Rapid purification of rotor system axis trajectory based on improved SVD algorithm
SU Zina1，MA Jun1,2，WANG Xiaodong1,2，XIONG Xin1,2
1.Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China;
2.Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China
摘要为实现转子系统轴心轨迹的快速提纯，提出一种改进的奇异值分解（Improved Singular Value Decomposition，ISVD）算法。首先，构建一种奇异值差异比（Singular Value Difference Ratio，SVDR）的评判指标来确定矩阵行数，对Hankel矩阵的结构进行优化；其次，利用奇异值与频率的数量关系筛选有效分量，对有效分量进行重构得到特征信号；然后，将降噪提纯后的特征信号合成轴心轨迹，实现轴心轨迹的提纯；最后，利用仿真和实测信号对所提方法进行分析。实验结果表明，与最大维数法相比，在SVDR确定的矩阵结构下，奇异值分解的降噪性能保持不变，但分解的时间缩短了90.18%，提高了计算效率。
Abstract：An improved singular value decomposition (ISVD) algorithm is proposed to achieve rapid purification of rotor center trajectory. Firstly, a singular value difference ratio (SVDR) evaluation index is constructed to determine the number of matrix rows, and the structure of Hankel matrix is optimized. Secondly, the quantitative relationship between singular value and frequency is used to screen the effective components, and the effective components are reconstructed to obtain the characteristic signals. Then, the characteristic signals after denoising and purification are combined into the shaft center trajectory to achieve the purification of the shaft center trajectory. Finally, the proposed method is analyzed by simulation and experiment. The experimental results show that compared with the matrix structure determined by the maximum dimension method, the calculation time of the matrix structure determined by SVDR criterion is reduced by 90.18%. Its computational efficiency has been improved.
苏紫娜1，马军1,2，王晓东1,2，熊新1,2. 改进SVD算法的转子系统轴心轨迹快速提纯研究[J]. 振动与冲击, 2023, 42(10): 144-154.
SU Zina1，MA Jun1,2，WANG Xiaodong1,2，XIONG Xin1,2. Rapid purification of rotor system axis trajectory based on improved SVD algorithm. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(10): 144-154.
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