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中文摘要：
提出一种基于轴心轨迹流形拓扑空间的转子系统故障诊断新方法。采用总体平均经验模态分解对采集现场的振动信号进行处理，将降噪后的IMF分量重构形成还原信号。采用还原的信号构成轴心轨迹，将获得的每一个轴心轨迹看作高维流形特征的一个维度，进行轴心轨迹流形高维空间的相重构。采用基于非线性局部切空间流形学习算法对高维特征进行降维，网格搜索算法进行局部切空间维数和邻域点数的寻优，最终获得轴心轨迹流形拓扑空间内的敏感特征。应用该方法进行试验台转子系统的正常、不对中、碰摩三种类型的故障诊断，得到可视化的流形敏感特征。将其应用于数控车床主轴正常和偏心的特征获取并采用支持向量机（Supportvectormachine，SVM）进行故障诊断，训练准确率为98．8636％，测试准确率为99％。验证了算法的有效性。
英文摘要：
A new fault diagnosis method based on orbit manifold topological space is put forward. Firstly, vibration signals from the working is reduced the noise by using EEMD for vibration signal noise reduction. Restored signal constitutes an orbit. An orbit of the orbit track points is defined as a dimension feature of the high-dimensional space; the high-dimensional characteristics space is reduced into lower-dimensional space based on LTSA algorithm, then orbit manifold sensitive characteristics in the topological space are obtained. It provides a new method for fault diagnosis of electrical system. Application of the method to test-bed of normal, misalignment, rotor rub-impact fault diagnosis of three types, and fault diagnosis of NC lathe spindle and eccentric properly, the correct rate is up to 99%. The validity of the algorithm is verified.
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