为了解决有效特征值的选择问题，从理论上证明了有效特征值的数量规律，即一个频率对应两个特征值; 推导了特征值的排序规律，即信号的幅值越大，其对应的两个特征值也越大。将上述两个性质统称为主成分分析的幅值滤波特性，提出了一种基于该特性的信号分离算法，并通过仿真信号及实际转子信号的分析，验证了算法在信号分离中的有效性。研究结果表明，该算法无论是在同时提取多个频率成分还是提取单个频率成分方面都表出了优良的特性，提纯信号中既不会含有多余成分，也不会发生相位偏移。最后将本文提出的算法应用于大型滑动轴承试验台转子的轴心轨迹提纯，成功识别了转子的不对中故障。

To solve the problem of selecting effective eigenvalues，the number law of effective eigenvalues was de-duced theoretically． That is，one frequency corresponds to two eigenvalues． The order rule of eigenvalues was de-duced as well． That is，the larger the amplitude of signal is，the larger the corresponding two eigenvalues are．The above two properties were collectively referred as amplitude filter characteristics of principal component analysis (PCA-AF)，and a novel signal separation algorithm based on this characteristic was proposed． Through the analy-sis of simulation signal and the actual rotor signal，the effectiveness of the algorithm for signal separation was veri-fied． Ｒesearch results show that the algorithm has excellent advantage in both extracting multiple and single fre-quency components，and the purified signal does not contain redundant components，nor does phase deviation oc-cur． Finally，the proposed algorithm was applied to purify rotor axis orbit of large sliding bearing test bed，and the misalignment fault of the rotor was identified successfully．