises one or more entities, and the similarity between each entity in the one or more entities and the extracted entities is larger than a preset threshold value; adjusting the path weight corresponding to the path between the entities in combination with the context information associated with the user; wherein the paths refer to the paths among the entities in the knowledge graph, and the larger the weight of the paths is, the higher the failure rate among the entities in the corresponding paths is; and screening out a target path from the entity list based on the adjusted path weight, and determining a corresponding family bandwidth fault from the knowledge graph based on an entity corresponding to the target path.

The invention discloses a wire harness processing control method and system based on artificial intelligence, and the method comprises the steps: collecting sensor array data, carrying out the normalization processing, forming a standardized parameter set, precisely recognizing an abnormal mode through a classification algorithm, marking a potential fault point, extracting a time sequence feature sequence, and generating a prediction deviation vector through a prediction algorithm, analyzing dominant influence factors, grouping similar deviation modes through a clustering algorithm, and judging process adjustment requirements; and an optimization instruction sequence is generated, a production scene is matched based on historical data, the adjusted parameter configuration is dynamically injected into a control system, and the parameter model is continuously optimized through real-time monitoring and feedback circulation. According to the method, through data-driven anomaly detection and prediction adjustment, the stability and efficiency of the manufacturing process are remarkably improved, the fault rate is reduced, and intelligent machining state optimization is achieved.

The invention relates to the technical field of intelligent management of construction elevators, in particular to a digital intelligent management system for construction elevators, which comprises an operation monitoring module used for collecting operation state data of each construction elevator in real time through a sensor network, detecting fault conditions and performing early warning; the intelligent scheduling module is used for intelligently distributing transportation tasks based on the passenger flow data, the material transportation demand data and the operation state data of each construction elevator; the predictive maintenance module is used for acquiring key component parameters of each construction elevator, predicting the residual life according to the key component parameters in combination with a fault model, and generating a maintenance work order in advance so as to reduce the fault rate; the authority management module is used for limiting unauthorized personnel to use the construction elevator; and the digital archive management module is used for recording the operation state, the fault history and the maintenance record of the construction elevator and generating a complete equipment life cycle archive. According to the invention, fine and informatization management of the whole life cycle of the construction elevator can be realized.

A control method for the analog input / output setting mode of a hydro-generator monitoring system includes a newly added program for identifying input / output pulses. The control logic is as follows: ① If an input pulse command of 1 is detected, the analog input / output setting value = analog input / output setting value + fixed step size; otherwise, the analog input / output setting value remains unchanged. ② If an input pulse command of 1 is detected, the analog input / output setting value = analog input / output setting value - fixed step size; otherwise, the analog input / output setting value remains unchanged. A new analog input / output setting value initialization program is also included, employing the following control logic: When other control modes are switched to analog input / output setting mode, the current input / output setting feedback value sent by the governor is assigned to the analog input / output setting value as the initial value. This invention can improve the guide vane opening and active power regulation speed, enhance equipment stability and reliability, reduce intermediate links and fault points, lower equipment failure rate, and ensure the safe, stable, and high-quality operation of the generator unit and the power grid.

The application discloses a ship-boat coupling type inspection robot for offshore wind turbine generator units, which comprises a drone and an unmanned ship, and a ship body monitoring module for assisting the drone in monitoring tasks is arranged on the unmanned ship; the body of the drone is in a cylindrical frame structure, can conveniently pass through inside a wind turbine tower drum, and the body is provided with a drone monitoring module; the drone monitoring module comprises an image acquisition device, a sound and image acquisition device and a vibration acquisition device, and can effectively acquire various data inside the tower drum of the wind turbine generator unit. The ship-boat coupling type inspection robot can realize all-around state monitoring and fault diagnosis of offshore wind turbine generator units by adopting the combined operation mode of the unmanned ship and the drone, can timely master the operation state of the wind turbine generator unit, early discover potential fault signs, reduce the fault rate, ensure safe and efficient power generation operation of the wind turbine generator unit, simultaneously changes the previous manual inspection mode, greatly saves the operation and maintenance cost, and ensures personnel safety.

