To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,...To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.展开更多
The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model fo...The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.展开更多
A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical...A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.展开更多
An improved covariance driven subspace identification method is presented to identify the weakly excited modes.In this method,the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability ...An improved covariance driven subspace identification method is presented to identify the weakly excited modes.In this method,the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics.The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix,in combination with component energy index(CEI)which indicates the vibration intensity of signal components,an alternative stabilization diagram is adopted to effectively separate spurious and physical modes.Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method.The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters.The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio(SNR)and gives a reliable separation of spurious and physical estimates.展开更多
We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identi...We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification.The generalized Poisson moment functional is focused.A total least squares equation based on this filtering approach is derived.Inspired by the idea of discrete-time subspace identification based on principal component analysis,we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables.Order determination and other instrumental variables are discussed.The usefulness of the proposed algorithms is illustrated through numerical simulation.展开更多
In this paper, a stochastic model of plague is first studied by subspace identification. First, the discrete model of plague is obtained based on the classical model. The corresponding stochastic model is proposed for...In this paper, a stochastic model of plague is first studied by subspace identification. First, the discrete model of plague is obtained based on the classical model. The corresponding stochastic model is proposed for the existence of stochastic disturbances. Second, for the model, the parameter matrices and noise intensity are obtained. Finally, the simulations of the model show that the subspace identification is more precise than least square method.展开更多
Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a sy...Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics.The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources.This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis functions.More importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data.The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin equation.This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.展开更多
Switched systems, i.e., systems changing the parameter values(even structural forms) abruptly and randomly at arbitrary instants, have been extensively utilized in many fields of modern industries. Rapid identificatio...Switched systems, i.e., systems changing the parameter values(even structural forms) abruptly and randomly at arbitrary instants, have been extensively utilized in many fields of modern industries. Rapid identification of switched systems, i.e.,capturing all the changing instants and reconstructing the mathematical models rapidly, is of great significance for behavior prediction, performance evaluation and possible control, but is restricted by small data amount available. Here, the rapid identification problem is successfully solved by a data-driven method in variational framework. The data-driven method only requires a small amount of data due to the compact form of the variational description, and is robust to data noise due to the holistic viewpoint. Two numerical examples, i.e., Duffing oscillator and van der Pol system(as two representative systems in nonlinear dynamics), are adopted to illustrate its application, efficiency and robustness to noise.展开更多
We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single ...We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant(LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search(ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.展开更多
Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters pr...Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.展开更多
In this paper, a data-driven stochastic subspace identification(SSI-DATA) technique is proposed as an advanced stochastic system identification(SSI) to extract the inter-area oscillation modes of a power system from w...In this paper, a data-driven stochastic subspace identification(SSI-DATA) technique is proposed as an advanced stochastic system identification(SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters(frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis.The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified,first using transient stability data generated from the IEEE16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system(WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.展开更多
