52 results about "Remaining life" patented technology

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 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 application relates to a rotating machine residual life prediction method and system, and belongs to the field of mechanical life prediction. The method comprises the following steps: collecting signals capable of reflecting mechanical life of a rotating machine to be measured at least at two positions, extracting one-dimensional time characteristic data from the signals capable of reflecting mechanical life by using principal component analysis pooling, training a related vector machine by using the one-dimensional time characteristic data, so that the full life cycle of the rotating machine is divided into a period without obvious failure trend and a failure tendency period; training a deep separable convolution gate recurrent unit network by using data of the failure tendency period, and predicting the residual life of the rotating machine in the failure tendency period. The application can improve the prediction accuracy when the residual service life of the rotating machine is predicted.

This invention discloses a method for predicting the remaining life of brake friction pads, belonging to the field of brake life prediction. The method includes the following steps: S1, obtaining an initial effective feature set based on Pearson correlation coefficient and strong wear correlation features; S2, constructing a multi-physics coupled nonlinear wear degradation model for the friction pads and outputting multi-physics degradation parameters; S3, obtaining the optimal low-dimensional feature set through dual-source fusion; S4, optimizing the hyperparameters of the hybrid basis learner group using a dynamically perturbed improved gray wolf algorithm; S5, constructing a weighted fusion remaining life prediction model and completing accuracy evaluation and error correction; S6, online adaptive updating to achieve real-time remaining life prediction and life threshold warning. Using the above-mentioned method for predicting the remaining life of brake friction pads, high-precision prediction of the entire life cycle of brake friction pads through online dynamic adaptive updating is achieved, significantly improving prediction reliability and engineering application value.

A server (2) acquires first data related to a load applied to a vehicle body of an electrically powered mobile body, acquires second data related to operation of a battery (14) mounted on the mobile body, calculates a remaining life of the vehicle body based on the first data, calculates a remaining life of the battery based on the second data, compares the remaining life of the vehicle body with the remaining life of the battery (14), and in a case where the remaining life of the vehicle body is shorter than the remaining life of the battery (14), instructs at least one of activation of a function that is not used among a plurality of functions using electric power in the mobile body and performance improvement of a function that is being used.

The invention discloses an electrochemical device residual life prediction method based on a fusion model. The method comprises the following steps: S1, acquiring historical operation data of an electrochemical device, performing correlation analysis on the historical operation data, and screening out parameters strongly related to life attenuation as performance degradation indexes; s2, preprocessing the data of the performance degradation index; s3, predicting a degradation track of the preprocessed performance degradation index through a data-driven prediction model, and dynamically correcting the degradation track by fusing a physical experience correction model to obtain a degradation track correction fusion model as shown in a formula (1-1); and S4, determining the residual service life of the electrochemical device based on the corrected degradation track and a preset failure threshold value. According to the method, signal processing, machine learning optimization and an empirical model are fused, so that the residual service life of the electrochemical device is quickly and accurately predicted.

The invention relates to a method and device for predicting the residual life of a slurry pump, electronic equipment and a medium, and belongs to the technical field of fault diagnos.The method comprises the steps that original data of a target slurry pump are preprocessed, and the preprocessed original data are obtained; the original data comprises mechanical dynamic data, process performance data, equipment state data and historical maintenance data; fusing the preprocessed original data based on a Kalman filtering algorithm to obtain a global multi-modal fusion feature sequence; inputting the global multi-modal fusion feature sequence into a fully trained xLSTM model to obtain a fault classification result of the target slurry pump; and constructing a knowledge graph based on the fault classification result, and predicting the residual life of the target slurry pump based on the knowledge graph. The method improves the prediction accuracy of the residual life of the slurry pump.

