156 results about "Failure data" patented technology

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention discloses a safety valve service life evaluation method based on pressure history statistics and dynamic damage accumulation. According to the method, only safety valve inlet pressure time sequence data is needed, a load cycle is extracted through an improved rain flow counting method, and the nonlinear damage increment is calculated according to an S-N curve model dynamically degraded along with the accumulated damage degree. According to the method, a self-adaptive failure probability threshold is established based on historical failure data, and probabilistic prediction of the residual life is realized. The model is clear in physical significance, only needs a single data source, significantly reduces the implementation complexity and cost, greatly improves the precision of life prediction by considering the degradation of material performance, and is especially suitable for predicting the accelerated failure risk of the safety valve in the later period of long-term service.

The invention relates to the technical field of data mining, in particular to a mining analysis method and system for vehicle wire harness fault data, and the method comprises the following steps: extracting a maintenance timestamp and a path segment index to construct a two-dimensional coding matrix, carrying out the segment division of a vehicle operation mileage, generating a total number of segment faults and a probability sequence, and carrying out the mining analysis. And executing logic comparison according to the serial numbers of the node major loop and the branch line, configuring a path weight parameter, executing tensor operation on the probability sequence and the weight parameter, obtaining an aggregation degree parameter, and constructing a mining analysis result. According to the method, a data multi-dimensional space topological structure is remodeled, dynamic recession mapping is calculated to capture a deep derivation relation, a probability sequence and a weight parameter are input into a sensing network to execute feature extraction, an aggregation degree index is obtained, and according to feature collection operation, space associated conduction paths of wire harness state degradation at different positions are deeply mined. And a fault data mining analysis system with a network deep topology feature analysis capability is formed.

The invention discloses a ship mechanical and electrical product accelerated life test stress profile generation method and device, a medium and a product, and relates to the field of ship reliability engineering and test.The method comprises the steps that working condition profile data and marine environment stress data of a target ship mechanical and electrical product under a typical navigation task are obtained, and a working condition-environment mapping model is established; according to the designed life target of the target ship mechanical and electrical product, the whole life cycle total damage to the target ship mechanical and electrical product is determined by using the working condition-environment mapping model and the failure physical damage model, an accelerated stress profile is generated by strengthening the stress level with damage equivalence as the principle, an accelerated factor is calculated, and an accelerated life test is performed; and failure data of the failure event is recorded, parameters of the failure physical damage model are calibrated, and the accelerated stress profile is iteratively optimized by using the calibrated failure physical damage model. Accurate and dynamic generation of the accelerated life test stress profile of the ship mechanical and electrical product is realized.

The invention discloses a metro vehicle supervision manufacturing multi-station data traceability control system, and relates to the technical field of industrial data traceability, and the system comprises a data collection module which obtains the original manufacturing data of each station of metro vehicle manufacturing; the feature extraction module is used for processing the original manufacturing data and generating behavior fingerprint vectors and station features; the identity analysis module is used for carrying out feature matching calculation and outputting a candidate component identity set and matching similarity; the logic verification module is used for carrying out logic consistency verification and determining the identity of the target component; and the traceability control module establishes a mapping relation with the original manufacturing data based on the identity of the target component, generates a unique traceability node, and writes the traceability node into a multi-station traceability data chain. According to the method, the problems of identification failure, data link breakage and affiliation errors caused by station switching or equipment sharing are solved, supervision data continuity and identity recognition accuracy are improved, and reliable support is provided for vehicle full-life-cycle data tracing.

The invention relates to a method for establishing an SIL dynamic evaluation model based on Weibull distribution, and belongs to the technical field of safety protection systems. The method comprises the following steps: (1) establishing a dynamic cumulative failure probability function model based on Weibull distribution according to obtained failure data through a reliability test; (2) calculating Weibull distribution parameters by using a median rank method, a linear regression fitting method and a least square method; (3) drawing a function model image by using MATLAB software; and (4) in combination with a fault tree method, establishing an SIL dynamic evaluation model based on Weibull distribution. According to the SIL dynamic evaluation model for the final executive component of the safety instrument system based on Weibull distribution, the dynamic change trend of the final executive component PFDavg along with time obtained by the model basically accords with the reality, the accuracy of SIL evaluation is improved, and scientific method support is provided for design personnel of the safety instrument system.