The invention discloses a transmission mechanism of a road marking machine. The device comprises a bottom plate, a driving transmission chain wheel and a transmission shaft which are used for forming a transmission relation with a driving mechanism are rotationally mounted on the bottom plate, and a transmission chain wheel forming a transmission relation with the driving transmission chain wheel is fixed on the transmission shaft; a walking transmission chain wheel and a transmission gear which are used for forming a transmission relation with the walking mechanism are further fixed to the transmission shaft. A movable plate is further installed on the bottom plate in a swinging mode, a lineation transmission gear used for forming a transmission relation with the lineation mechanism is rotationally installed on the movable plate, and the lineation transmission gear can selectively form a transmission relation with the transmission gear through swinging. The marking machine has the advantages that driving force from the driving mechanism is firstly transmitted to the transmission shaft and then transmitted to the walking mechanism and the marking mechanism, the structure is simple and compact, a hydraulic system or multi-motor driving is not needed, the fault rate is low, and therefore the overhaul period can be effectively prolonged, the operation efficiency is improved, and the service life is prolonged.

The invention provides a method for reducing the failure rate of a unit air cooling island speed reducer. The method comprises the following steps that S1, a multi-module cooperative prevention and control system is built; s2, configuring a parameter monitoring module assembly; s3, standardizing the installation standard of the sensor; s4, building a data transmission and analysis platform; s5, starting an intelligent diagnosis and data tracing function; s6, executing a monitor calibration and maintenance process; s7, carrying out preventive maintenance operation; s8, starting a working condition linkage adjusting mechanism; s9, activating an operation and maintenance training auxiliary module; and S10, a supplement lubricating oil leakage monitoring link. According to the method for reducing the failure rate of the unit air cooling island speed reducer, full-dimension real-time monitoring and intelligent diagnosis of operation parameters of the speed reducer are achieved by building a multi-module cooperative prevention and control system, and by combining graded early warning and preventive maintenance, fault hidden dangers are accurately positioned and handled; the working condition linkage adjusting mechanism can actively adjust the operation state when parameters are abnormal, and fault expansion is avoided.

The invention relates to the technical field of reliability test and intelligent screening of electronic components, and discloses an intelligent optimization method for secondary screening of electronic components, which comprises the following steps of: 1, firstly, constructing a multi-dimensional screening parameter system of the electronic components; according to the method, a screening parameter system covers four categories of electrical performance, environmental adaptability, reliability and process consistency, and judgment deviation caused by a single parameter is avoided; meanwhile, parameter anomaly detection and relevance verification are introduced, even if a single parameter is qualified, relevance anomaly is still marked to be unqualified, and the missed judgment rate is reduced to 2% or below from traditional 8%. On the other hand, the scene adaptation module divides the application into four grades, each grade corresponds to different parameter threshold coefficients, and it is ensured that the screening standard is matched with the scene requirement; the fault feedback module counts the fault correlation degree through FMEA, when the fault contribution degree of a certain parameter is larger than or equal to 5%, the weight of the parameter is automatically improved, the threshold value is tightened, the screening model is optimized along with actual fault data, and the fault rate of downstream elements is reduced by 60% or above.

The present disclosure relates to a kind of multi-extreme weather adapted offshore electric-hydrogen-alcohol system multi-energy flow failure modeling and supply-demand evaluation method and device.The method includes: constructing system success flow model based on GO method, identifying key equipment failure transmission chain;GO chart is converted into fault tree, combined with the minimum cut set method and sequential Monte Carlo method Quantification system failure probability and equipment contribution;Build wind and light hydrogen alcohol power model, introduce operation control strategy, dynamically evaluate power and fuel supply and demand reliability index;Establish fault-supply coupling mechanism, under the extreme weather such as typhoon, cold wave, strong wind and rain, the output of wind and light and equipment failure rate are doubly corrected, and a three-dimensional comprehensive reliability index system is constructed.The present application realizes the complete causal chain modeling from equipment failure to supply shortage, improves the accuracy and comprehensiveness of system comprehensive reliability evaluation under multi-extreme weather scenario, and can be used for system optimization design, operation decision and risk prevention and control.

The invention relates to the technical field of stamping part machining, and discloses an industrial manipulator feeding and discharging automatic stacking mechanism which comprises a mechanical arm, a second adsorption assembly connected with the mechanical arm, a table body arranged on one side of the mechanical arm, a transfer table and an adjusting assembly arranged on the top of the table body, and a first adsorption assembly arranged on the adjusting assembly. The second adsorption assembly comprises a box body, the box body is arranged on the mechanical arm, and a third rotating shaft is rotationally connected to the box body; the manipulator grabs the blanks from the blank placing area according to a set sequence, the blanks are sequentially processed by a plurality of punching machines, and finally finished products are orderly placed in the finished product placing area, so that automation and continuity of the production process are realized, and the production efficiency is greatly improved. And on the other hand, the automatic stacking structure is precisely matched with the manipulator, so that the error rate and the fault rate in the operation process are effectively reduced, production interruption and loss caused by human factors or mechanical faults are reduced, and the overall production quality and stability are improved.

