Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts ...Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.展开更多
A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is impro...A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is improved by a method based on the Paris formula and the Foreman formula allowing the establishment of different degradation stages.The remaining useful life of rolling element bearings can be predicted by the adjusted model with inputs of physical data and operating status information.The late operating trend is predicted by the use of the particle filter algorithm.The rolling bearing full life experimental data validate the proposed method.Further,the prediction result is compared with the single SSM and the Gamma model,and the results indicate that the predicted accuracy of the proposed method is higher with better practicability.展开更多
A process parameter optimization method for mold wear during die forging process is proposed and a mold life prediction method based on polynomial fitting is presented,by combining the variance analysis method in the ...A process parameter optimization method for mold wear during die forging process is proposed and a mold life prediction method based on polynomial fitting is presented,by combining the variance analysis method in the orthogonal test with the finite element simulation test in the forging process.The process parameters with the greatest influence on the mold wear during the die forging process and the optimal solution of the process parameters to minimize the wear depth of the mold are derived.The hot die forging process is taken as an example,and a mold wear correction model for hot forging processes is derived based on the Archard wear model.Finite element simulation analysis of die wear process in hot die forging based on deform software is performed to study the relationship between the wear depth of the mold working surface and the die forging process parameters during hot forging process.The optimized process parameters suitable for hot forging are derived by orthogonal experimental design and analysis of variance.The average wear amount of the mold during the die forging process is derived by calculating the wear depth of a plurality of key nodes on the mold surface.Mold life for the entire production process is predicted based on average mold wear depth and polynomial fitting.展开更多
In order to predict the storage life of a certain type of HTPB(hydroyl-terminated polybutadiene)coating at 25℃ and analyze the influence of pre-strain on the storage life,the accelerated aging tests of HTPB coating a...In order to predict the storage life of a certain type of HTPB(hydroyl-terminated polybutadiene)coating at 25℃ and analyze the influence of pre-strain on the storage life,the accelerated aging tests of HTPB coating at 40℃,50℃,60℃,70℃ with the pre-strain of 0%,3%,6%,9%,respectively were carried out.The variation regularity of the change of crosslinking density was analyzed and the aging model of HTPB coating under pre-strained thermally-accelerated aging was proposed.The storage life of HTPB coating at 25℃ was estimated by using the Berthelot equation as the end point of the aging life with a 30% decrease in maximum elongation.The results showed that the change of crosslinking density of HTPB coating increased with the increase of aging temperature and aging time,and decreased with the increase of pre-strain.Under 0% prestrain,the relationship between the change of crosslinking density of HTPB coating and the aging time can be described by the logarithmic model with the confidence probability greater than 99%.The stress relaxation phenomenon existed under 3%,6%and 9%pre-strained aging.The aging model considering chemical aging and pre-strain was established with the confidence probability greater than 90%.The storage life of HTPB coating was 15.2935 years at 25C under 0% prestrain,which was reduced by 13.9007%,75.6949% and 89.7859% under 3%,6% and 9% pre-strain,respectively.The existence of pre-strain has a serious impact on the storage life of HTPB coating,therefore,the pre-strain should be avoided as much as possible during the actual storage.展开更多
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power...Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.展开更多
Vibration fatigue is one of the main failure modes of blade.The vibration fatigue life of blade is scattered caused by manufacture error,material property dispersion and external excitation randomness.A new vibration ...Vibration fatigue is one of the main failure modes of blade.The vibration fatigue life of blade is scattered caused by manufacture error,material property dispersion and external excitation randomness.A new vibration fatigue probabilistic life prediction model(VFPLPM)and a prediction method are proposed in this paper.Firstly,as one-dimensional volumetric method(ODVM)only considers the principle calculation direction,a three-dimensional space vector volumetric method(TSVVM)is proposed to improve fatigue life prediction accuracy for actual threedimensional engineering structure.Secondly,based on the two volumetric methods(ODVM and TSVVM),the material C-P-S-N fatigue curve model(CFCM)and the maximum entropy quantile function model(MEQFM),VFPLPM is established to predict the vibration fatigue probabilistic life of blade.The VFPLPM is combined with maximum stress method(MSM),ODVM and TSVVM to estimate vibration fatigue probabilistic life of blade simulator by finite element simulation,and is verified by vibration fatigue test.The results show that all of the three methods can predict the vibration fatigue probabilistic life of blade simulator well.VFPLPM &TSVVM method has the highest computational accuracy for considering stress gradient effect not only in the principle calculation direction but also in other space vector directions.展开更多
