238 results about "Remaining life" patented technology

Disclosed is a method for evaluating the fatigue damage and life of a bridge structure under a multi-factor coupling effect. The method comprises the following steps: S1, reproducing complex service environment action values; S2, analyzing performance values of a bridge structure; S3, calculating coupling fatigue damage of a vulnerable part; and S4, predicting a remaining life of the bridge structure. By adopting the method, the fatigue damage condition and remaining life of an in-service bridge under a multi-factor coupling effect can be accurately evaluated, and the effect of each factor / action and the coupling effect the factors / actions in fatigue damage of a vulnerable part are quantitatively analyzed, thereby providing data support and decision-making reference for the operation and maintenance management, reinforcement and repair, and optimal design of bridges.

A method for predicting the remaining life and health status of a lithium battery based on LSTM+Mamba includes the following contents: charging and discharging the lithium battery, collecting various lithium battery aging data during the cycle, and preprocessing the data; constructing a multi-layer neural network, which is composed of an LSTM layer, a multi-head attention mechanism, and a Mamba module; constructing a combined weighted loss function with optimized weights to train the multi-layer neural network; and using the multi-layer neural network to predict the remaining life and health status of the lithium battery. In the face of the high nonlinearity of the lithium battery degradation process, the present invention significantly improves the prediction accuracy of the remaining life and health status.

The invention discloses an intelligent prediction method and device for the residual life of equipment, and relates to the field of reliability engineering, and the method comprises the steps: collecting sensor observation data, carrying out the preprocessing of the sensor observation data, and extracting data fluctuation features; based on a time window input strategy, processing the sensor observation data and the data fluctuation characteristics, and determining input characteristics; training a global-local feature extraction network according to the input features, and determining a trained global-local feature extraction network; the global-local feature extraction network comprises a multi-layer time convolution network, a causal convolution gating unit, a two-dimensional compression excitation fusion module and a Transform encoder which are connected in sequence; performing performance verification on the trained global-local feature extraction network, and determining a life prediction model; and predicting the residual life of the to-be-tested equipment according to the life prediction model. According to the invention, the life prediction performance can be comprehensively improved.

The invention discloses a buried pipeline residual life prediction method, relates to the technical field of pipeline prediction in computer measurement, can realize multi-step prediction of the residual life of a buried pipeline, and is small in accumulated error and higher in precision. According to the buried pipeline residual life prediction method, a training set is input into a TFT neural network for network training, a test set is input into the trained TFT neural network for testing, TFT model parameters are stored, and training of the neural network is completed; the TFT neural network comprises a gating residual network GRN module used for processing a nonlinear correlation relation between input data and a prediction target, a variable selection network module used for carrying out important feature screening on static and dynamic variables, and a multi-head attention layer module used for learning a long-term dependency relation of a pipeline life sequence. And a long and short term memory network (LSTM) module, and a quantile regression module for outputting the residual life of the pipeline based on regression of different quantiles.

The system includes a wireless network that combines the functionality of the sensors with the formation and maintenance of the network, while balancing the network with the minimization of the energy consumed by the entire system. The system may include nodes, gateways, coordinators, extension sensors and boosters. The system may perform a method comprising applying a known load to a battery in a first node; measuring a voltage drop in the battery; determining an internal resistance of the battery; estimating a remaining life in the battery based on the internal resistance; and in response to the remaining life being below a remaining life threshold, re-directing, by a first gateway, signals in a mesh network away from the first node and to one or more of a plurality of nodes interfacing with the first node, wherein the first gateway interfaces with the first node and a network.

A steel wire rope remaining life prediction method of a four-rope grab crane comprises the steps that 1, an electromagnetic device is used for magnetization in the axial direction of a steel wire rope; 2, a Hall sensor is installed at the fixed end of the steel wire rope; 3, a Hall sensor monitors changes of a magnetic field around the steel wire rope in real time; 4, whether missing values and abnormal values exist in the collected digital signal sequence or not is checked; if missing values exist, a linear interpolation method is adopted for filling; 5, extracting characteristics related to the damage and residual life of the steel wire rope from the cleaned digital signal data; 6, establishing a magnetization prediction model; 7, magnetic field change characteristic data of the steel wire rope in different use stages are collected, a least square method is adopted for the magnetization prediction model to estimate model parameters, and a loss function is minimized; and 8, predicting the real-time residual life of the steel wire rope. The method can predict the service life of the steel wire rope at high frequency and accurately predict the residual service life of the steel wire rope.

