College of Sciences, National University of Defense Technology, Changsha 410073, China.
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
Sensors (Basel). 2023 Apr 5;23(7):3759. doi: 10.3390/s23073759.
The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately.
奇异值分解包（SVDP）常用于信号分解和特征提取。目前，由于汉克尔矩阵的两行结构，一般的 SVDP 特征提取能力不足，导致模式混合。本文提出了一种改进的奇异值分解包（ISVDP）算法：通过改变汉克尔矩阵的结构来提高特征提取能力，并通过相似性选择相似的信号子分量，以避免将具有相同频率分量的信号分解为不同的子信号。本文通过一组模拟信号说明了 ISVDP 的有效性，并将其应用于轴承数据的故障诊断。结果表明，ISVDP 可以有效地抑制模型混合现象，并能更准确地提取轴承振动信号中的故障特征。
