Abstract：Focusing on the performance of local linear embedding (LLE) with respect of fault feature extraction which is influenced by the noise, embedding dimensionality and neighborhood size, it is improved form both the estimation of weight coefficient and the selection of neighborhood sizes and embedding dimensionality. The cross-correntropy is being substituted for the Euclidean distance to measure similarity of vectors. An estimation model of weight coefficient is created based on cross-correntropy. At the same time, it is simplified by the Lagrange method because of computation difficulties. The model of weight coefficient based on cross-correntropy will improve the performance of the local linear embedding and reduce the influence from noise in fault feature extraction. The Ncut criterion is employed to choosing the neighborhood sizes and embedding dimensionality. A model for choosing the parameters in a more automatic way is created. The improved LLE is employed in the fault feature extraction of rolling bearings. The experimental results for fault diagnosis of rolling ball bearings show that the proposed approach, compared with other approaches, is more effective to extract the fault features form vibration signals, and enhance the classification ability of failure pattern.
收稿日期: 2014-05-04
出版日期: 2015-08-15
引用本文:
胡峰，苏讯，刘伟，吴雨川，范良志. 基于改进局部线性嵌入算法的故障特征提取方法[J]. 振动与冲击, 2015, 34(15): 201-204.
Hu Feng, Su Xun, Liu Wei,Wu Yuchuan, Fan Liangzhi. Fault Feature Extraction Based on improved Locally Linear Embedding. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(15): 201-204.
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