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Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description

机译:支持向量数据描述在样本类型不完整情况下的故障诊断

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摘要

Faulty samples are much harder to acquire than normal samples, especially in complicated systems. This leads to incompleteness for training sample types and furthermore a decrease of diagnostic accuracy. In this paper, the relationship between sample-type incompleteness and the classifier-based diagnostic accuracy is discussed first. Then, a support vector data description-based approach, which has taken the effects of sample-type incompleteness into consideration, is proposed to refine the construction of fault regions and increase the diagnostic accuracy for the condition of incomplete sample types. The effectiveness of the proposed method was validated on both a Gaussian distributed dataset and a practical dataset. Satisfactory results have been obtained.
机译:有缺陷的样本比正常样本更难获取,尤其是在复杂的系统中。这导致训练样本类型不完整,进而降低诊断准确性。本文首先讨论了样本类型不完全性与基于分类器的诊断准确性之间的关系。然后,提出了一种基于支持向量数据描述的方法,该方法考虑了样本类型不完整的影响,以改进故障区域的构造并提高对样本类型不完整情况的诊断准确性。该方法的有效性在高斯分布数据集和实际数据集上均得到了验证。已经获得满意的结果。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第10期|432651.1-432651.10|共10页
  • 作者单位

    Nanjing Tech Univ, Coll Automat & Elect Engn, Nanjing 211816, Jiangsu, Peoples R China.;

    Jiangsu Key Lab Internet Things & Control Technol, Nanjing 210016, Jiangsu, Peoples R China.;

    Jiangsu Key Lab Internet Things & Control Technol, Nanjing 210016, Jiangsu, Peoples R China.;

    Nanjing Tech Univ, Coll Automat & Elect Engn, Nanjing 211816, Jiangsu, Peoples R China.;

    Jiangsu Key Lab Internet Things & Control Technol, Nanjing 210016, Jiangsu, Peoples R China.;

    Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China.;

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