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首页> 外文期刊>Circuits, systems and signal processing >A Novel Method for the Diagnosis of the Incipient Faults in Analog Circuits Based on LDA and HMM
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A Novel Method for the Diagnosis of the Incipient Faults in Analog Circuits Based on LDA and HMM

机译：基于LDA和HMM的模拟电路早期故障诊断新方法。

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摘要

Diagnosis of incipient faults for electronic systems, especially for analog circuits, is very important, yet very difficult. The methods reported in the literature are only effective on hard faults, i.e., short-circuit or open-circuit of the components. For a soft fault, the fault can only be diagnosed under the occurrence of large variation of component parameters. In this paper, a novel method based on linear discriminant analysis (LDA) and hidden Markov model (HMM) is proposed for the diagnosis of incipient faults in analog circuits. Numerical simulations show that the proposed method can significantly improve the recognition performance. First, to include more fault information, three kinds of original feature vectors, i.e., voltage, autoregression-moving average (ARMA), and wavelet, are extracted from the analog circuits. Subsequently, LDA is used to reduce the dimensions of the original feature vectors and remove their redundancy, and thus, the processed feature vectors are obtained. The LDA is further used to project three kinds of the processed feature vectors together, to obtain the hybrid feature vectors. Finally, the hybrid feature vectors are used to form the observation sequences, which are sent to HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method has better recognition capability than the popularly used backpropagation (BP) network.
机译：电子系统，尤其是模拟电路的早期故障的诊断非常重要，但也非常困难。文献中报道的方法仅对硬故障，即部件的短路或开路有效。对于软故障，只能在组件参数变化较大的情况下才能诊断故障。本文提出了一种基于线性判别分析（LDA）和隐马尔可夫模型（HMM）的新方法，用于诊断模拟电路中的早期故障。数值仿真表明，该方法可以显着提高识别性能。首先，为了包括更多的故障信息，从模拟电路中提取了三种原始特征向量，即电压，自回归移动平均值（ARMA）和小波。随后，使用LDA减小原始特征向量的维数并消除其冗余，从而获得处理后的特征向量。 LDA还用于将三种处理后的特征向量投影在一起，以获得混合特征向量。最后，将混合特征向量用于形成观测序列，然后将其发送到HMM以完成对早期故障的诊断。测试了该方法的性能，表明该方法具有比流行的BP网络更好的识别能力。

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  • 来源
    《Circuits, systems and signal processing》 |2010年第4期|P.577-600|共24页
  • 作者

    Lijia Xu; Jianguo Huang; Houjun Wang; Bing Long;

  • 作者单位

    School of Automation Engineering of University of Electronic Science and Technology of China, Chengdu 610054, China Information and Engineering Technology Institute of Sichuan Agriculture University, Ya'an 625014, China;

    rnSchool of Automation Engineering of University of Electronic Science and Technology of China, Chengdu 610054, China;

    rnSchool of Automation Engineering of University of Electronic Science and Technology of China, Chengdu 610054, China;

    rnSchool of Automation Engineering of University of Electronic Science and Technology of China, Chengdu 610054, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    HMM; LDA; feature extraction; fault diagnosis;

    机译：HMM;LDA;特征提取;故障诊断;

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