文章摘要
基于局部线性嵌入的多流形学习故障诊断方法
Fault diagnosis method based on mutil-local linear embedding
  
DOI:
中文关键词: 局部线性嵌入  多流形学习  特征提取  故障诊断
英文关键词: local linear embedding  Multi-manifold learning  feature extraction  fault diagnosis
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作者单位
王广斌1,罗军1,贺旖琳1,杜晓阳1,陈庆怡2 1.湖南科技大学 湖南省机械设备健康维护重点实验室湖南 湘潭 4112012.云南省楚雄州工业学校云南 楚雄 675000 
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中文摘要:
      故障样本具有复杂多样性,而不同故障类型存在于不同维数的多流形子空间中,将样本统一降维到同一维数的单流形上则不能进行高效的特征提取.提出了一种基于局部线性嵌入(Local Linear Embedding,LLE)的多流形学习(Multi-LLE)故障诊断方法,将单流形故障诊断方法扩展到多流形,首先利用Multi-LLE分别提取各故障数据集在其本征维数流形上的特征,再通过各特征向量的聚类中心与故障新样本在不同维数下的嵌入向量的距离比较,将距离最近者归为一类实现分类识别.利用转子实验故障数据对算法进行了验证,并将Multi-LLE方法与LLE和海赛局部线性嵌入(HLLE)方法进行了比较,结果表明该方法能够有效的实现故障诊断.
英文摘要:
      Fault samples are of complex diversity, different fault types exist in multi-manifold subspace of different dimensions, which reduce dimension to single manifold of the same dimension can not be more efficient feature extraction. A fault diagnosis method based on multi-local linear embedding (Multi-LLE) was proposed, the single manifold fault diagnosis method was extended to the multi-manifold, Multi-LLE were extraited respectively the essential characteristics of each failure data sets which on its manifolds of the intrinsic dimension, to classify by comparing the distance of cluster centers of each feature vector and embedded vector of new failure samples on different dimensions. To verify the algorithm by making use of rotor test failure data, the results show that Multi-LLE method achieve more effective fault diagnosis from comparison Multi-LLE and LLE, HLLE.
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