A method and procedure of high cycle fatigue life prediction for helicopter transmission system tail gearbox casing is presented, including fatigue test load, three parameters S-N curve, reduction factor and cumulativ...A method and procedure of high cycle fatigue life prediction for helicopter transmission system tail gearbox casing is presented, including fatigue test load, three parameters S-N curve, reduction factor and cumulative damage law. According to the fatigue test results, the design load spectrum and the three parameters S-N curve, a fatigue life prediction of the tail gearbox casing of a helicopter is performed as an example.展开更多
A fatigue damage model is developed for evaluating accumulative fatigue damage of dumpers. The loading spectrums acted on dumpers are created according to measured strain data in field. The finite element analysis is ...A fatigue damage model is developed for evaluating accumulative fatigue damage of dumpers. The loading spectrums acted on dumpers are created according to measured strain data in field. The finite element analysis is carried out for assessing stress distribution and strength characteristics of dumpers. Fatigue damage indexes and service life are calculated by a modified Palmgren-Miner rule. The investigation shows that fatigue notch factor has a significant influence on the calculation of fatigue damage of dumpers.展开更多
How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anti...How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory. Different from ＇traditional techniques （or regimes）＇ proposed in the previous literature, the present scheme guarantees that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.展开更多
A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain...A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human motion.The residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep network.Experiments on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.展开更多
Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagn...Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.展开更多
Fresh extruded rice-shaped kernels(FER) are remoulded rice products from cereals or seed flours, which have the advantages of safety, nutrition, health and time saving. However, the finished products are easy to react...Fresh extruded rice-shaped kernels(FER) are remoulded rice products from cereals or seed flours, which have the advantages of safety, nutrition, health and time saving. However, the finished products are easy to react with oxygen, so it is necessary to develop a fast, simple and reliable approach to monitor and predict the shelf-life of FER. A comprehensive mathematical model of FER shelf-life prediction was developed using a dynamic modelling approach based on real supply chain conditions. This predictive model was developed to determine four key indexes including acid value, iodine blue value, water uptake ratio and peroxide value. The results showed that when the peroxide value was 1.6849, the FER lost its edible value, nutritional value and commodity value. Moreover, the acid value and peroxide value of FER were used to establish a first-order kinetic model, and the iodine blue value of FER was suited for a zero-order kinetic model. The validation experiment of predicted and measured shelf life showed that the relative error was 3.12%, which was less than 5%. Therefore, this kinetic model could be used to predict the shelf-life of FER quickly and conveniently. The kinetic-based shelf-life prediction model proposed in this study is rapid and practical, providing theoretical basis and guidance for the establishment of quality monitoring and quality evaluation systems of FER during the production, storage, transport and marketing.展开更多
基金support from "973 Project" (Contract No. 2010CB226706)
文摘Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.
文摘A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is improved by a method based on the Paris formula and the Foreman formula allowing the establishment of different degradation stages.The remaining useful life of rolling element bearings can be predicted by the adjusted model with inputs of physical data and operating status information.The late operating trend is predicted by the use of the particle filter algorithm.The rolling bearing full life experimental data validate the proposed method.Further,the prediction result is compared with the single SSM and the Gamma model,and the results indicate that the predicted accuracy of the proposed method is higher with better practicability.
基金This work was supported in part by the National Natural Science Foundation of China(No.51575008).
文摘A process parameter optimization method for mold wear during die forging process is proposed and a mold life prediction method based on polynomial fitting is presented,by combining the variance analysis method in the orthogonal test with the finite element simulation test in the forging process.The process parameters with the greatest influence on the mold wear during the die forging process and the optimal solution of the process parameters to minimize the wear depth of the mold are derived.The hot die forging process is taken as an example,and a mold wear correction model for hot forging processes is derived based on the Archard wear model.Finite element simulation analysis of die wear process in hot die forging based on deform software is performed to study the relationship between the wear depth of the mold working surface and the die forging process parameters during hot forging process.The optimized process parameters suitable for hot forging are derived by orthogonal experimental design and analysis of variance.The average wear amount of the mold during the die forging process is derived by calculating the wear depth of a plurality of key nodes on the mold surface.Mold life for the entire production process is predicted based on average mold wear depth and polynomial fitting.
基金This work was supported by the National Defense Pre-Research Projects[grant number ZS2015070132A12002].
