ID 原文 译文
17855 针对复杂工况下滚动轴承故障信号盲提取问题，该文提出一种独立分量分析(ICA)中非线性函数自适应选择方法，解决了等变化自适应源分离算法(EASI)在多类振动源共存的情况下无法分离轴承故障信号的问题。 For the problem of blind extraction of rolling bearing fault signals under complex working conditions, an adaptive selection method of non-linear functions in Independent Component Analysis (ICA) is proposed, which solves the problem that Equivariant Adaptive Separation via Independence(EASI) can not separate bearing fault signals when multiple vibration sources coexist.
17856 此外，为了解决在线盲分离算法稳态误差与收敛速率的平衡问题，提出基于模糊逻辑的自适应迭代步长选择方法，极大地提高了学习算法的收敛速度，且稳态误差更小。 In addition, in order to balance the steady-stateerror and convergence rate of the online blind separation algorithm, an adaptive iterative step selection methodbased on fuzzy logic is proposed, which improves greatly the convergence speed of the learning algorithm andreduces the steady-state error.
17857 轴承故障数据的盲提取仿真结果验证了算法的性能。 The simulation results of blind extraction of bearing fault data verify theperformance of the proposed algorithm.
17858 针对传统LS-ESPRIT算法在估计GTD模型参数时抗噪效果差，估计精度不高这一问题，该文提出了一种改进的LS-ESPRT算法，有效地提高了算法的参数估计性能与抗噪性。 The traditional Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques(LS-ESPRIT) algorithm is not effective while estimating parameters of the Geometric Theory of Diffraction(GTD) at lower SNR. To solve this problem, an improved LS-ESPRIT algorithm is proposed in this paper.
17859 首先，根据雷达目标的回波数据构建Hankel矩阵； Firstly, a Hankel matrix is constructed by the echo data of radar targets.
17860 其次，采用核范数凸优化方法对上述Hankel矩阵进行降噪处理，得到低秩的重构Hankel矩阵； Secondly,a low- rank reconstructedHankel matrix is obtained,which is solved by the nuclear norm convex optimization method.
17861 最后，利用传统的LS-ESPRIT算法对降噪后的数据进行处理，估计出GTD模型参数。 Finally, thetraditional LS-ESPRIT algorithm is used to process the data after noise reduction and estimate the parametersof the GTD model.
17862 基于改进算法与传统算法分别得到重构RCS，并针对不同带宽对参数估计精度的影响作以仿真探究。 Moreover,the reconstructed Radar Cross Section (RCS) can be obtained by the traditionalLS-ESPRIT algorithm and the improved LS-ESPRIT algorithm. The influence of different bandwidths onparameter estimation is also analyzed in this paper.
17863 仿真结果表明，与传统LS-ESPRIT算法与传统TLS-ESPRIT算法相比，改进LS-ESPRIT算法的参数估计性能更高，抗噪性更强，且重构RCS的幅值与相角误差更小。 Simulation results show that the estimation accuracy andnoise resistance of the improved LS-ESPRIT algorithm is better than the traditional LS-ESPRIT algorithm andthe traditional TLS-ESPRIT algorithm. Furthermore, the amplitude error and phase angle error of the RCSwhich is reconstructed by the improved algorithm are smaller than the traditional algorithm.
17864 对不同带宽下的参数估计精度也进行了探究，并得出：带宽越大，估计精度越高。 Different bandwidths also have influences on parameter estimation accuracy, the more wider bandwidth is, the more accurate parameters can be estimated.