Fault diagnosis method of non-linear analog circuits based on Volterra series and SVM

Author(s):  
Huafeng He ◽  
Yicheng Chen ◽  
Zheng Yang ◽  
Huixuan Deng
2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.


2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.


Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 714-722 ◽  
Author(s):  
Michał Tadeusiewicz ◽  
Stanisław Hałgas

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