文摘In order to predict the storage life of a certain type of HTPB(hydroyl-terminated polybutadiene)coating at 25℃ and analyze the influence of pre-strain on the storage life,the accelerated aging tests of HTPB coating at 40℃,50℃,60℃,70℃ with the pre-strain of 0%,3%,6%,9%,respectively were carried out.The variation regularity of the change of crosslinking density was analyzed and the aging model of HTPB coating under pre-strained thermally-accelerated aging was proposed.The storage life of HTPB coating at 25℃ was estimated by using the Berthelot equation as the end point of the aging life with a 30% decrease in maximum elongation.The results showed that the change of crosslinking density of HTPB coating increased with the increase of aging temperature and aging time,and decreased with the increase of pre-strain.Under 0% prestrain,the relationship between the change of crosslinking density of HTPB coating and the aging time can be described by the logarithmic model with the confidence probability greater than 99%.The stress relaxation phenomenon existed under 3%,6%and 9%pre-strained aging.The aging model considering chemical aging and pre-strain was established with the confidence probability greater than 90%.The storage life of HTPB coating was 15.2935 years at 25C under 0% prestrain,which was reduced by 13.9007%,75.6949% and 89.7859% under 3%,6% and 9% pre-strain,respectively.The existence of pre-strain has a serious impact on the storage life of HTPB coating,therefore,the pre-strain should be avoided as much as possible during the actual storage.
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
基金Shivaji University,Kolhapur for financial assistance through Research Strengthening Scheme。
文摘Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.
基金supported by the Aviation Science Foundation of China(No.20150252003)
文摘Vibration fatigue is one of the main failure modes of blade.The vibration fatigue life of blade is scattered caused by manufacture error,material property dispersion and external excitation randomness.A new vibration fatigue probabilistic life prediction model(VFPLPM)and a prediction method are proposed in this paper.Firstly,as one-dimensional volumetric method(ODVM)only considers the principle calculation direction,a three-dimensional space vector volumetric method(TSVVM)is proposed to improve fatigue life prediction accuracy for actual threedimensional engineering structure.Secondly,based on the two volumetric methods(ODVM and TSVVM),the material C-P-S-N fatigue curve model(CFCM)and the maximum entropy quantile function model(MEQFM),VFPLPM is established to predict the vibration fatigue probabilistic life of blade.The VFPLPM is combined with maximum stress method(MSM),ODVM and TSVVM to estimate vibration fatigue probabilistic life of blade simulator by finite element simulation,and is verified by vibration fatigue test.The results show that all of the three methods can predict the vibration fatigue probabilistic life of blade simulator well.VFPLPM &TSVVM method has the highest computational accuracy for considering stress gradient effect not only in the principle calculation direction but also in other space vector directions.
文摘A method and procedure of high cycle fatigue life prediction for helicopter transmission system tail gearbox casing is presented, including fatigue test load, three parameters S-N curve, reduction factor and cumulative damage law. According to the fatigue test results, the design load spectrum and the three parameters S-N curve, a fatigue life prediction of the tail gearbox casing of a helicopter is performed as an example.
文摘A fatigue damage model is developed for evaluating accumulative fatigue damage of dumpers. The loading spectrums acted on dumpers are created according to measured strain data in field. The finite element analysis is carried out for assessing stress distribution and strength characteristics of dumpers. Fatigue damage indexes and service life are calculated by a modified Palmgren-Miner rule. The investigation shows that fatigue notch factor has a significant influence on the calculation of fatigue damage of dumpers.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 10472091 and 10502042） and the Scientific and Technological Innovation Foundation for Young Teachers of Northwestern Polytechnical University, China.
文摘How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory. Different from ＇traditional techniques （or regimes）＇ proposed in the previous literature, the present scheme guarantees that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.
文摘A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human motion.The residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep network.Experiments on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.
基金supported in part by the National Natural Science Foundation of China (52107229, 62203423, and 61903114)in part by the Fujian Provincial Natural Science Foundation (2022J01504)。
文摘Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.
基金a grant from the National Key Research and Development Program of China(2017YFD0401305)the Key Research and Development Program of Shandong Province (2021CXGC010809)
文摘Fresh extruded rice-shaped kernels(FER) are remoulded rice products from cereals or seed flours, which have the advantages of safety, nutrition, health and time saving. However, the finished products are easy to react with oxygen, so it is necessary to develop a fast, simple and reliable approach to monitor and predict the shelf-life of FER. A comprehensive mathematical model of FER shelf-life prediction was developed using a dynamic modelling approach based on real supply chain conditions. This predictive model was developed to determine four key indexes including acid value, iodine blue value, water uptake ratio and peroxide value. The results showed that when the peroxide value was 1.6849, the FER lost its edible value, nutritional value and commodity value. Moreover, the acid value and peroxide value of FER were used to establish a first-order kinetic model, and the iodine blue value of FER was suited for a zero-order kinetic model. The validation experiment of predicted and measured shelf life showed that the relative error was 3.12%, which was less than 5%. Therefore, this kinetic model could be used to predict the shelf-life of FER quickly and conveniently. The kinetic-based shelf-life prediction model proposed in this study is rapid and practical, providing theoretical basis and guidance for the establishment of quality monitoring and quality evaluation systems of FER during the production, storage, transport and marketing.