The application discloses a kind of digital twin's main steam isolation valve residual life prediction method and system, belong to nuclear power key equipment state monitoring and life prediction technical field, method includes: real-time acquisition main steam isolation valve part's multidimensional operating state data, pre-processing;The data after pre-processing is input into the response rapid prediction model trained, obtains predicted vibration response and predicted stress time history;The deviation of predicted vibration response and sensor measured vibration response is calculated;If deviation exceeds set threshold, then the model is updated online;If not, then keep the model after training;Based on the predicted stress time history output by online update or the model after training, cyclic load statistics and fatigue damage calculation are carried out, and the residual life of main steam isolation valve is obtained.The application realizes the dynamic, online, high-precision prediction of the health status and fatigue life of main steam isolation valve by constructing digital twin, deeply fusing physical mechanism and real-time data.

The invention relates to the field of equipment state monitoring, and provides a method and device for determining the service life of an arc extinguish chamber, electronic equipment and a storage medium, and the method comprises the steps: detecting the opening operation of a generator circuit breaker, and obtaining a target operation parameter corresponding to the opening operation; the target operation parameters are input into a pre-trained abrasion loss determination model, target contact quality loss and target nozzle ablation depth corresponding to an arc extinguish chamber are obtained through processing of the abrasion loss determination model, and the arc extinguish chamber is a functional part in the generator circuit breaker; accumulating the target contact mass loss to obtain the total mass loss of the contact; accumulating the target nozzle ablation depths to obtain the total nozzle ablation depth amount; and determining the residual life of the arc extinguish chamber according to the total mass loss of the contact and the total ablation depth of the nozzle. According to the invention, accurate prediction of the residual life of the arc extinguish chamber is realized.

The invention discloses a method and system for predicting the residual life of a fuse and a storage medium, and relates to a method for evaluating the life of the fuse, and the system comprises a data collection module, an SVM model building module, a model algorithm efficiency analysis module and a decision tree algorithm optimization module. The data is mapped to a space with a higher dimension, and an optimal hyperplane is found to divide different categories, so that prediction is carried out; meanwhile, the SVM has good generalization ability and robustness, a good prediction effect can still be obtained in the face of a small number of training samples and noise, and beneficial effects are obtained from historical data of the fuse; when the speed of the SVM algorithm is slow or the prediction precision is low, the decision tree algorithm is used to optimize the SVM algorithm, and the speed and the prediction precision of the model are improved.

The present application relates to the method, device, computer equipment and storage medium for predicting the remaining life of SSD, the method comprises the following steps: finding the latest log file; presetting the value list of n, the PE value list, finding the corresponding log file; taking out the value n, finding the log file, reading the current user write amount; calculating the average write speed v; taking out the PE_b value, finding the corresponding log file, reading the current user write amount; calculating the time t required for user write amount, calculating the error t1 of t and actual time; judging whether PE_b is the last value of PE_list; calculating the error of all predicted time and actual time; judging whether n is the last value of n_list; finding the average write speed v1, calculating the predicted SSD remaining life value time_r according to the average write speed v1. The present application overcomes the periodicity rule characteristics of user behavior, and does not require user to give up privacy data.

The application discloses a kind of equipment component remaining life prediction method, device, equipment and storage medium, and is based on mixed fixed weight and improved GAT-BiMamba model to realize industrial equipment component remaining useful life and health state prediction.Adopting the technical scheme of the application, the degradation characteristics implied in the vibration signal of industrial equipment component during operation are efficiently and accurately extracted to realize the rapid and accurate prediction of the remaining useful life of industrial equipment component.

A remaining lifetime estimator comprises a relationship acquisition unit (102), an actual information acquisition unit (103), a relationship correction unit (104), a scope of use information acquisition unit (105), and a remaining lifetime estimator unit (106). The relationship acquisition unit receives a standard remaining lifetime determination relationship (40) from which the remaining lifetime is determined based on scope of use information of the electronic device. The actual information acquisition unit receives lifetime factor actual information relating to a lifetime factor that causes a change in the remaining lifetime. The relationship correction unit corrects the standard remaining lifetime determination relationship based on the lifetime factor actual information. The scope of use information acquisition unit receives scope of use information of the electronic device.The remaining service life estimation unit estimates the remaining service life based on the usage scope information and the corrected remaining service life determination relationship.

