关键词: 局部线性嵌入算法, 特征提取, 降维, 切空间度量, 自适应邻域, 故障诊断

Abstract:

As a classic manifold dimensionality reduction algorithm， Locally Linear Embedding （LLE） algorithm is widely used in the field of fault diagnosis because of its good feature extraction ability. However， due to the inherent defects of LLE algorithm， such as sensitivity to neighborhood parameters and single structure of mining， the features extracted by it have poor discrimination ability in practical applications. Therefore， a Discriminant Similarity and Tangent Adaptive Neighborhood Locally Linear Embedding （DSTANLLE） algorithm was proposed and applied to the bearing fault diagnosis. Firstly， a new metric method， which fuses tangent space， was used to evaluate the local similarity between samples. Then， an adaptive neighborhood graph was constructed to select neighbors for each sample point. Finally， the discriminant similarity information was added to extract the discriminant structure of the data. Experimental results on two synthetic datasets and two bearing fault datasets show that DSTANLLE algorithm can extract significant discriminative features from data， achieving an OA （Overall Accuracy） of up to 100% in the application of bearing fault diagnosis.

Key words: Local Linear Embedding (LLE) algorithm, feature extraction, dimension reduction, tangent space metric, adaptive neighborhood, fault diagnosis