基于多信息融合的自适应局部线性嵌入算法
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东北石油大学电气信息工程学院

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TH133.3

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海南省科学技术厅, 海南省自然科学基金, 623MS071, 基于深度学习的油气管道泄漏故障检测技术


Adaptive Local Linear Embedding algorithm based on multiple information fusion
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    摘要:

    局部线性嵌入算法LLE的降维性能与挖掘的流形结构密切相关,但LLE挖掘的流形结构单一,并且对邻域参数选取敏感,无法提取全面的流形局部结构,限制了LLE的降维性能。为此,本文提出基于多信息融合的自适应局部线性嵌入算法MIF-ALLE。MIF-ALLE首先利用切空间近似判据自适应选择邻域参数,获取更准确的局部邻域;然后,将局部邻域中蕴含的切空间角度信息与局部线性信息相融合,挖掘更全面的流形局部结构,降低局部低维嵌入的偏差;最后,在公开轴承数据集以及实验室提取的轴承数据集上进行实验验证。实验结果表明:MIF-ALLE可以挖掘更全面的流形结构,提取更显著的特征,轴承故障诊断准确率最高可达100%。

    Abstract:

    The dimensionality reduction performance of Local Linear Embedding algorithm LLE is closely related to the manifold structure mined. However, the manifold structure mined by LLE is singular and sensitive to the selection of neighborhood parameters, making it difficult to extract a comprehensive local structure of the manifold, which limits its dimensionality reduction performance.Therefore, this article proposes an adaptive local linear embedding algorithm based on multiple information fusion MIF-ALLE. MIF-ALLE firstly uses tangent space approximation criterion to adaptively select neighborhood parameters to obtain more accurate local neighborhood; Then, the angle information of Tangent space contained in the local neighborhood is fused with the local linear information to mine a more comprehensive local structure of the manifold and reduce the deviation of local low dimensional embedding; Finally, the experimental verification is carried out on the bearing data set published and the bearing data set extracted from the laboratory. The experimental results show that MIF-ALLE can mine more comprehensive manifold structures, extract more significant features, and achieve bearing fault diagnosis accuracy of up to 100%.

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  • 收稿日期:2023-07-14
  • 最后修改日期:2023-10-11
  • 录用日期:2023-10-12
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