78 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 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 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 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.

The invention discloses a klystron residual life prediction system and a klystron residual life prediction method, belongs to the technical field of electron linear accelerators, and aims to solve the problems that no method for predicting the klystron residual life exists, maintenance and replacement are often carried out when the klystron cannot work suddenly, and stocking / replacement of the klystron needs a quite long time, so that the klystron is inconvenient to use. The system comprises a data acquisition unit, a data transmission unit, a data storage unit, a data operation unit and a data output unit. By collecting and analyzing the key operation data of the klystron, the residual life of the klystron can be predicted more accurately, and maintenance or replacement plan errors caused by inaccurate prediction are avoided; by making a maintenance or replacement plan in advance, the shutdown time of equipment caused by sudden failure of the klystron can be shortened, so that economic loss caused by shutdown is reduced, and meanwhile, reasonable spare part inventory management can also avoid extra cost caused by insufficient spare parts or untimely replacement.

An online intelligent prediction method for the remaining life of an aircraft structure based on acoustic emission is proposed. First, fatigue experiments are conducted on the aircraft structure to obtain raw data of multiple full-life acoustic emission waveform streams. Then, the raw data undergoes sliding window sampling feature extraction, and the parts with good monotonicity are selected to construct health indicators. A portion is randomly selected as the test set, and the remainder is used as the training set. The dataset is then segmented according to the 3σ principle to find the starting prediction point. Finally, in the testing phase, the test set data is input into the trained life prediction model to achieve online intelligent prediction of the remaining life of the aircraft structure. This invention utilizes a combination of DenseNet and contrastive learning to predict the remaining life of the aircraft structure online using intelligent algorithms, achieving good prediction results.

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.

Provided is a management system capable of determining the remaining life of equipment of an elevator with higher accuracy even when operation data indicating the operation state of the elevator cannot be directly obtained. In the management system (1), a reproduction unit (27) uses operation data acquired by a first server-side communication unit (25) to reproduce operation data according to an elevator operation model. The operation data indicates an operation state related to at least one of running of a car (6) of the elevator and opening and closing of a car door (9) of the car (6). The operation data includes time series data when the operation represented by the operation data is performed, and represents the operation state of the elevator. An evaluation unit (28) uses the reproduced operation data and evaluates a degradation factor that affects the degradation of the elevator equipment on the basis of a degradation factor model. A determination unit (29) determines the remaining life of the device from a life model of the device using the evaluated degradation factor.

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.

The invention discloses a method and system for predicting the residual life of a redundant safety instrument system, and the method comprises the steps: carrying out the life test of a to-be-predicted safety instrument system, and obtaining the residual life of the to-be-predicted safety instrument system according to the life data obtained through the test; constructing a mixed Weibull distribution model used for representing fault occurrence probability distribution characteristics of each device forming the safety instrument system to be predicted; performing maximum likelihood estimation on the mixed Weibull distribution model to obtain a maximum likelihood estimation value of a mixed Weibull distribution parameter; and constructing a fault distribution function of the to-be-predicted safety instrument system based on the fault equipment distribution characteristics corresponding to the failure of the to-be-predicted safety instrument system so as to obtain a failure probability density function, and based on the failure probability density function, obtaining the residual life of the current safety instrument system in combination with the maximum likelihood estimation value. The problem that the service life prediction of the redundant safety instrument system is limited to a single fault type and the application range is small is solved, and accurate prediction of the residual service life of the safety instrument system with multiple fault types at the same time is achieved.

This invention relates to the field of artillery barrel life prediction technology, specifically to a method for predicting the remaining life of an artillery barrel. The method includes: acquiring experimental data from multiple firing operations; acquiring the wear amount per shot for various propellant charges during a single firing; acquiring the current rifling wear at a fixed point; and acquiring the remaining firing count for each propellant charge. This method can accurately determine how many rounds the artillery can fire before the end of its barrel life, thereby preventing premature projectile detonation and barrel explosion accidents.

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 invention relates to the technical field of fault prediction, in particular to a method and system for predicting the remaining life of an aero-engine component, and the method comprises the following steps: calling a geometric model of the aero-engine component in a computer-aided design environment, and scanning and analyzing geometric discontinuous features of all turbine blades on the geometric model; according to the method, through scanning of a turbine blade geometric model and extraction of geometric discontinuous features, in combination with potential field gradient analysis, a potential high-risk area is pre-judged in a geometric space, fatigue vulnerable points can be recognized in advance, and a multi-dimensional random variable space is constructed around the high-risk area, so that the recognition accuracy of the fatigue vulnerable points is improved. According to the method, material attributes and geometric tolerance information are fused and then sampled, and samples are mapped back to a real geometric model to form a structure instance set, so that actual manufacturing and material fluctuation factors are effectively introduced, and the residual life of a part can be obtained by arranging life value frequency and cumulative frequency without depending on empirical estimation.

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 lithium battery residual life state evaluation method based on an improved TCN, obtains measured voltage and current data of a lithium battery under a cycle working condition, and obtains a cycle period and an actual capacity sequence after data slicing, ampere-hour integration and averaging treatment, so as to serve as input of an A-TCN-DAE model, battery capacity data Cap cur is predicted, then the subsequent capacity of the battery is recursively predicted until Cap cur ≤Cap EOL , the recursive prediction is ended, the number of residual cycle periods reaching an EOL capacity is counted to obtain the current residual life of the battery. The lithium battery residual life state evaluation method has strong local feature capturing capability and self-adaptive capability of input data noise reduction and reconstruction, and the precision of battery residual service life RUL estimation is obviously improved compared with existing deep learning network technology.

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.