关键词:模拟电路; 故障诊断; 小波变换; 模糊C均值算法; 分层决策
Analog circuit fault identification approach based on wavelet analysis and hierarchical decision
SONG Guo-ming1,2, WANG Hou-jun1, JIANG Shu-yan1, LIU Hong1,3
(1. School of Automation Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China; 2. Dept. of Computer Engineering, Chengdu Electromechanical College, Chengdu 610031, China; 3. School of Computer Science & Technology, Changchun University of Science & Technology, Changchun 130022, China)
Abstract:Aiming at overlapped recognition on analog circuit fault diagnosis with large number of fault categories, this paper presented a fault identification approach based on wavelet analysis and hierarchical decision. Firstly, extracted two types of fault features of circuit under test by using wavelet transform. Then processed clustering analysis for fault feature data sets by fuzzy C-mean algorithm, which separated fault sub-classes in form of decision tree. Partitioned the fault sub-classes maximally by optimizing the feature selection on each tree node. Finally, constructed a hierarchical fault decision system by combining multiple classifiers according to the structure of decision tree. Chose support vector machines and neural networks as classifiers for tree nodes to validate the proposed method and improved the fault identification accuracy effectively. The experimental results on a high-pass filter are higher than 99%, which is better than classical support vector machine methods.......