The invention relates to a model determination method, a fault rate determination method and a related device, and relates to the technical field of electronic information, the method comprises the following steps: according to an update process corresponding to a terminal device, determining a statistical parameter corresponding to the update process, the update process being used for describing the number of times of fault occurrence of the terminal device. The statistical parameters are used for describing distribution of the number of times that the terminal equipment breaks down. And determining an undetermined fault rate model corresponding to the first equipment type of the terminal equipment according to the statistical parameters and the failure strength model. And obtaining equipment data corresponding to the first equipment type. The device data comprises a first device activation number and a first device failure rate corresponding to the first device type in at least one specified time period. And according to the undetermined fault rate model and the equipment data, determining a target fault rate model corresponding to the first equipment type, the target fault rate model being used for predicting the equipment fault rate of the first equipment type. According to the invention, the quantizability and interpretability of the model can be improved.

The invention belongs to the technical field of power grid fault analysis and early warning, and particularly relates to a power grid fault risk early warning method based on a fault quantitative relation model, a storage medium and electronic equipment. According to the scheme, a fault quantitative relation model of dynamic and non-linear correlation between the fault rates of various power grid equipment lines and the meteorological levels of various severe meteorological types is established through preferential function fitting, and meteorological level thresholds corresponding to various power grid equipment in various severe meteorological types are scientifically set; clear and quantitative decision criteria are provided for starting fault defense measures or not, power grid fault risk early warning analysis is carried out, and early risk early warning of power grid equipment line faults in a severe meteorological environment is accurately and quickly realized. And key data support can be provided for response measures such as preparation of a power grid fault defense plan in advance, calling of emergency standby resources such as energy storage, configuration scale and the like.

The invention discloses a power grid fault data processing method and device and a medium, and the method comprises the steps: carrying out the multi-dimensional detection data collection of a target power grid, obtaining a power grid image and sensing parameters, and collecting the environment parameters in a target power grid environment; acquiring historical detection data of a target power grid, extracting a fault sample proportion coefficient, performing combination division on the historical detection data, and performing training of an integrated image fault identification branch and an integrated sensing fault identification branch; performing fault rate influence analysis and detection data influence analysis according to the environmental parameters to obtain a fault rate influence coefficient, an image influence coefficient and a sensing influence coefficient; according to the fault rate, the image and the sensing influence coefficient, the number of image recognition branches and the number of sensing recognition branches are calculated and obtained, fault recognition branch calling and fault recognition are carried out, a power grid fault recognition result is obtained, and the technical problem that the machine learning recognition fault perception rate and accuracy are low due to the fact that power grid line inspection fault data samples are few is solved.

The invention relates to the technical field of power distribution networks, in particular to a power distribution equipment pressure assessment and risk prediction method, which comprises the following steps of: analyzing fault causes of three typical power distribution equipment, namely a power distribution transformer, an overhead line and switch equipment, extracting key characteristic quantities influencing the pressure state of the power distribution equipment; a power distribution equipment pressure index calculation model is established by adopting a subjective and objective combination weighting method, a fault rate model based on a pressure index is proposed, and a BP neural network is introduced to quantify the relationship between an overhaul strategy and equipment fault rate rollback in consideration of the influence of equipment overhaul on the fault rate. According to the method, the key characteristic quantity is extracted, and the pressure index calculation model, the fault rate model and the BP neural network fault rate curve correction model are established, so that accurate evaluation and risk prediction of the pressure of the power distribution equipment are realized, the method has relatively high accuracy and reliability, and a powerful guarantee is provided for safe operation of the power distribution network.

This invention relates to the field of power system fault quantification technology, and discloses a method, system, equipment, and medium for quantifying distribution network line faults under typhoon weather. It calculates the wind-induced fault rate of distribution network lines by constructing a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors. With the goal of minimizing power outage losses in the distribution network, a coupled influence model of transmission network faults on the distribution network line fault rate is constructed and solved to obtain the optimal load shedding amount for each load node in the distribution network and its corresponding operational constraint boundary. Under this boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated using a double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results. The combined time-varying fault rate and type of distribution network lines are determined by integrating the two fault rates, achieving accurate quantification of distribution network line faults under the influence of transmission-distribution coupling.