The invention provides a low-voltage current transformer residual life prediction method and device, equipment and a medium, and relates to the technical field of current transformers, and the method comprises the steps: obtaining a multi-stress operation information parameter and a performance index parameter of a to-be-predicted low-voltage current transformer; calculating performance degradation index parameters according to the performance index parameters; training to obtain a preset residual life prediction model based on the multi-stress operation information parameter historical sample value, the performance index parameter historical sample value and the residual life historical sample value, and predicting the residual life according to the multi-stress operation information parameter, the performance degradation index parameter and the preset residual life prediction model. The residual life prediction value of the to-be-predicted low-voltage current transformer is obtained, multiple stress factors are comprehensively considered, the health condition of the transformer is comprehensively evaluated, the accuracy and reliability of residual life prediction of the low-voltage current transformer are improved, production shutdown and safety risks caused by equipment faults are effectively reduced, and the production efficiency is improved. And continuity and stability of production are guaranteed.

The invention discloses a method and a system for predicting the residual life of a downhole casing in a full life cycle. The method comprises the following steps: acquiring the working pressure, the internal pressure resistance safety coefficient and the extrusion resistance safety coefficient of a to-be-tested casing; based on the working pressure of the to-be-tested casing, the internal pressure resistance safety coefficient and a pre-constructed first model, obtaining the internal pressure resistance safety service time of the to-be-tested casing; obtaining the anti-extrusion safety service time of the to-be-tested casing pipe based on the working pressure and the anti-extrusion safety coefficient of the to-be-tested casing pipe and a pre-constructed second model; and obtaining the residual life of the to-be-tested casing according to the internal pressure-resistant safe service time and the extrusion-resistant safe service time. By means of the method, life prediction of the shale gas downhole tubular column in the whole life cycle can be achieved, sleeve life prediction is more accurate, and the method has more practical production guiding significance.

The invention relates to a residual life evaluation system based on a direction control valve. The residual life evaluation system comprises an acquisition module for acquiring change condition data of the direction control valve through a sensor; the data processing module is used for performing data processing based on the change condition data of the direction control valve acquired by the data acquisition module so as to extract characteristic parameters of the direction control valve; the service life evaluation module is used for predicting the residual service life of the direction control valve based on a pre-established safety threshold value and a relation model; and when the performance parameters of the direction control valve exceed the safety threshold value, the system automatically gives out early warning. Through the sensor installed at the key position of the direction control valve, the system can monitor parameters such as pressure, flow and valve element displacement in real time. Therefore, an operator can know the operation state of the control valve at any time and find potential abnormal conditions in time.

A cutter residual life prediction method based on adaptive stage division comprises the following steps: firstly, establishing a state space model for cutter residual life prediction so as to comprehensively describe a cutter wear process; secondly, a basis is provided for follow-up analysis by obtaining abrasion data of a current tool and historical tools; then establishing a self-adaptive stage division model, and subdividing the wear stage of the cutter; and finally, accurately obtaining a stage switching point of the current cutter by utilizing the self-adaptive stage division model, and finally predicting the residual life of the cutter through a particle filtering algorithm. The method can adjust the prediction model in real time, adapts to the individual difference of cutter degradation, provides a more accurate residual life prediction result, and has a wide industrial application prospect.

The invention discloses an operation and maintenance method, device and equipment for filtering and separating equipment and a readable storage medium, belongs to the field of gas filtering, and aims to consider that related sensing data of a filter cartridge can reflect the residual life of the filter cartridge, so that a filter cartridge life prediction model is pre-trained, and then in the operation process of the filtering and separating equipment, the residual life of the filter cartridge is predicted. A real-time sensing data set (including at least one of temperature and humidity data of the filter cartridge, a pressure difference value of the surface of the filter cartridge, particle concentration of an inlet and an outlet of the filter cartridge and flow of the outlet of the filter cartridge) is obtained through a preset sensor group, and then the real-time sensing data set is input into a pre-trained filter cartridge service life prediction model to obtain a residual service life prediction value of the filter cartridge. And the prompter is controlled to prompt the residual life prediction value, so that the condition that the filter cartridge is poor in state and short in service life can be found in time and replaced in time, the filtering and separating equipment is prevented from running under the condition that the filter cartridge is poor in state, the filtering effect is guaranteed, and damage to rear-end equipment is avoided.

