Abstract：In order to increase the speed and improve the accuracy of soft fault diagnosis in tolerance analog circuits, a new soft fault diagnosis approach, which is based on Randomized algorithms (RAs), sensitivity analysis, immune genetic algorithms (IGAs) and neural networks, is proposed. First, the proposed RAs based sensitivity analysis method allows for removing the difficulties in the selections of input stimuli frequencies and the most suitable test nodes for faulty circuits. Then, the system uses the selected stimuli to excite the circuit, samples its outputs and preprocesses them by principal component analysis (PCA) and normalization to generate optimal features for training the neural network. In order to overcome the shortcomings that back propagation (BP) algorithms suffer from the problem of getting stuck at local minima, the IGAs are introduced to optimize the BP neural networks (BPNNs) and IGA-BPNNs based fault diagnosis system is formed. The diagnosis principles and steps are described. Finally, the reliability of the method is shown by a practical example.
祝文姬, 何怡刚. 容差模拟电路软故障诊断的神经网络方法[J]. 电工技术学报, 2009, 24(11): 184-191.
Zhu Wenji, He Yigang. Neural Network Based Soft Fault Diagnosis of Analog Circuits With Tolerances. Transactions of China Electrotechnical Society, 2009, 24(11): 184-191.
2009, Vol. 24 
