Abstract：In this paper, aiming at the shortcomings of the ensemble empirical mode decomposition algorithm, an improved decomposition algorithm is proposed based on clustering analysis to achieve the goal that the noise reduction and reconstruction of response signal. Firstly, make an analysis for the input signals, and then to verify the amplitude standard deviation of added white noise and integration times of EEMD. Secondly, making EEMD decomposition and clustering analysis for the obtained intrinsic mode function (IMF) by using Euclidean distance to verify that whether there is a modal aliasing in the obtained intrinsic mode functions. Finally, it is necessary that calculating the fuzzy similarity coefficient between each IMF and measured signals by using fuzzy comprehensive evaluation method in order to select out the effective IMF components, and then using principal component analysis method and Pareto Diagram method reconstruct the signal of preserved effective IMFs, so that can achieve effective decomposition of the measured signal and the noise reduction. An empirical analysis was taken to a large cable-stayed bridge in this
paper in oder to verify that the algorithm can be applied to actual bridges. In the first place, refactoring the measured response signal of the sensor and then regarding it as the input of the data-driven stochastic sunspace algorithm,which is be used to identify the modal parameters. And at the same time,making comparison analysis for the results of the various algorithms to further validate this algorithm is much more accurate than the existing algorithms. The conclusion is that this proposed algorithm can reconstruct better and has a great noise reduction performance of the response signal, and this result is more closer to the real value, which can be applied to modal parameters identification of real bridge.
收稿日期: 2015-07-28
引用本文:
陈永高，钟振宇. 基于CEEMD分解和Data-SSI算法的斜拉桥模态参数识别[J]. 振动与冲击, 2016, 35(8): 166-172.
Chen yonggao，ZHONG Zhenyu. Modal Parameter Identification of a Cable-Stayed Bridge Based on CEEMD and DATA-SSI Algorithm. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(8): 166-172.
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