一种基于多流形局部线性嵌入算法的故障诊断方法

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故障类别的多样性往往导致原始样本数据在特征空间中呈间断性分布，针对传统k近邻的邻域构建方法难以保证数据集几何结构完整性的问题，提出一种新的非线性最小二乘约束．局部线性嵌入算法。通过非负线性最小二乘约束搜索边界点，并结合第一主成分直线寻找其邻域样本点，重新构造关于边界点的邻域图，用经典的局部线性嵌入算法机理发现数据内在分布和几何结构，根据得到的低维嵌入采用KNN分类器进行故障模式识别：仿真数据分析与试验验证结果表明该算法高度保持了原始数据的几何拓扑结构，增强了低维嵌入的有效性，提高了故障识别精度。

The original data sets are disjunctively distributed in the feature space frequently because of the diversity of faults; the completeness of the geometric structure cannot be keep up by the classical neighborhood structure with k-nearest neighbor. A new approach nonnegative linear least squares locally linear embedding （NLLS-LLE） is presented to solve this problem. The boundary points are searched with the nonnegative linear least squares constraints, the neighborhoods of boundary points are determined by the first principal straight line. The neighborhood graphs of boundaries are reconstructed, and the intrinsic distribution and geometry structure of data set is discovered by the classical locally linear embedding, and the fault modes are recognized accurately by KNN classifiers where the low dimensional spaces projection used as inputs. The simulation data analysis and experiment show that the proposed algorithm highly keeps geometry and topology of original data sets, enhances the validity of low dimensional spaces, and improves the classification correctness of fault.

局部线性嵌入，多流形，非负线性最小二乘，故障诊断，Locally linear embedding Multi-manifold Nonnegative linear least squares Fault diagnosis。

一种基于多流形局部线性嵌入算法的故障诊断方法。