The application discloses a kind of equipment health state evaluation method and evaluation module, including the following steps: statistics and registration the remaining life of each part in the equipment, and constantly update each part remaining life as time goes by;Determine the first time of shutdown replacement parts, and the number of first shutdown replacement parts;Wherein, the number of first shutdown replacement parts and the health degree f1 of the equipment are positively correlated;The health degree f1 and the contribution degree X of the equipment's daily output, the contribution degree Y of quality score, the contribution degree Z of cost consumption are positively correlated.The equipment health state evaluation method of the application can optimize shutdown time, provide production efficiency, while plannedly do the storage and use of spare parts, which is beneficial to cost reduction and efficiency increase.

The invention provides a transfer learning prediction method and system for the residual life of a hydraulic system, and the method comprises the following steps: obtaining the measured hydraulic data of a field device and the full-life-cycle data of a test bench, and extracting the power spectrum density characteristics of the measured hydraulic data and the full-life-cycle data; and calculating a mapping conversion matrix by comparing spectrum features of a specific frequency window, aligning source domain features to a target domain, and generating a migration feature set. And in an evolution state space formed by fluid thermodynamic entropy increase and vibration energy level, constructing a standard degradation track by using the migration feature set. And mapping real-time data to the space, and deducing a time distance to a failure boundary in combination with a tropism weight of the real-time data to obtain a life prediction result. According to the method, the technical problem that a prediction model based on full-life data training is difficult to be directly applied to field equipment due to the difference between the laboratory working condition and the field actual working condition is solved.

This invention discloses a method and system for predicting the remaining life of equipment by integrating mechanistic information and degradation perception, relating to the field of big data technology. The method includes: extracting electrochemical and physical features from the raw operating data of the equipment under test to obtain multiple interpretable features; constructing an equipment remaining life prediction model based on a degradation stage perception soft-gating mechanism, an encoder with multi-resolution Mamba units, and a projection layer; training the equipment remaining life prediction model using machine learning algorithms based on preset historical equipment data to obtain a trained equipment remaining life prediction model; and using the multiple interpretable features as input, predicting the remaining life of the equipment under test using the trained equipment remaining life prediction model to obtain the prediction result. This invention alleviates the technical problem of the difficulty in accurately predicting the remaining life of equipment in existing technologies.

The invention discloses an equipment residual life prediction method and system driven by a dynamic working condition, and relates to the technical field of equipment operation and maintenance and life prediction, and the method comprises the steps: obtaining multi-source data, and carrying out the feature extraction; performing coupling mechanism quantification on the environment data after feature extraction, constructing a test scheme to fit a secondary response surface model, and calculating a coupling damage factor; constructing a PINN network containing mechanism constraints and coupling damage factors, establishing a trend evolution model in combination with a neural network, and obtaining a damage evolution trend; and calculating the residual life based on the damage evolution trend and the failure threshold. According to the method, through direct binding of working condition characteristics and test design, the coupling damage factors are calculated based on field actual measurement data and form cooperation with multi-dimensional characteristics, the interpretability of a physical mechanism and the data driving precision are considered, and accurate prediction of the equipment life is realized.

The invention relates to the technical field of industrial equipment life prediction, in particular to a residual life prediction method for automatic angle-adjusting welding tongs, and aims to solve the problems that in the prior art, a residual life prediction method for the automatic angle-adjusting welding tongs generally depends on single parameter monitoring, for example, only the current of a driving motor or the abrasion loss of a gear is monitored; the problems that the coupling loss effect between a mechanical system and an electrical system is not considered, the prediction error is large due to the cutting monitoring mode, and the actual loss process cannot be accurately reflected are solved. According to the method, coupling loss evolution is simulated through multi-physics field monitoring data and a digital twin model, residual life prediction is realized in combination with an LSTM model and a material fatigue theory, and the problem that a traditional method neglects a mechanical and electrical system coupling effect is solved; the method has the advantages of improving the prediction precision of the residual life, reducing the waste of maintenance resources and avoiding non-planned shutdown.

