| 基于小波变换和ICA特征提取的开关电流电路故障诊断 |
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| 引用本文： | 龙英,何怡刚,张镇,谢明华,尹柏强.基于小波变换和ICA特征提取的开关电流电路故障诊断[J].仪器仪表学报,2015,36(10):2389-2400. |
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| 作者姓名： | 龙英 何怡刚 张镇 谢明华 尹柏强 |
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| 作者单位： | 1.长沙学院环境光催化应用技术湖南省重点实验室长沙410003；
2.合肥工业大学电气与自动化学院合肥230009 |
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| 基金项目： | 国家自然科学基金（61201108,61501162）、中国博士后科学基金（2014M551797，2015T80650,2015M571926）项目资助 |
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| 摘 要： | 提出了采用小波变换和独立成分分析(ICA)作为预处理器来进行特征提取的神经网络开关电流电路故障诊断方法。该方法对采集到的故障响应信号进行Haar小波正交滤波器分解,获得低频近似信息和高频细节信息;然后利用独立成分分析方法进行ICA故障特征提取;最后将所得到的最优故障特征输入到BP神经网络中进行故障分类。对六阶切比雪夫低通滤波器和六阶椭圆带通滤波器电路进行了仿真实验验证,获得了100%的故障诊断准确率,与其他方法进行比较,实验结果显示了该方法的优越性。
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| 关 键 词： | 开关电流电路 Haar小波变换 ICA特征提取 故障诊断 |
Switched current circuit fault diagnosis based on wavelet
transform and ICA feature extraction |
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| Long Ying,He Yigang,Zhang Zhen,Xie Minghu,Yin Boqiang.Switched current circuit fault diagnosis based on wavelet
transform and ICA feature extraction[J].Chinese Journal of Scientific Instrument,2015,36(10):2389-2400. |
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| Authors: | Long Ying He Yigang Zhang Zhen Xie Minghu Yin Boqiang |
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| Affiliation: | 1.Hunan Province Key Laboratory of Applied Environmental Photocatalysis, Changsha University, Changsha 410003,China;2.School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China |
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| Abstract: | A neural network switched current circuit fault diagnosis approach utilizing wavelet transform and ICA feature extraction as preprocessors for feature extraction is proposed. The diagnostic approach performs Haar wavelet transform (HWT) on the acquired fault response signals, and the low frequency approximation information and high frequency detail information are obtained, then ICA fault feature is extracted by employing independent component analysis method. Finally, the obtained optimal fault features are sent to BP neural network to classify different faults. A 6th order Chebyshev low pass filter circuit and a 6th order Elliptic bass pass filter circuit were used to conduct simulation experiment and verify the proposed method, and the fault diagnosis accuracy of 100% is achieved. Compared with other methods, the experiment result indicates the superiority of the presented method. |
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| Keywords: | switched current circuit Haar wavelet transform ICA feature extraction fault diagnosis |
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