scholarly journals Research on ELM Soft Fault Diagnosis of Analog Circuit Based on KSLPP Feature Extraction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 92517-92527 ◽  
Author(s):  
Gan Xu-Sheng ◽  
Qu Hong ◽  
Meng Xiang-Wei ◽  
Wang Chun-Lan ◽  
Zhu Jie
2010 ◽  
Vol 17 (3) ◽  
Author(s):  
Wei Zhang ◽  
Longfu Zhou ◽  
Yibing Shi ◽  
Chengti Huang ◽  
Yanjun Li

2013 ◽  
Vol 307 ◽  
pp. 327-330
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu

Because of the diversity and complexity of soft fault in analog circuit, the rapid and accurate diagnosis is very difficult. For this, an adaptive BP wavelet neural network diagnosis method of soft fault is proposed. It combines the time-frequency localization characteristics of wavelet and the self-learning ability of neural network in soft fault diagnosis of analog circuit, and by introducing the adaptive learning rate the diagnosis ability of BP wavelet neural network model can effectively be improved. In addition, PSPICE software is used to obtain the simulation data of actual analog circuit for the experiment. The results also verify the validity of the proposed method.


2017 ◽  
Vol 50 ◽  
pp. 252-259 ◽  
Author(s):  
Gan Xu-sheng ◽  
Gao Wen-ming ◽  
Dai Zhe ◽  
Liu Wei-dong

2012 ◽  
Vol 19 (4) ◽  
pp. 817-830 ◽  
Author(s):  
Yongcai Ao ◽  
Yibing Shi ◽  
Wei Zhang ◽  
Xifeng Li

Abstract While the Slope Fault Model method can solve the soft-fault diagnosis problem in linear analog circuit effectively, the challenging tolerance problem is still unsolved. In this paper, a proposed Normal Quotient Distribution approach was combined with the Slope Fault Model to handle the tolerances problem in soft-fault diagnosis for analog circuit. Firstly, the principle of the Slope Fault Model is presented, and the huge computation of traditional Slope Fault Characteristic set was reduced greatly by the elimination of superfluous features. Several typical tolerance handling methods on the ground of the Slope Fault Model were compared. Then, the approximating distribution function of the Slope Fault Characteristic was deduced and sufficient conditions were given to improve the approximation accuracy. The monotonous and continuous mapping between Normal Quotient Distribution and standard normal distribution was proved. Thus the estimation formulas about the ranges of the Slope Fault Characteristic were deduced. After that, a new test-nodes selection algorithm based on the reduced Slope Fault Characteristic ranges set was designed. Finally, two numerical experiments were done to illustrate the proposed approach and demonstrate its effectiveness.


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