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首页> 外文期刊>Science, Measurement & Technology, IET >Feature extraction of analogue circuit fault signals via cross-wavelet transform and variational Bayesian matrix factorisation
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Feature extraction of analogue circuit fault signals via cross-wavelet transform and variational Bayesian matrix factorisation

机译：通过交叉小波变换和变分贝叶斯矩阵分解对模拟电路故障信号进行特征提取

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摘要

Analogue circuits are one of the most commonly used components in industrial equipment, but circuit failure may lead to significant causalities and even enormous financial losses. To address this issue, in this work the authors propose a new feature extraction scheme based on cross-wavelet transform (XWT) and variational Bayesian matrix factorisation (VBMF). Primarily, fault signals acquired from defect circuits are collected and processed by using XWT to obtain the joint time-frequency representation. VBMF is utilised to fetch the time-frequency information of the fault signal. A nine-dimensional feature vector is then constructed. Finally, a support vector machine optimised by a flower pollination algorithm is introduced to locate faults. Results show that the proposed approach can effectively locate the different kinds of defection while achieving a higher accuracy.
机译：模拟电路是工业设备中最常用的组件之一，但是电路故障可能导致重大的因果关系，甚至造成巨大的财务损失。为了解决这个问题，作者在这项工作中提出了一种基于交叉小波变换（XWT）和变分贝叶斯矩阵分解（VBMF）的新特征提取方案。首先，利用XWT收集并处理从缺陷电路获取的故障信号，以获得联合时频表示。 VBMF用于获取故障信号的时频信息。然后构造一个九维特征向量。最后，介绍了一种通过花授粉算法优化的支持向量机来定位故障。结果表明，所提出的方法可以有效地定位不同种类的缺陷，同时获得更高的准确性。

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  • 来源
    《Science, Measurement & Technology, IET》 |2019年第2期|318-327|共10页
  • 作者

    He Wei; He Yigang; Li Bing; Zhang Chaolong;

  • 作者单位

    Hefei Univ Technol, Sch Elect Engn & Automat, Hefei, Anhui, Peoples R China;

    Hefei Univ Technol, Sch Elect Engn & Automat, Hefei, Anhui, Peoples R China|Wuhan Univ, Sch Elect Engn, Wuhan, Hubei, Peoples R China;

    Hefei Univ Technol, Sch Elect Engn & Automat, Hefei, Anhui, Peoples R China;

    Hefei Univ Technol, Sch Elect Engn & Automat, Hefei, Anhui, Peoples R China|Anqing Normal Univ, Sch Phys & Elect Engn, Anqing, Peoples R China;

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