关键词: 模拟电路, 主成分分析, 极限学习机器, 故障诊断

Abstract: For analog circuit Prognostics and Health Management （PHM） applications, a fault diagnosis method combining Principal Component Analysis （PCA） and Extreme Learning Machine （ELM） is discussed. The fault samples are obtained based on Sallen-Key bandpass filter design failure mode, and the fault feature extraction is performed by PCA. It trains the ELM by fault samples to obtain fault diagnosis model. Simulation results show that the method has a high recognition rate and robustness, with the value of research and application in engineering practice.

Key words: analog circuit, Principal Component Analysis（PCA）, Extreme Learning Machine（ELM）, fault diagnosis