The invention belongs to the technical field of lithium battery health management, and particularly relates to a lithium battery residual life prediction method based on self-adaptive modal number estimation and multi-scale decomposition, which comprises the following steps of: firstly, acquiring a capacity attenuation sequence of a battery, and then calculating the residual life of the battery through a variational modal decomposition method combining a self-adaptive modal number determination mechanism and Bayesian modal screening. Carrying out adaptive decomposition and denoising on the sequence, and separating a low-frequency trend mode and a medium-high frequency oscillation mode; according to the long-range dependence of the trend mode, a Transform model is adopted for prediction; and for the nonlinear fluctuation characteristics of the oscillation mode, a random forest model is adopted for prediction. And finally, fusing the prediction results of the two groups of components, reconstructing a complete future capacity attenuation curve, and calculating the remaining service life based on a preset failure threshold. According to the method, through adaptive decomposition and grouping modeling, the adaptability, noise immunity and prediction precision of the prediction model to different battery individuals and complex working conditions are effectively improved.

The invention discloses a service life prediction system and an air conditioning system, and the system comprises a data collection module which is used for collecting the operation data of a to-be-predicted device; the data analysis module is used for calculating the life loss of the to-be-predicted device according to the operation data; and the life prediction module is used for estimating the residual life of the to-be-predicted device according to the life loss. Compared with the prior art, the service life of the to-be-predicted device can be accurately predicted according to the environment parameters and the operation parameters of the to-be-predicted device, so that the to-be-predicted device is maintained and replaced in advance, the service life of the whole system is effectively prolonged, and fault risks and resource waste are reduced.

The invention provides a lithium battery residual life detection method and apparatus, an electronic device and a storage medium. The method comprises the steps of performing correlation analysis on a voltage battery capacity curve of each charging interval and a battery health state to obtain a correlation coefficient; when the absolute value of the correlation coefficient is greater than a preset value, constructing a battery voltage characteristic sample set through the battery voltage and the battery capacity of the charging interval, and taking impedance spectrum data corresponding to each charging interval in the battery voltage capacity characteristic sample set as an impedance spectrum characteristic sample set; inputting the battery voltage characteristic sample set into a deep learning model to obtain a predicted impedance spectrum, and training the deep learning model based on the predicted impedance spectrum and the impedance spectrum characteristic sample set; and inputting the charging data of a to-be-predicted lithium battery into the trained deep learning model to obtain a target predicted impedance spectrum, and determining the predicted residual life of the lithium battery based on the equivalent circuit model and the target predicted impedance spectrum. The detection efficiency is improved by detecting the battery voltage characteristics of the lithium battery.

The invention discloses a method for evaluating the residual life of an electrical component under the influence of stress fluctuation, and the method comprises the steps: firstly, obtaining the initial state information of the electrical component, including characteristic parameters and initial values thereof; then, a static artificial accelerated aging corrosion test is carried out, and the change value of the characteristic parameters along with time is obtained; based on the change values, the basic weight of each characteristic parameter is determined. And establishing a function relation between the characteristic parameters and time, and describing the aging process. An Arrhenius model is used to calculate an acceleration factor, and the model is adjusted to adapt to different temperature conditions. And obtaining a characteristic parameter degradation model according to the acceleration factor and the function relation. And when the electrical component is only influenced by the environmental stress fluctuation, further establishing a degradation model considering the influence. Finally, according to the operation state of the electrical component, the residual life is evaluated by using a corresponding model, and a scientific basis is provided for maintenance and management. According to the method, various factors are comprehensively considered, the evaluation accuracy is improved, and the system reliability and safety are ensured.

