针对实测转子位移信号存在噪声污染的问题，提出一种基于 SVD 原理的 PCA 特征频率提取算法。首先，从理论上推导了 PCA 与 SVD 的内在联系，即 PCA 产生的协 方差矩阵特征值等于 SVD 产生的矩阵奇异值的平方，且 PCA 产生的特征向量等于 SVD 产生的左奇异向量; 然后，基于上述结论，提出一种基于 SVD 原理的 PCA 特征频率提 取算法，并通过仿真信号验证了算法的有效性; 最后，将该算法应用于大型滑动轴承试验台 主轴的轴心轨迹提纯，得到的轴心轨迹清晰、集中，可成功识别转子的不对中及碰磨故障。

A PCA feature frequency extraction algorithm based on SVD principle was proposed to solve the noise pollution problem in the measured displacement signal of rotor． Firstly，the intrinsic relationship between PCA and SVD was deduced theoretically． That is，the eigenvalue of the covariance matrix generated by PCA was equal to the square of the singular value of the matrix generated by SVD，and the eigenvector generated by PCA was equal to the left singular vector generated by SVD． Then，based on the above conclusions，a PCA feature frequency ex- traction algorithm based on SVD principle was proposed，and the effectiveness of the algorithm was verified by simu- lation signals． Finally，the algorithm was applied to purify axis orbits of the rotor of a large sliding bearing test bed． The axis orbits are clear and concentrated，the misalignment and friction faults were identified successfully．