The disclosure provides a kind of decommissioned wind power equipment multi-component resource recycling method and device, method includes: according to the design life of each component in wind power equipment, real-time damage and failure damage, obtain the component remaining life of each component in wind power equipment;According to the component remaining life of each component in wind power equipment, obtain the unit remaining life of wind power equipment, and when the unit remaining life of wind power equipment is lower than preset value, wind power equipment is decommissioned;The processing and removal of each component of decommissioned wind power equipment;Quality detection is carried out to the removed component, and when the quality of component is not up to standard, cutting route planning and cutting parameter determination are carried out to the component, and the component is cut and decomposed according to the planned cutting route and determined cutting parameter;The sorted and recycled material is decomposed.In the decommissioned wind power equipment multi-component resource recycling method and device of the disclosure, the high-quality recycling of multi-component resources in decommissioned wind power equipment is realized.

The application discloses a kind of equipment component remaining life prediction method and system, and the remaining useful life and health state prediction of industrial equipment component are realized based on FM-Mamba-SE model.Adopting the technical scheme of the application, the degradation characteristics in the vibration signal during the operation of industrial equipment component are accurately extracted to realize the rapid and accurate prediction of the remaining useful life of industrial equipment component.

A kind of MCAT residual life prediction method and system for mechanical equipment, wherein residual life prediction method includes: constructing historical operation life cycle dataset, then the historical operation life cycle dataset is preprocessed, then training set is obtained by division;Residual life prediction model is constructed;Residual life prediction model is trained using training set, and loss function is constructed in each round of training, the parameters of residual life prediction model are adjusted according to loss function, to obtain trained residual life prediction model;Real-time receiving multidimensional sensor detects the monitoring data stream output by the measured mechanical equipment, is input into trained residual life prediction model after preprocessing, to obtain the residual life prediction value of the measured mechanical equipment at current time, then grading early warning is carried out according to residual life prediction value.The present application is used to predict the residual life of mechanical equipment, and the prediction accuracy and robustness are high.

This invention relates to the field of battery life prediction technology, specifically providing a method, apparatus, and medium for predicting the remaining life of a battery, aiming to solve the problem of inaccurate prediction of the remaining battery life. To this end, the method includes acquiring the battery's full life-cycle operating parameters and dividing these parameters into training and test sets; selecting highly correlated features from the training set to establish a training set health model; and calculating the actual health HI of the test set based on its features. Test And based on the health model of the training set, predict the predicted health HI of the test set. Pred The two models are compared to select the K closest battery health models; the final lifetime TL of these K closest battery health models is obtained, and the predicted lifetime TL of the test set batteries is calculated. Pred Predicted remaining lifetime RUL = TL Pred -TL Curr , among which, TL Curr This method aims to accurately predict the remaining lifespan of a test cell.

This invention provides a method, system, and storage medium for predicting the remaining life of retired power batteries. The method includes the following steps: acquiring cell-level data from the battery management system, operational data from the energy storage converter, and environmental data; performing feature extraction and standardization on the cell-level data, operational data, and environmental data to generate a full-dimensional feature matrix; calculating the initial remaining life of the battery using a transfer learning model; dynamically correcting the initial remaining life by calling a preset life correction factor library; and outputting the final remaining life assessment result. The method for predicting the remaining life of retired power batteries provided by this invention, through multi-source data fusion, achieves life prediction across battery types and usage scenarios, can respond promptly to changes in battery operating conditions, and dynamically corrects the prediction results, thereby significantly improving the accuracy and reliability of battery remaining life prediction.

The invention relates to the technical field of aero-engine hot end component materials and application, and discloses a method for predicting the lasting residual life of an isometric crystal casting nickel-based superalloy, and the method comprises the steps: obtaining first microstructure data and interruption time of an alloy sample through a lasting interruption experiment, and constructing a service condition prediction model based on a machine learning algorithm; second microstructure data and pre-damage time of the alloy sample are obtained through a pre-damage experiment, and a residual life prediction model is constructed based on a machine learning algorithm; and representing the microstructure parameters of the to-be-evaluated alloy, inputting the microstructure parameters into the service condition prediction model to obtain a service condition prediction value, and inputting the microstructure parameters and the service condition prediction value into the residual service life prediction model to output the residual service life of the to-be-evaluated alloy. The method can be used for carrying out macroscopic and microscopic service damage evaluation on the isometric crystal casting nickel-based superalloy for the centripetal impeller of the aero-engine, and can also be used for predicting the residual life of the alloy.

