基于AEEMD和改进DATA-SSI算法的桥梁结构模态参数自动化识别

摘要

Modal parameters are important dynamic indexes to monitor the service performance of bridge structures in practice.The precise identification of modal parameters is of great significance to bridges.On view of this,the current vibration signal de-noising algorithms and modal parameter identification algorithm are commented and improved to some extent.On the one hand,a novel adaptive signal decomposition and reconstruction algorithm,adaptive ensemble empirical mode decomposition (AEEMD),is proposed.Compared with the ensemble average empirical mode decomposition (EEMD),the proposed method can add amplitude standard deviation of white noise and average numbers of integration automatically according to the specific characteristics of signals.Not only can the endpoint effect effectively handled,but also can the modal aliasing phenomenon existed in the intrinsic mode function be avoided.Finally,the automated extraction of the effective IMF components and signal reconstruction can be fulfilled by the improved EEMD.On the other hand,Multidimensional data clustering analysis method is adopted in stochastic subspace identification to distinguish the false mode and the true mode intelligently by establishing a discriminant matrix model with vibration frequency,modal damping ratio and coefficients,thus the automatic modal parameter identification can be realized.Furthermore,the effectiveness of the proposed method is verified by both the simulated signal and testing signal from real bridge structure.The analysis results show that the proposed method can be used in the automatic model parameter identification of actual bridge structures.%模态参数作为桥梁结构最重要的动力参数之一,在实际运用中,可通过监测其变化情况来辨识结构的使用性能,精确地参数识别对保障桥梁健康运营具有十分重要的意义.鉴于此,该文对现阶段常用的振动信号降噪处理算法和模态参数识别算法进行了相应的改进.一方面,提出一种新的信号自适应分解与重构算法,即自适应总体平均经验模态分解算法(AEEMD),该算法相比总体平均经验模态分解算法(EEMD)而言,能够根据信号的自身特征自动化确定添加白噪声的幅值标准差和集成平均次数;能更好地处理端点效应;同时还能够保证所得本征模态函数之间不存在模态混叠现象;最终实现有效IMF分量的自动化筛选和信号重构.另一方面,将多维数据聚类分析算法引入随机子空间算法中,并以频率值、阻尼比以及振型系数为因子建立判别矩阵,以智能化区分虚假模态和真实模态,最终实现模态参数自动化识别.文章最后分别用模拟信号和实际桥梁测试信号对所提算法的有效性进行验证,结果表明,该文所提算法能运用于实际桥梁结构的模态参数自动化识别.