The invention discloses an unmanned aerial vehicle inspection method and system for a wind turbine generator set, and belongs to the technical field of intelligent operation and maintenance of the wind turbine generator set. The method comprises the following steps: establishing a digital inspection task model based on the layout and historical operation fault data of the wind generating set, and generating an initial inspection path scheme; controlling the unmanned aerial vehicle to fly along the path, and collecting multi-source inspection data by using an airborne sensor; carrying out processing and fusion analysis on the data, realizing abnormity identification and fault diagnosis, and outputting a diagnosis result containing a fault type and a severity level; and generating a structured inspection report according to a diagnosis result, and dynamically adjusting a flight path, a hovering position and sensor parameters. According to the invention, intelligent optimization of the inspection path and adaptive control of data acquisition are realized, the inspection efficiency and the data quality are improved, real-time diagnosis and predictive maintenance of faults are supported, the labor cost and the safety risk are effectively reduced, and the method is suitable for intelligent operation and maintenance of a large-scale wind power plant.

The invention discloses a rehabilitation equipment fault data monitoring method and system, and particularly relates to the technical field of electric digital data processing, comprising the following steps: S1, synchronously acquiring equipment operation time sequence data and patient interaction time sequence data, S2, performing feature extraction and adaptive fusion on the two types of data, and S3, acquiring the data of the equipment operation time sequence and the patient interaction time sequence; s3, equipment health prediction and rehabilitation curative effect deviation prediction are executed in parallel based on the fusion features, and the relevance of the equipment health prediction and the rehabilitation curative effect deviation prediction is analyzed through a cross attention mechanism, and S4, a personalized dynamic baseline model is combined, and an equipment fault risk value, a curative effect deviation degree and a relevance confidence degree are integrated to generate hierarchical monitoring feedback. According to the invention, by constructing an equipment-patient-curative effect ternary closed-loop analysis framework, the spanning from pure fault early warning to treatment effectiveness guarantee is realized, the problems of single monitoring dimension and unhooking with clinical curative effect in the prior art are solved, and the method has the outstanding effects of strong early warning perspectiveness, low false alarm rate and support of root cause analysis.

The application discloses an industry data management method based on big data, and belongs to the technical field of industry data management, and specifically comprises the following steps: constructing an after-sales data warehouse based on historical after-sales maintenance records; extracting the correlation relationship of spare parts and restoring the level of finished parts from the after-sales data warehouse to construct a coupling correlation topology; screening abnormal samples deviating from the expected failure mode, extracting corresponding supplier production process parameters and batch information, and forming a process parameter set; deconstructing the coupling correlation topology and combining the process parameter set to establish an attribution mapping parameter set; constructing a causal inference model between the attribution mapping parameter set and the failure mode to obtain the attribution weight of each supplier batch to the failure mode; and marking the supplier batch with an attribution weight exceeding a preset threshold as a target traceability batch and outputting the same. The application realizes accurate attribution from after-sales failure data to a supplier batch, and provides a quantitative basis for supply chain quality control.

