For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtaine...For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.展开更多
Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the...Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.展开更多
The purpose of this article is to develop and analyze least-squares approximations for the incompressible magnetohydrodynamic equations. The major advantage of the least-squares finite element method is that it is not...The purpose of this article is to develop and analyze least-squares approximations for the incompressible magnetohydrodynamic equations. The major advantage of the least-squares finite element method is that it is not subjected to the so-called Ladyzhenskaya-Babuska-Brezzi （LBB） condition. The authors employ least-squares functionals which involve a discrete inner product which is related to the inner product in H^-1（Ω）.展开更多
In this paper, we establish the maximum norm estimates of the solutions of the finite volume element method （FVE） based on the P1 conforming element for the non-selfadjoint and indefinite elliptic problems.
In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can n...In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can not solve an identification problem with infinitely many solutions well.Then we propose PRCs identification based on the minimal norm method,which satisfies observability conditions and has advantages of high computing efficiency and short time consumption.The two identification methods are applied in a water network,and their identification results are compared under the same conditions.From the results,we know that PRCs identification based on the minimal norm method has advantages of higher computing efficiency,shorter time consumption and higher precision.展开更多
针对水下阵列波达方位(direction of arrival,DOA)估计在少快拍情况下对相邻声源分辨能力差的问题,提出了基于迭代原子范数最小化的均匀圆环阵DOA快速估计方法。所提方法利用模态域处理方法对阵列流形进行预处理,将均匀圆环阵转换为虚...针对水下阵列波达方位(direction of arrival,DOA)估计在少快拍情况下对相邻声源分辨能力差的问题,提出了基于迭代原子范数最小化的均匀圆环阵DOA快速估计方法。所提方法利用模态域处理方法对阵列流形进行预处理,将均匀圆环阵转换为虚拟直线阵,然后通过对角重构估计无噪接收信号协方差矩阵,消除模态域处理引入的非均匀噪声的影响。为了充分利用接收信号稀疏性,同时避免字典网格搜索带来的误差,在模态域引入迭代原子范数最小化稀疏恢复方法,提出均匀圆环阵迭代原子范数最小化(uniform circular array-iterative atomic norm minimization,UCA-IANM)方位估计方法。原子范数最小化稀疏恢复问题一般采用内点法求解,该方法随接收信号快拍数增加,计算量急剧上升,不适用于水下计算资源受限的场景。在交替方向乘子法(alternating direction multiplier method,ADMM)的基础上,针对正则化参数难以选择的问题,提出了基于参数优化ADMM的UCA-IANM(UCA-IANM assisted by ADMM with parameter optimization,UCA-IANM-APO)DOA快速估计算法。仿真实验与实测数据分析表明,UCA-IANM-APO DOA快速估计方法的角度分辨能力和估计精度均优于传统DOA估计方法,求解速度较内点法提升了两个数量级。展开更多
基金supported by Aeronautical Science Foundation of China (No. 20100251006)Technological Foundation Project of China (No. J132012C001)
文摘For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.
基金Supported by National Natural Science Foundation of China(Grant No.51607180)
文摘Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.
基金supported by the National Basic Research Program of China (2005CB321701)NSF of mathematics research special fund of Hebei Province(08M005)
文摘The purpose of this article is to develop and analyze least-squares approximations for the incompressible magnetohydrodynamic equations. The major advantage of the least-squares finite element method is that it is not subjected to the so-called Ladyzhenskaya-Babuska-Brezzi （LBB） condition. The authors employ least-squares functionals which involve a discrete inner product which is related to the inner product in H^-1（Ω）.
基金The Major State Basic Research Program (19871051) of China and the NNSP (19972039) of China.
文摘In this paper, we establish the maximum norm estimates of the solutions of the finite volume element method （FVE） based on the P1 conforming element for the non-selfadjoint and indefinite elliptic problems.
基金Sponsored by the National"Eleventh-five"Tackle Key Problem Program-China Science and Technology Support Project(Grant No.2006BAJ01A04)
文摘In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can not solve an identification problem with infinitely many solutions well.Then we propose PRCs identification based on the minimal norm method,which satisfies observability conditions and has advantages of high computing efficiency and short time consumption.The two identification methods are applied in a water network,and their identification results are compared under the same conditions.From the results,we know that PRCs identification based on the minimal norm method has advantages of higher computing efficiency,shorter time consumption and higher precision.
文摘针对水下阵列波达方位(direction of arrival,DOA)估计在少快拍情况下对相邻声源分辨能力差的问题,提出了基于迭代原子范数最小化的均匀圆环阵DOA快速估计方法。所提方法利用模态域处理方法对阵列流形进行预处理,将均匀圆环阵转换为虚拟直线阵,然后通过对角重构估计无噪接收信号协方差矩阵,消除模态域处理引入的非均匀噪声的影响。为了充分利用接收信号稀疏性,同时避免字典网格搜索带来的误差,在模态域引入迭代原子范数最小化稀疏恢复方法,提出均匀圆环阵迭代原子范数最小化(uniform circular array-iterative atomic norm minimization,UCA-IANM)方位估计方法。原子范数最小化稀疏恢复问题一般采用内点法求解,该方法随接收信号快拍数增加,计算量急剧上升,不适用于水下计算资源受限的场景。在交替方向乘子法(alternating direction multiplier method,ADMM)的基础上,针对正则化参数难以选择的问题,提出了基于参数优化ADMM的UCA-IANM(UCA-IANM assisted by ADMM with parameter optimization,UCA-IANM-APO)DOA快速估计算法。仿真实验与实测数据分析表明,UCA-IANM-APO DOA快速估计方法的角度分辨能力和估计精度均优于传统DOA估计方法,求解速度较内点法提升了两个数量级。
