针对多种故障类型的特征属性相互交叉导致故障难以辨识的问题，提出一种考虑相邻点之间成为近邻点概率的新度量函数。将新提出的近邻概率距离（Nearby Probability Distance）应用于局部保持投影算法（Locality Preserving Projection，LPP）与K-近邻（K-Nearest Neighbor, KNN）分类器中，提出基于近邻概率距离的局部保持投影算法（Nearby Probability Distance Locality Preserving Projection，NPDLPP）与基于近邻概率距离的K-近邻（Nearby Probability Distance K-Nearest Neighbor，NPDKNN）分类器。首先通过时域、频域特征提取方法，将振动信号转化为高维特征数据集，然后通过NPDLPP将高维数据集降维到低维空间，最后将降维得到的低维敏感特征集输入到NPDKNN中进行模式识别。用一个双跨度转子系统的振动信号集合进行验证，证明了本文提出的降维算法效果明显，它能够达到各个故障类型更好分离。研究表明，本研究新提出的近邻概率距离较传统的欧式距离测度更能最小化类内散度，最大化类间分离度。

Aiming at the problem of faults’ being difficult to identify due to their characteristics intersecting, a new measure function called the neighbor probability distance was proposed considering the probability for adjacent points to become neighbor ones. The neighbor probability distance was applied into the locality preserving projection (LPP) algorithm and K-nearest neighbor (KNN) classifier, then the neighbor probability distance-based locality preserving projection (NPDLPP) algorithm and the neighbor probability distance-based K-nearest neighbor (NPDKNN) classifier were proposed. Firstly, vibration signals were converted into high-dimensional data sets using feature-extracting methods in time domain and frequency domain. Then high-dimensional data sets were projected into a lower-dimensional space with NPDLPP. Finally, lower-dimensional sensitive feature sets obtained with dimension reduction were input into NPDKNN for pattern recognition. The vibration signal sets of a double-span rotor system were used to verify the proposed method. It was shown that the proposed method has an obvious effect to better separate various fault types; compared with the traditional Euclidean distance measure, the neighbor probability distance can minimize the divergence within a class and maximize the separation between classes.