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

The invention relates to the technical field of solid-state storage, in particular to a solid-state disk data consistency control method, and aims to solve the problems that a solid-state disk is poor in data consistency, high in power failure loss risk and difficult to balance writing efficiency and consistency under the scenes of power failure, parallel writing and garbage collection. Full-process consistency control is realized by constructing a'cache-persistent cache 'layered architecture and combining a multi-channel write synchronization, power failure emergency migration and periodic check repair mechanism: receiving data and storing the data in a cache firstly, distributing a multi-channel write task according to weighted polling and synchronizing the state; the standby capacitor is triggered to supply power during power failure, and the cache data is migrated to the persistent cache according to the FIFO; and according to a configurable period of 1-24 hours, flash memory block data are verified through SHA-256 hash, and when the flash memory block data are inconsistent, the flash memory block data are repaired from a persistent cache / backup block. According to the method, the data consistency compliance rate of the solid-state disk is larger than or equal to 99.9%, the power-down data loss rate is smaller than or equal to 0.01%, the write-in delay is reduced by 15%-20%, and the method is adaptive to consumption-level, enterprise-level and industrial-level solid-state disks.

The invention discloses a natural disaster emergency response method and system based on power failure monitoring, and the method comprises the steps: obtaining a set of power failure data, which comprises a power failure influence unit, a power failure public and special variable, a power failure cell and an initial power failure influence user number; and a data processing and analysis algorithm is introduced at the rear end to carry out collaborative analysis on a set of the power failure data, so that the total number of the power failure users is accurately predicted, and corresponding natural disaster emergency response is carried out. Therefore, the intelligence of emergency management and response of natural disasters can be realized, so that the disaster response efficiency is improved, and the loss is reduced.

The invention discloses an electronic hand accelerator rotating speed storage and control system, method and device, and relates to the technical field of commercial vehicle electronic control, the storage and control system is arranged in a storage control unit, and comprises a signal acquisition module, a logic processing module, a storage management module, an operation control module and an interactive output module. The system verifies a preset action sequence to activate a configuration mode when an engine is not started by collecting an external switch signal, the target rotating speed is calculated through an add-subtract switch, boundary amplitude limiting is conducted, and setting can be completed without a special calibration tool. And the operation control module executes and starts anti-galloping inspection, and activates constant rotating speed control only when detecting that the edge of the opening switch jumps and the power takeoff is physically combined. In addition, the system has the functions of abnormal power failure data protection and braking forced quit in operation. According to the invention, potential safety hazards caused by dependence on tools on rotation speed setting and forgetting to turn off the switch are effectively solved, and the robustness and operation safety of the system are improved.

The application provides a kind of urban rail burst event influence on passenger flow quantitative evaluation method.The method comprises: based on the multi-source data of urban rail transit, a multi-layer dependent network model of urban rail transit is constructed, a node and edge traffic propagation process is considered to construct an urban rail transit point-edge joint cascading failure model, the parameter calibration of the model is carried out using the historical failure data of urban rail, and a calibrated urban rail transit point-edge joint cascading failure model is obtained;the parameter information of urban rail burst event is input into the calibrated urban rail transit point-edge joint cascading failure model, and the calibrated urban rail transit point-edge joint cascading failure model outputs the influence of the burst event on urban rail transit passengers.The application is based on the physical law and real-time multi-source data of urban rail transit system, designs dynamic cascading failure rules, and accurately simulates the dynamic processes such as passenger flow transfer, station congestion propagation and multi-transportation mode connection obstruction under burst event.

The invention discloses an inverter post-evaluation method and device, equipment and a medium, and the method comprises the steps: determining the complete machine fault frequency, the complete machine fault type, the construction unit fault frequency and the construction unit fault type of a to-be-evaluated inverter through complete machine fault data and construction unit historical fault data; determining a first post-assessment score of the to-be-assessed inverter based on the complete machine fault frequency and the complete machine fault type of the to-be-assessed inverter, and determining a second post-assessment score of the to-be-assessed inverter based on the construction unit fault frequency and the construction unit fault type; based on the after-sales service data, determining a service type of the to-be-evaluated inverter, and based on the service type, determining a third post-evaluation score corresponding to the after-sales service data; and based on the first post-evaluation score, the second post-evaluation score and the third post-evaluation score, determining a comprehensive post-evaluation score of the to-be-evaluated inverter, thereby realizing comprehensive quantitative evaluation of the comprehensive quality and service level of the inverter, and effectively improving scientificity of equipment type selection and operation and maintenance decision.

