中国机械工程 ›› 2013, Vol. 24 ›› Issue (13): 1719-1723.

• 机械基础工程 • 上一篇    下一篇

基于谱聚类的振动多模态信号幅谱分割研究与应用

王丹丹;周宇;叶庆卫;王晓东   

  1. 宁波大学,宁波,315211
  • 出版日期:2013-07-10 发布日期:2013-07-11
  • 基金资助:
    国家自然科学基金资助项目(61141015);宁波市自然科学基金资助项目(2011A610181) 
    National Natural Science Foundation of China(No. 61141015);
    Natural Science Foundation of Ningbo(No. 2011A610181)

Spectrum Segmentation of Multi-mode Vibration Signals Based on Spectral Clustering

Wang Dandan;Zhou Yu;Ye Qingwei;Wang Xiaodong   

  1. Ningbo University,Ningbo,Zhejiang,315211
  • Online:2013-07-10 Published:2013-07-11
  • Supported by:
     
    National Natural Science Foundation of China(No. 61141015);
    Natural Science Foundation of Ningbo(No. 2011A610181)

摘要:

在强噪声下频域的模态峰往往受到强烈的干扰,导致模态参数的提取精度下降,甚至产生模态主频误判。针对这种情形,采用谱聚类算法对振动频谱进行宏观聚类,提出了一种新的幅谱分割方法。按照波峰概念把振动信号幅谱分割成波峰的集合,把每个波峰看成一个待聚类的样本,构建波峰相似度函数、拉普拉斯矩阵和聚类算法,引入谱聚类算法进行波峰自动聚类,聚类的结果就是宏观上的单模态大峰。仿真试验表明,这种幅谱波峰分割的谱聚类算法能够减小噪声和虚假模态的影响,与已有的k-means聚类算法相比,具有更强的噪声抵抗能力和更好的聚类能力。通过对斜拉索振动进行模态测试,证实该算法能够得到符合肉眼观察的幅谱分割效果,且具有较好的稳定性和准确性。

关键词: 多模态幅谱, 谱聚类, 波峰分割, 拉普拉斯矩阵

Abstract:

The modal peak in frequency domain was interfered strongly under the strong noise, causing the inaccurate modal parameters, as well as producing modal frequency miscalculation. According to this situation, this paper adopted spectrum clustering algorithms to cluster the vibration spectrum, and come up with a new method for spectrum segmentation. First, according to the concept of wave spectrum, the vibration signal spectrum was divided into pack of waves. For each wave as a sample of clustering, this paper used spectrum clustering to cluster these waves. The wave similarity function, Laplace matrix and clustering algorithm were constructed, and this algorithm was used to cluster automatically.  Finally, the clustering result is that a big peak in macroscopic. By the experimental results of simulation signals, this spectrum clustering algorithms can accord with the macroscopic observation of modal separation efficiency,and it is stronger to the noise resistance than that of the existing k-means algorithms. According to the test of a cable, the stability and accuracy of this algorithm is demonstrated again. 

Key words: multimode spectrum, spectral clustering, partition peaks of wave, Laplacian matrix

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