The invention discloses an unmanned vehicle tire replacement method and device and an unmanned vehicle, and belongs to the field of unmanned driving. The tire replacement method comprises the steps that after the remaining life of each tire on the unmanned vehicle is obtained, the life difference degree of the unmanned vehicle is determined based on the remaining life, and when the life difference degree is larger than the corresponding preset life difference degree, a tire replacement decision is triggered. According to the scheme, whether the tire is replaced or not is judged according to the service life difference degree, the problem that tire replacement and the like completely depend on subjective judgment in a traditional tire management technology is solved, and when the service life difference degree is larger than the corresponding preset service life difference degree, the tire replacement time is shortened, and the tire replacement efficiency is improved. The tire replacement decision is triggered at the moment, so that the small wear loss difference between the tires can be ensured, and the actual service life of the tires is prolonged.

The invention discloses a solid state disk remaining life prediction method, a memory and a computer program product, and relates to the technical field of storage, the solid state disk remaining life prediction method comprises the following steps: obtaining a first multi-dimensional health index associated with the service life of a solid state disk, the first multi-dimensional health index comprising a plurality of health indexes of different dimensions; performing index fusion processing on the first multi-dimensional health index by using a pre-trained convolutional auto-encoder network to obtain a first fused health index; determining a fault threshold corresponding to the first fusion health index by using a preset degradation model; and utilizing a pre-trained gating attention unit network to predict the residual life of the solid state disk according to the first fusion health index and the fault threshold. The embodiment of the invention can improve the accuracy of predicting the residual life of the solid state disk.

This application relates to the field of predicting the remaining life of alkaline electrolyzers, and discloses a method, system, and medium for predicting the remaining life of alkaline electrolyzers. The method includes: S1, acquiring target domain data and source domain data; S2, constructing a pre-trained model, and using the source domain data S... Y S3. Train the pre-trained model; S4. Construct the target model using the target domain data training set S. MX Train the target model; extract the normalized target domain data training set S. MX The external and internal feature representations are used for adversarial training with the external feature representation and the external features extracted in S2; S4, the trained target model is used to predict remaining lifetime. This application extracts global features to avoid the negative transfer effect caused by local feature differences and improves the accuracy of the model in predicting remaining lifetime in a data-scarce environment.

A remaining life estimation method, a remaining life estimation device, and a remaining life estimation program acquire a map generated based on a backscatter diffraction pattern by backscattered electrons from a material when the material is irradiated with an electron beam. From the map, a range of a predetermined number of crystal grains up to a propagation plane of a crack occurred in the material is extracted as a specific range. An index value based on crystal orientation of the material in the specific range is generated. A load condition is specified based on the index value in the specific range. A remaining life of the material is estimated based on the surface crack length of the crack by referring to a relationship between surface crack length and life ratio under the load condition.

This invention discloses a method and system for predicting the remaining life of rubber seals, relating to the field of remaining life prediction technology. The method involves acquiring the current pressure decay curve and current test temperature, and extracting the current pressure decay time constant; acquiring historical operating datasets, extracting life decay datasets, constructing a decay-compression deformation mapping model, and determining the compression deformation failure threshold; based on the life decay dataset, compensating for the current pressure decay time constant, and extracting the current pressure decay time constant ratio; inputting this ratio into the decay-compression deformation mapping model to generate the current compression deformation value. This invention uses the current compression deformation value and the compression deformation failure threshold, combined with a preset operating temperature, to perform prediction processing and generate the remaining life of the seal, thereby effectively avoiding discrepancies between the prediction results and the actual field conditions, and improving the reliability of life prediction.