The invention provides a multi-component resource recovery method and device for retired wind power equipment. The method comprises the steps that the component remaining life of each component in the wind power equipment is obtained according to the design life, the real-time damage amount and the failure damage amount of each component in the wind power equipment; the unit residual life of the wind power equipment is obtained according to the assembly residual life of each component in the wind power equipment, and when the unit residual life of the wind power equipment is lower than a preset value, the wind power equipment is decommissioned; all parts of the retired wind power equipment are treated and dismantled; quality detection is conducted on the disassembled part, when the quality of the part does not reach the standard, cutting route planning and cutting parameter determination are conducted on the part, and cutting decomposition is conducted on the part according to the planned cutting route and the determined cutting parameters; and the decomposed materials are sorted and recycled. According to the multi-component resource recycling method and device for the retired wind power equipment, high-quality recycling of multi-component resources in the retired wind power equipment is achieved.

The invention provides a wheel life prediction and operation and maintenance plan decision method and device and a storage medium. The method comprises the following steps: acquiring data related to the remaining life of a wheel; generating a wheel residual life based on the wheel life related data and a prediction model obtained by pre-training; obtaining wheel turning repair related data, and generating a wheel turning repair plan based on the wheel turning repair related data; updating the remaining service life of the wheel based on the wheel turning repair plan; and generating an operation, maintenance and / or purchase plan corresponding to the updated wheel remaining life based on the updated wheel remaining life. According to the technical scheme, the remaining service life of the wheel can be estimated only by using a small amount of data, the data complexity is reduced, the prediction accuracy is improved, the turning wheel operation plan is flexibly generated, the potential influence of the turning wheel on the remaining service life is accurately reflected, the timeline of the maintenance task is automatically generated or optimized, and the maintenance efficiency is improved. The accurate matching of the maintenance activity and the actual demand is ensured, and the non-planned downtime is reduced.

The invention belongs to the technical field of electromechanical equipment management and maintenance, and particularly relates to an electromechanical equipment service life prediction method. The method mainly comprises the following steps: determining a life expectancy association attribute sequence according to the type of electromechanical equipment; determining to-be-analyzed data and initial reference data; screening samples from the samples to establish a sample database, selecting a proper data period, intercepting corresponding periodic monitoring data by adopting a sliding time window, performing similarity analysis to determine similarity, extracting effective data, calculating periodic change rate deviation, and optimizing the samples to establish an effective reference database; establishing a prediction weight and prediction based on the time sequence similarity of the sample data; the method for predicting the life of the electromechanical equipment is mainly used for providing a method which is suitable for novel equipment, can better consider the relevance between historical measurement data and the life cycle, displays the relation between the running states of the electromechanical equipment in different life stages and the final expected life, and better analyzes and predicts the expected residual life of the electromechanical equipment.

The invention belongs to the technical field of lightning arrester monitoring, and particularly relates to a zinc oxide lightning arrester intelligent monitoring system based on an electric power internet of things, which comprises a zinc oxide lightning arrester operation state acquisition module, a zinc oxide lightning arrester defect degree acquisition module and a zinc oxide lightning arrester service life evaluation module. The zinc oxide arrester operation state acquisition module is used for judging whether the zinc oxide arrester has potential risks or not in a monitoring period; the zinc oxide arrester defect acquisition module judges the defect degree of the zinc oxide arrester based on the zinc oxide arrester defect data; and the zinc oxide arrester service life evaluation module is used for obtaining zinc oxide arrester residual service life matching data when judging that the defect degree of the zinc oxide arrester is serious, and obtaining the zinc oxide arrester residual service life based on the zinc oxide arrester residual service life matching data. According to the invention, the operation state, the lightning stroke influence and the defect degree of the lightning arrester can be accurately judged, and early warning of damage and explosion accidents of the lightning arrester is realized.

The present application relates to the technical field of nuclear power plant performance testing and discloses a method for assessing the remaining life of a bursting disk in a large-caliber vacuum system of a nuclear power plant, comprising the following steps: S1, obtaining a first sample of a reference bursting disk after a first fatigue test, obtaining a second sample of a bursting disk to be assessed, and performing a bursting test on the first and second samples to obtain a life coefficient; S2, performing a non-metallic material state test on non-metallic debris in the first and second samples after the test, obtaining non-metallic test results of similar non-metallic materials, and obtaining a first remaining life of the bursting disk to be assessed; S3, performing a metallic material state test on metallic debris in the first and second samples after the test to obtain a second remaining life of the bursting disk to be assessed; S4, obtaining a third sample of the bursting disk to be assessed, and performing a second fatigue test to obtain a third remaining life of the bursting disk to be assessed; and S5, obtaining a final remaining life of the bursting disk to be assessed based on the life coefficient, the first, second, and third remaining lives.

