Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated ...Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.展开更多
The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propaga...The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neurons of the second hidden layer. The paper presents the fundamental theory associated with this approach as well as a set of experimental results that evaluate the performance and accuracy of the proposed method against other methods found in the literature.展开更多
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to...We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.展开更多
The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning method.The filtered data of the direct numerical simulation is used to establis...The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning method.The filtered data of the direct numerical simulation is used to establish thetraining data set,the validation data set,and the test data set.The artificial neural network(ANN)methodand Back Propagation method are employed to train parameters in the ANN.The developed ANN is applied toconstruct the sub-grid scale model for the large eddy simulation of the Burgers turbulence in the one-dimensionalspace.The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.展开更多
We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforceme...We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforcement and width online.The laser vision sensor is mounted after the welding torch and used to profile the weld.With the extracted weld geometry and the adopted process parameters,a back propagation neural network(BPNN)is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings.A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs.This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.展开更多
The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a...The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.展开更多
Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic...Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.展开更多
A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently,...A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.展开更多
Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer tra...Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.展开更多
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. C...In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. Corresponding convergence results are proved.展开更多
基金the National Natural Science Foundation of China(No.51875209)the Guangdong Basic and Applied Basic Research Foundation(No.2019B1515120060)the Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment。
文摘Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.
文摘The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neurons of the second hidden layer. The paper presents the fundamental theory associated with this approach as well as a set of experimental results that evaluate the performance and accuracy of the proposed method against other methods found in the literature.
基金financially supported by the Russian federal research project No.FWZZ-2022-0026“Innovative aspects of electro-dynamics in problems of exploration and oilfield geophysics”.
文摘We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.
基金supported by the National Key R&D Program of China(Grant No.2022YFB3303500).
文摘The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning method.The filtered data of the direct numerical simulation is used to establish thetraining data set,the validation data set,and the test data set.The artificial neural network(ANN)methodand Back Propagation method are employed to train parameters in the ANN.The developed ANN is applied toconstruct the sub-grid scale model for the large eddy simulation of the Burgers turbulence in the one-dimensionalspace.The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.
基金the National Natural Science Foundation of China(No.51665037)the Open Fund of the Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province(No.20171BCD40003)the Open Fund of the Key Tahoratory of Nondestructive Testing Ministry of Education,Nanchang Hangkong University of China.(No.EW201980090)。
文摘We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforcement and width online.The laser vision sensor is mounted after the welding torch and used to profile the weld.With the extracted weld geometry and the adopted process parameters,a back propagation neural network(BPNN)is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings.A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs.This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.
基金supported by State Key Development Program of Basic Research of China (Grant No.2010CB429001)
文摘The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.
文摘Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.
文摘A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.
基金supported by the National Natural Science Foundation of China (61603398)。
文摘Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.
基金National Natural Science Foundation of China (10471017)Zhejiang Provincial Natural Science Foundation (Y606009)
文摘In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights. Corresponding convergence results are proved.
