摘要： 固有时间尺度分解(ITD)算法在前处理和系统定阶方面存在一定的人为因素,对模态参数的提取会造成误差,且对噪声较为敏感。针对上述问题,提出一种改进的ITD算法。利用基于数据驱动的随机子空间算法对原始数据进行处理,将正交三角分解得到的数据作为ITD法的输入数据,采用稀疏优化正交匹配追踪算法求出特征矩阵,并通过特征矩阵计算特征值、模态频率和阻尼比。通过统计的方法,从众多模态参数中选取真实模态,有效避免虚假模态的产生。实验结果表明,与ITD算法相比,改进ITD算法可降低噪声的影响,解决系统模型阶次必须准确定阶的要求,使模态参数的提取更加精确。
关键词:
固有时间尺度分解算法,
模态参数,
模型阶次,
稀疏优化,
相对误差
Abstract: The pre-processing and system order determination of Intrinsic Time-scale Decomposition(ITD) algorithm involve some human factors,which causes error on extraction of modal parameters,and ITD algorithm is also sensitive to noise.To solve these problems,an improved ITD algorithm is proposed.Firstly,the stochastic subspace algorithm based on data driving is used to deal with the original data.The data obtained by orthogonal triangular decomposition is used as the input data of ITD method.The sparse optimization orthogonal matching pursuit algorithm is used to find the feature matrix characteristic value,modal frequency and damping ratio can be calculated.The real modal is selected from many modal parameters through the method of statistics,which effectively avoids the false modal.Compared with ITD algorithm,the improved ITD algorithm reduces the influence of noise,solves the problem of system order determination and makes the extraction of modal parameters more precise.
Key words:
Intrinsic Time-scale Decomposition(ITD) algorithm,
modal parameter,
model order,
sparse optimization,
relative error
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