The utility model discloses a bumper car head center control mechanism with a large rotation angle, which comprises a first support plate, a second support is fixedly connected to the lower surface of the first support plate close to one side edge, a first support is fixedly connected to the lower surface of the second support, and a second support plate is fixedly connected to the upper surface of the first support. The upper surface of the second supporting plate is rotationally connected with a first rotating shaft, the cylindrical surface of the first rotating shaft is fixedly connected with a first gear, the upper surface, away from the first rotating shaft, of the second supporting plate is rotationally connected with a second rotating shaft, the cylindrical surface of the second rotating shaft is fixedly connected with a second gear, and the second gear is meshed with the first gear; and the second supporting plate is rotationally connected with a third rotating shaft away from the first rotating shaft, the cylindrical surface of the third rotating shaft is fixedly connected with a third gear, and the third gear is meshed with the second gear. By means of the structure, the angle adjusting range of the vehicle head can be larger, operation is more flexible, operation is stable, and the fault rate is low.

The invention relates to the technical field of availability allocation, in particular to an inherent availability allocation method fully considering system redundancy and fault influence. The method comprises the following steps: selecting similar products, and listing component names, failure rates and redundancy numbers of the similar products and analyzed products; calculating an unavailable time estimated value of each part of the analyzed product by referring to fault influence data of similar products; calculating an unavailable time ratio of each component of the analyzed product based on the unavailable time estimated value of each component of the analyzed product; and by taking the unavailable time ratio of each component as a distribution coefficient, carrying out product availability distribution, and calculating the availability distribution value of each component of the analyzed product. According to the availability allocation method provided by the invention, based on theoretical data calculation, an availability index allocation method which is applicable to whether the next-level structure of a product is single or not and whether redundancy exists or not and can fully consider the fault influence is constructed, so that the accuracy of availability allocation is realized, and the application range is widened.

The invention provides a computing power resource operation fault defense method and device of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, obtaining historical data corresponding to computing power resources of target equipment, the target equipment being equipment providing the computing power resources in the intelligent computing center; s2, performing feature extraction on the historical data to obtain a correlation feature vector and a time sequence feature vector corresponding to each physical index in a plurality of physical indexes included in the computing power resource; s3, splicing the associated feature vector and the time sequence feature vector corresponding to each physical index to obtain a prediction feature vector corresponding to each physical index; and S4, predicting a target parameter value corresponding to each physical index based on the prediction feature vector, wherein the target parameter value is used for determining whether the computing power resource of the target equipment is abnormal or not. The fault rate of equipment can be greatly reduced.

The invention provides a small shredder, and belongs to the technical field of shredding tools. The technical problems that in an existing grater, a driving structure and a reset structure are complex, reset needs to be conducted through an independent button, and the fault rate is high are solved. A plurality of guide protrusions and guide sliding grooves are formed on the inner side wall of a guide seat in the small grater, a lower sliding block is formed on the peripheral wall of a shredding push rod, and sawtooth protrusions are formed at the upper end of the shredding push rod; the lower end of the shredding tool apron is rotatably connected with a rotating seat, an upper sliding block is formed on the peripheral wall of the rotating seat, the lower end of the upper sliding block protrudes out of the lower side face of the rotating seat and extends inwards, the lower end of the upper sliding block is provided with an upper guiding face, and the upper end of each guiding protrusion is provided with a first lower guiding face and a second lower guiding face. The upper guide face can abut against the sawtooth protrusions and the guide protrusions respectively. Through cooperation of the guide base, the shredding push rod and the rotating base, jacking and resetting of the shredding tool apron are achieved, the structure is simple, and the failure rate is low.

The application belongs to the technical field of circuit automation detection, and specifically provides a secondary alternating current loop insulation monitoring method and system based on multi-modal machine learning, comprising: processing high-frequency pulse current signals generated by insulation defect faults of a secondary loop through multi-element variational modal decomposition to obtain time-frequency domain features of fault signals under different modes; selecting feature parameters for fault diagnosis, using a local linear embedding algorithm to reduce dimensions of feature parameters of fault time domain reconstruction signals and fault frequency domain reconstruction signals, taking the reduced historical fault time-frequency feature vectors and real-time fault time-frequency feature vectors as a training set and a test set respectively, and classifying through a support vector machine algorithm to obtain fault types and fault rates of each fault type after classification. The method and system utilize that insulation defects of a secondary loop will cause partial discharge, and the partial discharge will generate high-frequency pulse current signals, and analyze the insulation state of the secondary loop by extracting features of fault signals.

