关键词: 模拟电路软故障诊断, 特征提取, 小波包能量谱, 时域统计特征, 核判别分析, 有向无环图支持向量机

Abstract: In order to solve the problem of incomplete and inaccurate feature extraction in analog circuit soft fault diagnosis, a new method of two classes feature extraction based on Kernel Linear Discriminant Analysis（KLDA） following integrated time and frequency domains is proposed in this paper. At first, the statistics features in time domain such as mean, standard deviation and energies within different frequency bands by wavelet packet decomposition as frequency domain features are extracted from the acquired fault response signals. Then the kernel linear discriminant analysis method is used to further optimize and select fault features, which guarantees the validity on fault feature. Finally, the obtained optimal fault features are inputted into support vector machine to distinguish different faults. Experimental results on Sallen-Key band-pass filter circuit show that this method can reflect the essential characteristics of fault response signal and improve the performance of fault diagnosis effectively.

Key words: soft fault diagnosis of analog circuit, feature extraction, wavelet packet energy spectrum, statistic feature in time domain, kernel linear discriminant analysis, directed acyclic graph support vector machine