大跨径桥梁实时动态挠度信号的分离

由于经验模式分解(empirical mode decomposition，简称EMD)将非线性非平稳信号分解成为一系列线性、平稳的本征模函数(intrinsic mode function，简称IMF)信号，针对单通道大跨径桥梁挠度信号分离问题，结合盲源分离和经验模式分解各自优点，提出基于经验模式分解的盲源分离方法。利用奇异值分解(singular value decomposition，简称SVD)估计信号源数目，根据源信号数目将单通道挠度信号和其本征模函数重组为多通道输入信号，应用独立分量分析（independent component analysis，简称ICA）理论中的快速独立分量分析（fast independent component analysis，简称FastICA）算法对输入信号进行分解，实现桥梁挠度信号各分量的分离。仿真研究表明，该方法能较好地解决ICA模型源数估计和单通道挠度信号盲源分离难题。

Separation of Real-Time Dynamic Deflection Signals for Long-Span Bridges

The real-time dynamic deflection signal separation of the long-span bridge is an effective new method for the long span bridge fault diagnosis. Because empirical mode decomposition (EMD) can decompose nonlinear and non-stationary signals into a set of linear and stationary intrinsic mode functions, a blind source separation (BSS) method based on EMD is proposed that combines the respective advantages of BSS and EMD for single-channel long-span bridge deflection signal separation, while EMD can decompose the nonlinear and non-stationary signals into a set of linear and stationary intrinsic mode functions. The source number is estimated using singular value decomposition (SVD), and then to composes the multi-channel input signals with the single channel deflection signal and its intrinsic mode functions according to the source number. Finally, the method separates the source signals using the fast independent component analysis (Fast ICA) algorithm and obtains the separation value of each component of the bridge deflection signal. Simulation research indicates that this is a good solution for estimating the quantity of source number estimation for the independent component analysis (ICA) model and single-channel deflection signal BSS problem.