The invention discloses an aero-engine residual life prediction method based on a symmetric regular inferior differential equation, and the method comprises the steps: collecting monitoring parameters of a full life cycle of an aero-engine through a sensor; establishing a first-order Shentu differential equation, carrying out continuous time sequence modeling on the degradation process in a hidden variable space, and calculating a residual signal as a time-varying signal; establishing a Fourier neural operator to approximate a transfer function of a physical system, and mapping the working condition parameters and the hidden variables to sensor response parameters so as to construct a loss function of a neural network; time scale transformation between different degradation processes is considered, a symmetric regular term is constructed, the invariance of the Sheng differential equation on the time scale transformation is restrained, and a hidden variable process with a consistent structure is obtained; and inputting the training sample set and the test sample set into the neural network to obtain hidden variable processes corresponding to the training sample set and the test sample set, and estimating the residual life of the test sample set according to nearest neighbor samples on the training sample set.

The present invention suitably predicts a remaining life of a bearing. Provided is a bearing remaining life diagnosis method that predicts the remaining life of a rolling bearing which is for use in a rotary machine and which has inner and outer bearing rings and a rolling body between the bearing rings, said method comprising: a step for calculating the most recent estimated flaking length; and a step for calculating the most recent remaining life of the bearing on the basis of the most recent flaking length of a bearing ring. The step for calculating the most recent estimated flaking length includes a step for calculating the maximum rolling body load, a step for calculating the maximum contact surface pressure from the maximum rolling body load, and a step for calculating the most recent estimated flaking length of the bearing ring from the maximum contact surface pressure of the bearing ring.

The invention relates to the technical field of hydraulic engineering safety monitoring, in particular to a small reservoir residual life prediction method based on a health degree evaluation model, comprising the following steps: constructing a small reservoir health evaluation index system; calculating the health degree of the small reservoir sub-project; calculating the comprehensive health degree of the small reservoir; establishing a nonlinear Wiener process model; estimating model parameter values; predicting the residual life of the small reservoir; the beneficial effects are that according to the small reservoir residual life prediction method based on the health degree evaluation model provided by the invention, the comprehensive health degree value representing the health state of the small reservoir is calculated according to the health degree calculation result of each sub-project of the small reservoir; a degradation model based on a small reservoir health degree index is constructed by using a nonlinear Wiener process, a small reservoir residual life probability density function is deduced, and a small reservoir residual life value can be obtained more accurately.

The invention discloses a method for predicting the residual life of a reinforced concrete bridge based on monitoring data, and belongs to the technical field of bridge life prediction. The method comprises the steps of obtaining a residual life prediction result of a target bridge before reinforcement, and obtaining a reinforcement scheme and a predicted life prolonging effect adopted when the target bridge is reinforced; determining a plurality of to-be-monitored key parameter types through the reinforcement scheme, and generating a corresponding monitoring scheme according to the plurality of key parameter types for deployment of monitoring equipment to complete acquisition of key parameters; and acquiring a key parameter standard value corresponding to the predicted life prolonging effect during numerical simulation, performing numerical comparison on the key parameter standard value and a plurality of acquired key parameters, correcting the predicted life prolonging effect according to a comparison result, and prolonging a residual life prediction result according to a correction result to obtain the residual life of the reinforced concrete bridge. The method can reduce the dependence on the finite element model during prediction of the residual life of the reinforced concrete bridge.

The invention relates to the technical field of steel formwork detection, in particular to a method and device for predicting the remaining life of a steel formwork. The method comprises the following steps: acquiring image data of a steel template, dividing the image data of the steel template to obtain sub-image data of a plurality of steel templates, acquiring steel template mechanical data corresponding to the sub-image data of each steel template, performing feature extraction on the sub-image data of the plurality of steel templates to obtain a plurality of surface feature data, and calculating the surface feature data of the plurality of steel templates according to the surface feature data. Performing feature extraction on the mechanical data to obtain multiple pieces of mechanical feature data; carrying out feature fusion on each piece of surface feature data and the corresponding mechanical feature data to obtain a plurality of sub-fusion features, carrying out feature fusion on each sub-fusion feature according to an application influence factor to obtain a fusion feature matrix, and inputting the fusion feature matrix into a preset steel template residual life prediction model to obtain a prediction result of the residual life of the steel template. And obtaining the residual life of the steel template. By means of the configuration mode, the residual life of the steel formwork can be accurately predicted.

