Analog circuit fault identification approach based on wavelet analysis and hierarchical decision
基于小波分析和分层决策的模拟电路故障识别方法

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.