中文摘要：

针对旋转机械不同故障可能分布于不同故障流形，提出了基于多故障流形的旋转机械故障诊断方法。该方法分别提取每一类故障对应的故障流形，并在多故障流形上进行新增样本的故障识别。针对所需解决的低维流形提取、流形内蕴维数选取和多故障流形上的故障识别问题，分别采用线性局部切空间排列算法和免疫遗传算法来进行低维故障流形提取和流形内蕴维数选取，并通过故障样本重构误差这一新的判别准则进行故障识别。齿轮箱故障模拟实验的结果验证了此方法的有效性。

英文摘要：

The existing fault diagnosis methods based on manifold learning assume that all the faults distribute on a single mani-fold,however the faults may distribute on different manifolds in practical applications.Aiming at this problem,rotating ma-chinery fault diagnosis method based on multiple fault manifolds is proposed.Firstly,mixed-domain features are extracted from the vibration signals to characterize the property of the faults,and the vibration signals are also preprocessed by empirical model decomposition before feature extraction.Then,the corresponding fault manifold of each fault is extracted from the high-dimensional fault samples.In the method,linear local tangent space alignment is applied to solve the problem of low-dimen-sional manifold extraction,and immune genetic algorithm is used to select the intrinsic dimensionality of fault manifold.At last,the test samples are respectively projected to all the fault manifolds,and the projection errors are used as the criterion to determine the fault types of the test samples.In order to verify the effectiveness of the proposed fault diagnosis method,the method is applied to diagnose the faults of the gear box.The experimental results indicate that feature compression can remove the redundant information between features,and moreover fault diagnosis method based on multiple fault manifolds can obtain even better performance than those methods which project all the faults to a single low-dimensional manifold.