To provide a technique for determining a remaining life of an epoxy mold electric power device through simple operations without causing a power outage.SOLUTION: A method comprises the steps of: (a) detecting an electrical signal related to partial discharge generated from an epoxy mold electric power device to obtain a φ-q distribution; (b) determining whether the shape of the φ-q distribution is linear or planar; (c) determining, when the shape of the φ-q distribution is determined to be planar, whether a virtual figure formed by a closed curve smoothly connecting outer edges of an area showing the planar shape has a first shape in which there are substantially no notches, or a second shape in which there are notches in the virtual figure; (d) calculating an estimation value of an electrical tree length on the basis of a variance value Var related to a discharge charge q when the area showing the planar shape is the first shape; (e) calculating an estimation value of a propagation speed of an electrical tree; and (f) calculating an estimation value of a remaining life of the epoxy mold electric power device on the basis of the estimation value of the propagation speed of the electrical tree.SELECTED DRAWING: Figure 1

A method of estimating a remaining life span of a driver for driving a load, wherein said driver comprises a power stage arranged for receiving an input voltage and for providing an output to said load, wherein said method comprises the steps of receiving, by a state observer model, a temperature value related to said driver, and receiving a loss value related to a power loss of said driver, determining, by said state observer model, estimated internal states representing component temperature dynamics based on said received temperature value and said received loss value making use of an observer model for mimicking a thermal behavior of said driver, providing, by said state observer model, estimated temperatures of a plurality of electrical components in said driver based on said estimated internal states representing component temperature dynamics using a component matrix, and estimating, by a life span model, said remaining life span of said driver based on said estimated temperatures.

The invention discloses a residual life prediction method and system for an engineering mechanical structural member. The method comprises the steps that S1, a multi-dimensional original data set of the mechanical structural member is collected through a multi-source sensor; s2, performing time-frequency domain feature extraction based on multi-dimensional original data set matching in combination with the inherent frequency and the fault feature frequency, and obtaining a high-dimensional degradation feature set through wavelet packet analysis in combination with characterization damage evolution; s3, utilizing a pre-trained physical memory neural network fusion model CP-NMN to carry out adaptive dimension reduction optimization on the high-dimensional feature set, and obtaining a cross-working-condition same-dimensional health index; and S4, based on the same-dimensional health indexes, dynamically predicting the residual life by adopting a long-short-term memory network, and presetting a life threshold to realize engineering early warning, so that effective monitoring of the health state of the structural member and life prediction can be realized.

The present invention discloses a method and device for predicting the remaining life of a thin-film evaporator used in the production of Lyocell fibers, which relates to the technical field of mechanical fault diagnosis, and includes: collecting vibration data by vibration sensors in a preset area on the surface of the thin-film evaporator cylinder and performing preprocessing, and calculating the characteristic frequencies of key components; vectorially adding the vibration data of individual vibration sensors to construct a one-dimensional convolutional neural network model to obtain a characteristic curve; fitting with a plurality of high-order exponential functions to establish a remaining life characteristic curve; obtaining a data set to establish a one-dimensional convolutional neural network model for predicting the remaining life, inputting the data set into the one-dimensional convolutional neural network model to obtain a remaining life prediction model; and then using the vibration data collected in real time and the characteristic frequencies of key components after processing as the input of the prediction model. The present invention can reduce the complexity of the feature extraction network model, improve the real-time operation of the model, provide more accurate data labels, and effectively improve the accuracy of remaining life prediction.

A method of predicting a remaining life of a filter can include transmitting light toward the filter, taking a measurement of light reflected from the filter, adding the measurement to a database, predicting the remaining life of the filter using the database, displaying the remaining life of the filter, or any combination thereof. The light can include infrared light.