The application relates to the technical field of unmanned aerial vehicle structure design and optimization, and discloses a layout optimization method based on an unmanned aerial vehicle landing gear system. The method comprises the following steps: a three-dimensional design model of the landing gear is constructed; real flight attitude data and a load spectrum are imported; stress nephograms and displacement fields under multiple working conditions are dynamically generated to accurately identify a region to be optimized; preset layout rule library is used for iterative adjustment; component layout is optimized until mechanical indexes are met; the optimized model is compared with a historical failure case database; a risk component list with geometric or mechanical similarity is extracted; and local reinforcement or topological reconstruction is carried out according to the risk component list to generate a final scheme. The method fuses dynamic load analysis and historical failure data, improves the accuracy of layout design, and identifies and prevents potential structural failure risks.

The embodiment of the invention relates to the technical field of information, and discloses a channel route switching method and device, a medium and a product, and the method comprises the steps: receiving an authentication request initiated by a user; acquiring a real-time network communication state index and a real-time authentication service execution index of the third-party channel; predicting the historical time sequence data of the third-party channel by adopting a time sequence prediction algorithm to obtain a transaction volume trend prediction result of the third-party channel in a future target time period; according to the transaction volume trend prediction result, determining a corresponding fusing judgment threshold value; the fusing judgment threshold value comprises a failure rate threshold value and a failure frequency threshold value; counting real-time network failure data and real-time service failure data of the third-party channel in the current time window, and respectively comparing the real-time network failure data and the real-time service failure data with the fusing judgment threshold; and when any comparison result meets the fusing condition, marking the third-party channel as an unavailable state, and executing route switching, so that different fusing mechanisms can be adopted according to different flow scenes.

The invention discloses an electricity consumption anomaly detection method and detection system based on cloud edge collaborative multi-modal incremental learning and knowledge distillation, and computer equipment. The method comprises the following steps: inputting original electricity consumption data after multi-modal high-dimensional feature processing into a pre-trained detection model; the detection model comprises a teacher model and a plurality of student models, the teacher model is used for generating a soft label, the soft label is used for optimizing the student models, and the student models are used for detecting the original power consumption data after multi-modal high-dimensional feature processing to obtain a power consumption anomaly detection result and outputting detection failure data when detection fails; generating an Adapter module according to detection failure data, embedding the Adapter module into a backbone network of the detection model, performing feature enhancement, and freezing parameters of the backbone network to optimize and update the detection model; and the teacher model updates the cloud knowledge base according to the detection failure data, and the student model optimizes according to the updated cloud knowledge base, so that the accuracy of the power consumption anomaly detection result is improved.

The invention relates to the technical field of park management and risk assessment, and provides an industrial park safety assessment method and system based on accident statistical analysis, and the method specifically comprises the steps: collecting failure data related to historical accidents, and carrying out the preprocessing of the failure data; building a historical data analysis model, carrying out the real-time safety tracking and monitoring of the entrance and exit of products in the industrial park, and evaluating and calculating the real-time safety capacity of the industrial park; and accessing the real-time safety capacity to a security sentry box monitoring background and a product transport vehicle driving navigation background, and executing a dynamic safety transport management strategy. The method solves the problems that in the prior art, the industrial park is a multi-enterprise and multi-worker dense personnel area, and in the transportation and loading and unloading process of dangerous goods in the industrial park, the dangerous goods are exposed in a high-temperature, flammable and explosive dangerous environment and a crossed area where personnel are concentrated and traffic is busy, so that the dangerous goods cannot be transported and unloaded. And the possibility that people are injured when a safety accident occurs is greatly increased.

