中文摘要：

拉普拉斯特征映射算法能有效提取高维非线性数据中嵌入的低维流形特征。将其引入到设备故障诊断领域,应用于故障模式识别问题,提出了一种基于拉普拉斯特征映射的故障模式识别新方法。运用基于拉普拉斯特征映射的非线性降维算法直接对原始故障信号进行学习,提取出数据内在的流形特征,极大地保留了信号中内含的整体几何结构信息,有效克服了常规模式识别方法仅能获得局部线性结构的不足,明显改善了故障模式识别的分类性能。仿真和工程实例结果表明了所提方法的可行性和有效性。

英文摘要：

The Laplacian Eigenmaps algorithm can effectively extract the low dimension feature embedded in high dimension nonlinear data. It was introduced into the fault diagnosis field and was applied for fault pattern recognition problem,and a new method of fault pattern recognition based on the Laplacian Eigenmaps （LE-FPR） was proposed. A nonlinear dimensionality reduction algorithm based on Laplacian Eigenmaps was used to directly learn original fault signal and extract the intrinsic manifold feature in data set. The method greatly preserved the whole geometry structure information embedded into the signal,overcame the flaw of conventional pattern recognition methods which only obtained the local linear structure in data set,and obviously improved the classification performance of fault pattern recognition. The simulation and instance results demonstrate the feasibility and effectiveness of the new method.