关键词: 主成分分析, 极限学习机, 容差, 特征提取, 故障诊断

Abstract: To improve the integrality of extraction of analog circuit fault diagnosis feature information, realize the failure mode classification accurately, complete the network training and testing rapidly, this paper presents a new analog circuit fault diagnosis method based on Principal Component Analysis （PCA）and Extreme Learning Machine （ELM）. The MonteCarlo sample data in each state of the circuit are got by setting parameters and tolerances components in OrCAD16.3 circuit simulation. The optimal characteristic pattern feature information is extracted by reduction dimensions using PCA. Then, the faults of the circuit are classified by ELM. As an example, the Sallen-Key band pass filter circuit is studied. The simulation results show that the proposed method has the merits of good feature extraction, fast neural network training, efficient fault diagnosis, excellent generalization performance.

Key words: Principal Component Analysis（PCA）, Extreme Learning Machine（ELM）, tolerance, feature extraction, fault diagnosis