当前基于车桥耦合系统的桥梁频率间接识别方法普遍对行驶车辆参数和速度有较大约束,难以应用于正常行驶的普通车辆。为了解决这一问题,该文提出了一种考虑车辆无量纲响应的桥梁频率间接识别方法。首先,以无量纲化的车-桥耦合运动学方程...当前基于车桥耦合系统的桥梁频率间接识别方法普遍对行驶车辆参数和速度有较大约束,难以应用于正常行驶的普通车辆。为了解决这一问题,该文提出了一种考虑车辆无量纲响应的桥梁频率间接识别方法。首先,以无量纲化的车-桥耦合运动学方程为基础,构建基于改进子空间识别法的系统状态方程与输出信号方程,建立了考虑时间差的车辆双轴无量纲响应差值信号方程,从理论上有效地消除了状态方程与输出信号中的路面平整度信息,突破了传统子空间识别法对车辆参数的限制,使该方法适用于任何普通车辆,同时验证了基于单次行驶双轴车辆响应的桥梁频率间接识别方法对简支梁桥频率识别的可行性。然后,通过数值计算探讨了车辆行驶速度、路面平整度等级和随机车辆荷载对桥梁频率间接识别的影响。计算结果表明,充分的荷载激励对桥梁频率的稳定识别非常重要,并且能够激发桥梁高阶模态的振动,更有利于桥梁高阶频率的识别。最后,针对一座实际服役的高墩简支梁桥开展现场行车试验,基于车桥动态接触力作为信号输入,验证子空间识别法获得桥梁频率的可行性和准确性。试验结果表明,短时随机子空间识别方法可以提取测试桥梁前2阶频率,在高阶频率的识别中有着更优于MOESP(multivariable output error state space,MOESP)子空间识别法的效果。展开更多
Traffic load identification for bridges is of great significance for overloaded vehicle control as well as the structural management and maintenance in bridge engineering.Unlike the conventional load identification me...Traffic load identification for bridges is of great significance for overloaded vehicle control as well as the structural management and maintenance in bridge engineering.Unlike the conventional load identification methods that always encounter problems of ill-condition and difficulties in identifying multi parameters simultaneously when solving the motion equations inversely,a novel strategy is proposed based on smart sensing combing intelligent algorithm for real-time traffic load monitoring.An array of lead zirconium titanate sensors is applied to capture the dynamic responses of a beam bridge,while the Long Short-Term Memory(LSTM)neural network is employed to establish the mapping relations between the dynamic responses of the bridge and the traffic load through data mining.The results reveal that,with the real-time strain responses fed into the LSTM network,the speed and magnitude of the moving load may be identified simultaneously with high accuracy when compared to the practically applied load.The current method may facilitate highly efficient identification of the time-varying characteristics of moving loads and may provide a useful tool for long-term traffic load monitoring and traffic control for in-service bridges.展开更多
基金supported by National Natural Science Foundation of China(22478239)Science and Technology Commission of Shanghai Municipality(19DZ2271100)National Natural Science Foundation of China(22208208)。
文摘To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.
基金funded by the National Natural Science Foundation of China (Nos. 11572069 and 51775541)the China Postdoctoral Science Foundation (No. 2016M601354)
文摘The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.
文摘A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.
基金This project is supported by National Natural Science Foundation of China(No.10302019).
文摘An improved covariance driven subspace identification method is presented to identify the weakly excited modes.In this method,the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics.The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix,in combination with component energy index(CEI)which indicates the vibration intensity of signal components,an alternative stabilization diagram is adopted to effectively separate spurious and physical modes.Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method.The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters.The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio(SNR)and gives a reliable separation of spurious and physical estimates.
基金supported by the National Natural Science Foundation of China (Nos.60674086 and 60736021)the Scientific and Technology Plan of Zhejiang Province,China (No.2007C21173)
文摘We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification.The generalized Poisson moment functional is focused.A total least squares equation based on this filtering approach is derived.Inspired by the idea of discrete-time subspace identification based on principal component analysis,we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables.Order determination and other instrumental variables are discussed.The usefulness of the proposed algorithms is illustrated through numerical simulation.
基金Acknowledgments This work was supported by the National Natural Science Foundation of China （NSFC） under Grant 61374137 and the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds （2013ZCX02-03）.
文摘In this paper, a stochastic model of plague is first studied by subspace identification. First, the discrete model of plague is obtained based on the classical model. The corresponding stochastic model is proposed for the existence of stochastic disturbances. Second, for the model, the parameter matrices and noise intensity are obtained. Finally, the simulations of the model show that the subspace identification is more precise than least square method.
