134 results about "Remaining life" patented technology

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 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 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 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 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.

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.

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.

The invention provides a consumable service life processing method and device, equipment and a storage medium, and belongs to the technical field of data processing and big data. The method comprises the following steps: in response to execution of a consumable service life determination process, obtaining model information, consumable work order information, accumulated water consumption, water outlet flow and a feedback score of a target water purifier; determining expected life according to the model information and the consumable work order information; according to the consumable work order information, the accumulated water consumption, the water outlet flow and the feedback score, the service life is determined; if the expected life is greater than the used life, calculating a difference value between the expected life and the used life; according to the difference value and the consumable work order information, residual life prompt information is generated; outputting residual life prompt information; if the expected service life is smaller than or equal to the used service life, consumable replacement prompt information is output. According to the method, the problem that the accuracy of a judgment mode for judging whether the consumables need to be replaced or not is low is solved.

To provide a creep life prediction method and a creep life prediction device that can simply and accurately predict the remaining life of a target steel material regardless of the properties of the target steel material.SOLUTION: A creep life prediction method includes: a first nondestructive damage degree identifying step of using a nondestructive evaluation method to identify, as a first nondestructive creep damage degree, a creep damage degree of a target steel material subjected to remaining life prediction at a first time; a first rupture time identifying step for identifying, as a first Larson-Miller rupture time, a creep rupture time of the target steel material at the first time by applying, to a regression equation prescribed by the Larson-Miller method, a first stress imparted to the target steel material until the first time and a first temperature of the target steel material until the first time; and a degree-of-divergence identifying step for identifying a degree of divergence which is a correction value for the first Larson-Miller rupture time, on the basis of the first Larson-Miller rupture time and a first nondestructive rupture time which is a creep rupture time at the first time calculated from the first nondestructive creep damage degree.SELECTED DRAWING: Figure 4

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.

The present invention provides an adaptive prediction method and device for the remaining life of bearings of coal mine machinery and equipment, belonging to the technical field of bearing remaining life prediction; solves the problem of how to more accurately predict the development rate of bearing damage and the remaining life of the bearing after a bearing fault is detected; comprises the following steps: collecting the vibration signal and rotation speed of the bearing, sequentially calculating the bearing vibration characteristic-energy level and energy level; smoothing the bearing vibration characteristic-energy level and energy level; sequentially calculating the actual damage degree of the bearing based on the smoothed vibration characteristic-energy level and energy level; updating the model parameters of the bearing physical model based on the deviation between the actual damage degree of the bearing and the bearing damage degree predicted by the bearing physical model; and providing a prediction of the bearing damage degree and the remaining life of the bearing based on the updated model parameters of the bearing physical model; the present invention is applied to the prediction of the remaining life of the bearing.

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 present invention relates to an excavator. To solve the problem of unbalanced operation of the two oil pumps in an excavator with a dual-pump configuration, the present invention constructs a hydraulic oil supply control system, method, control device, and excavator. The control system includes a first oil pump, a first main valve, a third main valve, a second oil pump, and a second main valve whose oil inlet is connected to the second oil pump. The first main valve and the third main valve oil inlet are both connected to the first oil pump. The control system also includes: an oil supply distribution valve connected between the oil inlet of the third main valve and the second oil pump for controlling the on-off of the oil supply line between the third main valve and the second oil pump; a detection device for detecting the life parameters of the oil pumps; a controller for calculating the remaining life value of the corresponding oil pumps based on the life parameters; and controlling the displacement of each oil pump and the on-off of the oil supply distribution valve based on the remaining life value of the oil pumps when the third main valve is switched. The present invention realizes oil supply coordination between the two pumps, achieves balanced operation of the two pumps, and prevents premature excessive wear and failure of one pump.

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 discloses a fan residual life alarm method and device and a substrate management controller, and relates to the field of field, and the method comprises the steps: obtaining operation data of a fan in different operation stages in the operation process; predicting the residual life of the fan according to the operation data of the fan in different operation stages and the total number of the operation stages; matching an alarm strategy according to the residual life; and executing corresponding alarm measures according to the alarm strategy. And predicting the residual life of the fan according to the operation data of the fan. And then, an alarm strategy is matched based on the residual life of the fan, so that the maintenance work can be converted into preventive maintenance from traditional reactive maintenance. Downtime caused by equipment faults is reduced, and the reliability and availability of the equipment are improved. When the fan is close to the end point of the service life, an alarm is given out in time, potential risks caused by equipment faults can be avoided, and the safety of operators and equipment is guaranteed.

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 remaining life of a component of a mechanical system is predicted with high accuracy. A failure probability evaluation system evaluates a failure of a component of each mechanical system for a mechanical system group including a plurality of mechanical systems, and includes a failure history database for accumulating past failure history data about the mechanical system. A time-series operation database stores time-series physical quantity data representing an operating state of the mechanical system. A failure rate function identification unit calculates a failure rate function of a mechanical system by statistical processing based on the failure history data, and a damage model generation / update unit having a function of generating an explanatory variable expression of a failure rate function which minimizes a variation in life is defined by the failure rate function using the time-series physical quantity data.

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.

A method for predicting the remaining life of an IGBT module, which collects sensing data when the IGBT module performs power cycling under different working conditions, calculates transient thermal resistance data, and extracts and screens effective features therefrom; then uses source working condition data with remaining life labels and target working condition data whose remaining life is to be predicted as a training set to train a prediction model based on a probabilistic sparse self-attention mechanism; finally, in the online stage, the trained model is used to accurately predict the remaining life of the working condition. The present invention extracts and screens effective features from the transient thermal resistance data obtained from the power cycling of the IGBT module, uses a probabilistic sparse self-attention structure to further extract effective information, and measures the difference in deep features under different working conditions through the maximum mean discrepancy, so as to achieve cross-working condition prediction.

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.