基于递归理论的泵站压力管道运行状态监测

为了准确地识别建筑结构的模态参数，提出了一种基于多重信号分类算法（multiple signal classification,简称MUSIC）、经验小波变换（empirical wavelet transform,简称EWT）和同步提取小波变换（synchroextracting transform ，简称SET）的结构模态参数识别方法。首先，通过MUSIC-EWT对实测振动信号进行分解；其次，使用SET对单模态信号进行去噪处理；然后，采用自然环境激励技术（natural excitation technique,简称NExT）得到单模态信号的自由衰减响应；最后，利用Hilbert变换（hilbert transform,简称HT）和曲线拟合获得结构的自振频率和阻尼比。通过三层框架结构的数值模拟验证了该方法的准确性和鲁棒性。利用该方法对台风“达维”作用下广州中信广场的实测加速度数据进行分析，并将估计的结构模态参数和其他识别方法的分析结果进行对比，进一步证明了该方法的准确性和鲁棒性。

Monitoring the Operating Condition of Pipeline of Pumping Stations Based on Recurrence Theory

It is important to accurately identify structural modal parameters for structural health monitoring and damage identification. In this paper, a new structural modal parameter identification approach is developed based on multiple signal classification algorithm (MUSIC), empirical wavelet transform (EWT) and synchroextracting transform (SET). In the proposed method, the measured vibration signal is decomposed by MUSIC-EWT to obtain a single mode signal. Secondly, SET is used to remove noise from each mono component. Then, the free vibration response of each mono component is obtained using natural excitation technique (NExT). Finally, Hilbert transform (HT) and curve fitting method are used to estimate the natural frequency and the damping ratio. A numerical simulation of a three-story frame structure is employed to validate the effectiveness and robustness of the method. Furthermore, the measured acceleration responses of CITIC Plaza during Typhoon Damrey are analyzed based on the approach. The accuracy of the developed method is further verified by comparing the identified modal parameters of this super-tall building with those determined by some other existing methods.