The application discloses a kind of intelligent substation auxiliary system comprehensive monitoring platform, the application relates to the technical field of intelligent substation, the application includes the division module of perception domain, abnormal risk assessment module, abnormal processing module;The division module of perception domain is used to collect equipment historical failure data and divide equipment risk level, constructs the dynamic perception domain containing intersection area;Abnormal risk assessment module, with abnormal parameter calculation risk entropy value and diffusion index are obtained abnormal grade with equipment as node construction dependence atlas;Abnormal processing module, based on abnormal grade construction three-level resource pool adaptation processing demand.The application carries out to operation and maintenance resource configuration, improves abnormal response efficiency and reliability.

A kind of high-power wind turbine control method based on localization PLC, it includes: S1: central data processor reads unit operation history file from unit database, unit operation history file includes the historical failure data of unit and the ten-minute operation data of unit;S2: unit operation history file is split and cleaned, and the data of shutdown is eliminated;S3: from the historical failure data of unit, the occurrence frequency and occurrence time of each unit appearing overspeed, card paddle are counted;S4: according to the occurrence time of unit appearing overspeed, card paddle counted, the operation data of unit before and after corresponding time is inquired from the ten-minute operation data of unit and is carried out variable analysis;S5: central data processor transmits the key variable of each unit and variable operation threshold to the domestic PLC controller of unit, and the domestic PLC controller switches control according to the key variable of each unit and variable operation threshold the variable pitch strategy of unit.

The invention discloses a distribution network fault power failure event research and judgment method and system based on power failure data processing, and belongs to the technical field of power distribution networks, and the method comprises the steps: fusing multi-source heterogeneous power failure data streams in real time, and outputting a high-priority power failure event to-be-researched and judged list; carrying out alignment and joint verification on cross-domain multi-modal information by taking a fixed time-space stamp as an anchor point, eliminating invalid alarms, extracting a transient electrical process, and generating a multi-dimensional feature tensor containing a fault mechanism; reverse analysis of power grid topology constraints is carried out, a fault propagation path is backtracked step by step, and root cause equipment triggering a power failure event is positioned; solving through online optimization search, and generating and issuing a power supply recovery operation sequence; and deducing an adversarial simulation scene containing extreme working conditions and unknown disturbance, and feeding back a standardized knowledge case and the adversarial simulation scene to correct a risk probability field, thereby realizing iterative enhancement from passive replication to active generalization ability. According to the method, iterative enhancement from passive redisk to active generalization capability is realized.

The application discloses a kind of rehabilitation equipment failure data monitoring method and system, specifically related to electric digital data processing technical field, including S1, synchronous acquisition equipment operation time series data and patient interaction time series data, S2, two kinds of data are extracted and adaptively fused with features, S3, based on fusion features parallel execution equipment health prediction and rehabilitation curative effect deviation prediction, and the relevance of both is analyzed through cross attention mechanism, S4, combined with individualized dynamic baseline model, comprehensive equipment failure risk value, curative effect deviation and correlation confidence degree generate graded monitoring feedback.The application realizes the leap from simple failure early warning to treatment effectiveness guarantee by constructing equipment-patient-therapy ternary closed-loop analysis framework, solves the problem that the monitoring dimension of prior art is single and decoupled from clinical efficacy, and has the outstanding effect of strong early warning foresight, low false alarm rate and supporting root cause analysis.

The invention discloses a reliability evaluation method and device for a ship iterative mechanical product, a medium and a product, and relates to the field of reliability evaluation of ship mechanical products, and the method comprises the steps: obtaining a historical degradation and failure data set of a similar product of a target product; the historical degradation and failure data set is preprocessed; obtaining a degradation trend model of the similar product based on the preprocessed historical degradation and failure data set; acquiring supplementary test data of the target product; based on the supplementary test data, improving the degradation trend model to obtain a target product degradation trend model; and performing reliability evaluation on the target product based on the target product degradation trend model to obtain a multi-dimensional reliability evaluation index of the target product. According to the invention, the reliability evaluation efficiency, accuracy and real-time performance of the ship mechanical product can be improved.

