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    刘帮莉, 马玉鑫, 侍洪波. 基于加权距离邻域选取策略的多模态过程故障检测[J]. 华东理工大学学报(自然科学版), 2015, (2): 192-197.
    引用本文: 刘帮莉, 马玉鑫, 侍洪波. 基于加权距离邻域选取策略的多模态过程故障检测[J]. 华东理工大学学报(自然科学版), 2015, (2): 192-197.
    LIU Bang-li, MA Yu-xin, SHI Hong-bo. A Multimodal Process Fault Detection Method Based on Weighted Distance Neighborhood Selection Strategy[J]. Journal of East China University of Science and Technology, 2015, (2): 192-197.
    Citation: LIU Bang-li, MA Yu-xin, SHI Hong-bo. A Multimodal Process Fault Detection Method Based on Weighted Distance Neighborhood Selection Strategy[J]. Journal of East China University of Science and Technology, 2015, (2): 192-197.

    基于加权距离邻域选取策略的多模态过程故障检测

    A Multimodal Process Fault Detection Method Based on Weighted Distance Neighborhood Selection Strategy

    • 摘要: 针对多模态过程数据密度不规则性提出的一类基于密度的方法,大多是以欧式距离为基础来比较彼此间的相似性,从而检测过程是否发生故障。然而多模态数据密度在较小范围内变化较大,采用欧式距离很难获得全面的数据信息。本文提出了一种新的基于加权距离选择邻居的策略,该策略首先对距离进行合理的加权,再根据新的加权距离重新选择样本点的邻居,能有效地避免数据信息不全面的问题。在仿真实验中,首先通过比较基于传统的欧式距离和基于本文加权距离选取的邻居,说明本文策略的优越性;进而将该策略与局部离群因子(Local Outlier Factor, LOF)结合用于TE过程,对TE过程的仿真结果表明该策略在应用于基于密度的检测方法上获得了的良好效果。

       

      Abstract: Aiming at the irregularity of data density in multi model processes, most of the existing methods have utilized Euclidean distance to detect the fault in the process by comparing the similarity between each other. However, because there may be different data density in complex multi modal processes, it is difficult to obtain complete information by using Euclidean distance. This paper presents a new weighted distance based strategy to select neighbors. Firstly, the distance is weighted reasonably, which is then utilized to re select the neighbors of each sample point. This can effectively avoid the incompleteness of data information. A comparison simulation with the method based on traditional Euclidean distance shows that the proposed method based weighted Euclidean distance can get better results of the monitoring. Finally, this paper combines the proposed with the local outlier factor (LOF) to monitor TE process, whose results illustrates the superiority of this new selecting neighbor method.

       

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