基金supported by the National Key Research and Development Program of China(2018YFB1701202)the National Natural Science Foundation of China(92167201 and 51975237)the Fundamental Research Funds for the Central Universities,Huazhong University of Science and Technology(2021JYCXJJ028)。
文摘Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics.The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources.This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis functions.More importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data.The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin equation.This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11872328 and 11472240)supported by the National Natural Science Foundation of China (Grant Nos.11532011 and 11621062)。
文摘Switched systems, i.e., systems changing the parameter values(even structural forms) abruptly and randomly at arbitrary instants, have been extensively utilized in many fields of modern industries. Rapid identification of switched systems, i.e.,capturing all the changing instants and reconstructing the mathematical models rapidly, is of great significance for behavior prediction, performance evaluation and possible control, but is restricted by small data amount available. Here, the rapid identification problem is successfully solved by a data-driven method in variational framework. The data-driven method only requires a small amount of data due to the compact form of the variational description, and is robust to data noise due to the holistic viewpoint. Two numerical examples, i.e., Duffing oscillator and van der Pol system(as two representative systems in nonlinear dynamics), are adopted to illustrate its application, efficiency and robustness to noise.
文摘We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant(LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search(ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(No.52177085)Science and Technology Planning Project of Guangzhou(No.202102021208)。
文摘Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.
基金supported by the National Natural Science Foundation of China(No.51507028)the Hong Kong Polytechnic University under Project G-UA3Z
文摘In this paper, a data-driven stochastic subspace identification(SSI-DATA) technique is proposed as an advanced stochastic system identification(SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters(frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis.The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified,first using transient stability data generated from the IEEE16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system(WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.
文摘当前基于车桥耦合系统的桥梁频率间接识别方法普遍对行驶车辆参数和速度有较大约束,难以应用于正常行驶的普通车辆。为了解决这一问题,该文提出了一种考虑车辆无量纲响应的桥梁频率间接识别方法。首先,以无量纲化的车-桥耦合运动学方程为基础,构建基于改进子空间识别法的系统状态方程与输出信号方程,建立了考虑时间差的车辆双轴无量纲响应差值信号方程,从理论上有效地消除了状态方程与输出信号中的路面平整度信息,突破了传统子空间识别法对车辆参数的限制,使该方法适用于任何普通车辆,同时验证了基于单次行驶双轴车辆响应的桥梁频率间接识别方法对简支梁桥频率识别的可行性。然后,通过数值计算探讨了车辆行驶速度、路面平整度等级和随机车辆荷载对桥梁频率间接识别的影响。计算结果表明,充分的荷载激励对桥梁频率的稳定识别非常重要,并且能够激发桥梁高阶模态的振动,更有利于桥梁高阶频率的识别。最后,针对一座实际服役的高墩简支梁桥开展现场行车试验,基于车桥动态接触力作为信号输入,验证子空间识别法获得桥梁频率的可行性和准确性。试验结果表明,短时随机子空间识别方法可以提取测试桥梁前2阶频率,在高阶频率的识别中有着更优于MOESP(multivariable output error state space,MOESP)子空间识别法的效果。
基金National Key Research and Development Program of China(2020YFA0711700)National Natural Science Foundation of China(52122801,11925206 and 51978609)Foundation for Distinguished Young Scientists of Zhejiang Province(LR20E080003).
文摘Traffic load identification for bridges is of great significance for overloaded vehicle control as well as the structural management and maintenance in bridge engineering.Unlike the conventional load identification methods that always encounter problems of ill-condition and difficulties in identifying multi parameters simultaneously when solving the motion equations inversely,a novel strategy is proposed based on smart sensing combing intelligent algorithm for real-time traffic load monitoring.An array of lead zirconium titanate sensors is applied to capture the dynamic responses of a beam bridge,while the Long Short-Term Memory(LSTM)neural network is employed to establish the mapping relations between the dynamic responses of the bridge and the traffic load through data mining.The results reveal that,with the real-time strain responses fed into the LSTM network,the speed and magnitude of the moving load may be identified simultaneously with high accuracy when compared to the practically applied load.The current method may facilitate highly efficient identification of the time-varying characteristics of moving loads and may provide a useful tool for long-term traffic load monitoring and traffic control for in-service bridges.