The invention provides a power supply reliability analysis method and system based on real-time data, and the method comprises the steps: obtaining full-amount measurement real-time data, carrying out the preprocessing of the data, and generating standardized data; according to the standardized data, identifying a transformer area power failure event, and generating transformer area power failure data; generating power supply reliability operation data according to the transformer area power failure data and the corresponding lines; and calculating a power supply reliability index in combination with the power grid ledger and the power supply reliability operation data. Real-time statistics and analysis of power supply reliability indexes are achieved, power failure events can be accurately recognized, the power supply reliability level can be evaluated, data support is provided for operation management of a power system, and the power supply service quality is improved.

The present application relates to the technical field of industrial robot fault diagnosis, and particularly relates to a joint reducer fault data preprocessing method for industrial robots, which searches for reducer fault characteristic information modulated by rotating speed in the frequency domain distribution of current signals, extracts the reducer fault characteristic information based on the difference between the reducer fault characteristic frequency and the rotating speed frequency and the amplitude relationship before and after modulation, and obtains the time domain waveform of the reducer fault current signal without rotating speed modulation interference, which eliminates the interference of the rotating speed signal and provides data support and technical support for subsequent accurate extraction of reducer fault characteristics based on current signals.

The application provides a kind of embedded software requirement security verification method by means of failure data, which includes: establishing software requirement model, formalizing model description of software requirement, carrying out the patternization processing of failure data, explicitly failure data mode information, establishing the mapping relationship between software requirement model and failure data, carrying out the security verification of software requirement model, obtaining software requirement security verification result, the elements of the software requirement model include function input interface, function output interface and function processing process.The application is driven by failure data and security verification rules, and focuses on the key information that can easily cause engine control software to run failure, which can significantly improve work efficiency and quality, and ensure that potential failure modes and system hazards in software requirements are fully identified.

The invention discloses a probability model-driven smelting furnace life prediction method and system. The method comprises the following steps of collecting multi-source alarm and process data of smelting key components; feature engineering is carried out, degradation features are extracted, and discrete health states are divided; the current health state of the component is accurately recognized under the small sample condition by using the gray correlation degree theory; constructing a state transition probability matrix in combination with a Markov chain, and quantifying the randomness of the degradation process; and predicting the remaining service life of the component by solving an equation set taking failure as an absorption state. According to the method, the problem of life prediction caused by lack of historical failure data is effectively solved, dynamic, quantitative and interpretable evaluation of the residual life of key components such as an oxygen lance, a furnace lining and a flue is realized, and reliable technical support is provided for predictive maintenance decision-making of the smelting furnace.

The invention provides a method for establishing a continuous commutation failure fault data set of a direct-current transmission line, and particularly relates to a method for establishing a continuous commutation failure fault data set of a direct-current transmission line for machine learning. According to the method, calibration operation is carried out on the data of commutation failure based on the limit extinction angle criterion, outlier processing, normalization processing and feature engineering extraction operation are carried out on the data, and after t-SNE data dimension reduction operation is carried out, the separability of the data is obviously improved. And the method has certain significance for machine learning in direct current transmission line continuous commutation failure identification.

The present application relates to the technical field of marine diesel engine gas path control, and in particular to a marine diesel engine gas path control method based on digital twinning, which has the technical scheme: a multi-dimensional twinning body of the diesel engine is used to solve the problem that the traditional static model cannot adapt to the variable working conditions of the ship, a gas flow dynamic sub-model based on an LSTM neural network is fused with a cylinder combustion mechanism sub-model based on the law of thermodynamics to realize full working condition coverage, and a failure feature library based on historical failure data is used to realize a fault response closed loop; in view of the problem that the harsh environment of the ship can cause the failure of the sensor, a virtual-real interactive layer is used to drive the twinning body to perform multi-parameter coupling simulation, so as to improve the data reliability; based on the simulation results, a dynamic optimization module and a fault-tolerant control module are used to perform double optimization, so as to break through the limitation of the fixed weight coefficient and realize multi-objective dynamic optimization.