The invention relates to a rigidity monitoring method, system and equipment based on liquid amount deviation differential and a medium. The method comprises the steps that braking operation of a driver is monitored in real time, and actual liquid consumption of a braking system in a current braking cycle is obtained according to the braking operation; according to a pressure model in an ABS control module, calculating to obtain reference liquid amount consumption of the current brake cycle; calculating a deviation differential between the actual liquid amount consumption and the reference liquid amount consumption to obtain a deviation change rate between the actual liquid amount consumption and the reference liquid amount consumption; determining the stiffness failure rate of the brake system according to the deviation change rate and the action of the ABS valve in the current brake cycle; and judging whether the stiffness failure rate exceeds a preset threshold, and if yes, outputting a failure signal. According to the method, the rigidity abnormity can be found in time when the ABS valve has potential failure but the braking system does not detect the failure, and a driver is prompted in time when the abnormity occurs, so that the driving safety risk is reduced, and the safety of the driver is guaranteed.

The application discloses a boiler fault detection method and device, electronic equipment and storage medium, and relates to the technical field of machine fault. A target operation parameter corresponding to a boiler to be detected in a current operation state is acquired, wherein the target operation parameter is used to represent an attribute characteristic parameter of the boiler to be detected; the target operation parameter is input into a trained fault prediction model to determine a fault detection result of the boiler to be detected; if the fault detection result indicates that the boiler to be detected has a fault, a target maintenance strategy of the boiler to be detected is determined. The application reduces the fault rate, improves the efficiency and reliability of fault determination, and greatly reduces the labor cost.

The application discloses a kind of carbon tank failure mode mining systems based on big data, the present application relates to automobile emission control technical field, including: three-domain fusion perception layer, for obtaining the multi-source data of carbon tank in the running process, and the multi-source data is uniformly encoded, constructs the three-domain fusion feature space of the physical structure parameter of carbon tank, chemical adsorption characteristic and real-time operation condition.The carbon tank failure mode mining system based on big data, through multistage collaborative screening and evolution type feedback cycle verification framework, without increasing hardware cost, significantly improve the accuracy and early warning capability of carbon tank fault diagnosis, can effectively identify early weak fault characteristics that traditional method is difficult to capture, and accurately predict fault evolution path and remaining useful life.Overall, it realizes the upgrade of carbon tank fault from passive response maintenance to active predictive maintenance, reduces the risk of emission exceeding standard and vehicle operation failure rate caused by carbon tank failure.

The invention relates to the technical field of fault analysis, in particular to an MES (Manufacturing Execution System) for automatically adjusting a server manufacturing process based on a current network fault, which comprises a fault data processing module, a fault trend prediction module, a risk component identification module, a propagation path tracking module and a parameter adjustment module. According to the method, continuous statistical process monitoring is carried out on multi-dimensional quality indexes such as the first-time power-on fault rate, the annual fault rate and the existing network batch problem of the server, the abnormal fluctuation trend of the quality indexes can be recognized in advance before an isolated fault event occurs, the system does not passively respond to the occurring fault any more, and the system reliability is improved. Through fusion analysis of environment and operation factors such as load and memory utilization rate during server operation and temperature and humidity of a data center where the server is located, complex combination conditions causing quality indexes to deviate from a normal range are deeply mined. And the specific probability and the time window of future fault occurrence are calculated in a quantization mode.

The utility model relates to the technical field of machine tool magazine tools, in particular to a magazine tool with a protective structure, which comprises a protective box. A base is fixed to the bottom of the protection box through a supporting rod. A placement cavity is formed in the protection box, and a placement piece is placed in the placement cavity; the placing piece is composed of a plurality of placing discs, and the placing discs are connected together through supporting columns; a threaded hole communicated with the containing cavity is formed in the bottom of the protection box, a threaded rod is in threaded connection with the interior of the threaded hole, a rotating shaft is fixed to the bottom of the threaded rod, and the top of the threaded rod is rotationally connected with the bottom of the containing disc located at the bottommost portion. The device is simple in structure, convenient to use, low in overall failure rate and high in reliability due to the fact that conventional components are adopted as a whole, and overall use cost is reduced, and overall working efficiency and working continuity are guaranteed due to the fact that intelligent components are not adopted.