电子测量与仪器学报

谈恩民,王存存,张欣然.基于小波变换和CFA-LSSVM模拟电路故障诊断[J].电子测量与仪器学报,2017,31(8):1207-1212

基于小波变换和CFA-LSSVM模拟电路故障诊断

Analog circuit fault diagnosis based on wavelet transform and CFA-LSSVM

DOI：10.13382/j.jemi.2017.08.007

中文关键词: 故障诊断特征提取提升小波变换因子分析混沌萤火虫算法最小二乘支持向量机

英文关键词:fault diagnosis feature extraction lifting wavelet transform factor analysis chaotic firefly algorithm LSSVM

基金项目:

作者	单位
谈恩民	桂林电子科技大学电子工程与自动化学院桂林541004
王存存	桂林电子科技大学电子工程与自动化学院桂林541004
张欣然	桂林电子科技大学电子工程与自动化学院桂林541004

Author	Institution
Tan Enmin	School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Wang Cuncun	School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Zhang Xinran	School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China

摘要点击次数: 3056

全文下载次数: 16328

中文摘要:

为了提高模拟电路软故障诊断、识别的正确分类率，提出了一种提升小波变换和混沌萤火虫算法(CFA)优化LSSVM参数的模拟电路故障诊断方法。首先对采集到的被测电路输出电压信号进行提升小波变换；然后对变换后的数据进行因子分析法对优化处理，将经优化的数据作为不同模式的故障特征集；最后将所得故障特征集作为样本输入到CFA LSSVM模型进行故障诊断。实验结果表明，该方法的故障诊断正确率达到了98%以上，提高了诊断性能，可适用于模拟电路的故障诊断。

英文摘要:

In order to improve the correct classification rate of analog circuit fault diagnosis and recognition, a simulation circuit fault diagnosis method based on lifting wavelet transform and chaotic firefly algorithm (CFA) is proposed to optimize LSSVM parameters. Firstly, the wavelet transform is applied to the output voltage signal of the measured circuit. Then, the transformed data is analyzed by factor analysis method, and the optimized data is taken as the fault feature set of different modes. Finally, the obtained fault feature set as sample is imported into the CFA LSSVM model for troubleshooting. The experimental results show that the fault diagnosis accuracy of this method is more than 98%, which improves the diagnostic performance and can be applied to the fault diagnosis of analog circuits.

查看全文查看/发表评论下载PDF阅读器