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Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm

机译：基于局部线性嵌入算法的监测和均匀化距离故障诊断

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摘要

In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance.
机译：针对实际故障样本分布不均，局部线性嵌入算法容易受到邻近点影响的降维效果等问题，提出了一种改进的均化距离局部线性嵌入算法。该方法使样本点的整体分布趋于均匀化，并使用均匀化距离而不是传统的欧几里得距离来减少相邻点的影响。选择有效的相邻点来构造权重矩阵以进行降维是有帮助的。由于HLLE的故障识别性能提高有限且不稳定，因此，通过添加监督学习机制，进一步提出了一种新的监督和均化距离局部线性嵌入算法（SHLLE）。在同质化距离的基础上，监​​督学习增加了样本点的类别信息，从而可以收集相同类别的样本点，并分散异构的样本点类别。它有效地提高了故障诊断的性能，同时保持了稳定性。通过仿真实验与转子系统故障诊断比较了上述方法，结果表明SHLLE算法具有较好的故障识别性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|981598.1-981598.8|共8页
  • 作者

    Wang Guangbin; Luo Jun; He Yilin; Chen Qinyi;

  • 作者单位

    Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Hunan, Peoples R China;

    Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Hunan, Peoples R China;

    Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Hunan, Peoples R China;

    ChuXiong Ind Sch, Chuxiong City 675099, Yunnan, Peoples R China;

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