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| 基于提升小波变换和SVM的模拟电路故障诊断 | |
| 引用本文： | 宋国明,王厚军,刘红,姜书艳.基于提升小波变换和SVM的模拟电路故障诊断[J].电子测量与仪器学报,2010,24(1):17-22. |
| 作者姓名： | 宋国明 王厚军 刘红 姜书艳 |
| 作者单位： | 1. 电子科技大学自动化工程学院,成都610054;成都电子机械高等专科学校计算机工程系,成都610031 2. 电子科技大学自动化工程学院,成都,610054 3. 电子科技大学自动化工程学院,成都610054;长春理工大学计算机学院,长春130022 |
| 基金项目： | 国防基础科研(编号:A1420061264)资助项目;;国家自然科学基金(编号:60673011)资助项目 |
| 摘 要： | 故障特征提取和分类器设计是模拟电路故障诊断的两个重要环节,为了提高模拟电路故障辨识的准确率,提出了提升小波变换与支持向量机相结合的故障诊断方法。根据提升小波变换的原理,提取被测电路单脉冲响应信号的小波系数构成故障特征,建立以支持向量机为分类器的故障诊断系统。该方法对两个滤波器电路的故障诊断取得了满意的效果,在故障模式较多的情况下故障分类的精度达到了99%以上,优于传统的小波方法。 |
| 关 键 词： | 模拟电路 故障诊断 提升小波变换 支持向量机 |
Analog circuit fault diagnosis using lifting wavelet transform and SVM |
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| Song Guoming,Wang Houjun,Liu Hong,Jiang Shuyan.Analog circuit fault diagnosis using lifting wavelet transform and SVM[J].Journal of Electronic Measurement and Instrument,2010,24(1):17-22. | |
| Authors: | Song Guoming Wang Houjun Liu Hong Jiang Shuyan |
| Affiliation: | 1. School of Automation Engineering;University of Electronic Science and Technology of China;Chengdu 610054;China;2. Computer Engineering Department;Chengdu Electromechanical College;Chengdu 610031;3. School of Computer Science and Technology;Changchun University of Science and Technology;Changchun;130022;China |
| Abstract: | Feature extraction and classifier construction are two important stages for analog circuit fault diagnosis. In order to improve correctness rate of fault identification, an approach based on lifting wavelet transform (LWT) and support vector machine (SVM) is proposed. According to the theory of lifting wavelet transform, the impulse response signal of CUT is sampled and decomposed to form fault features, and then the analog circuit fault diagnosis system is established. The experimental results on two filte... |
| Keywords: | analog circuit fault diagnosis lifting wavelet transform SVM |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |