Journal of Systems Engineering and Electronics ›› 2006, Vol. 17 ›› Issue (3): 521-526.doi: 10.1016/S1004-4132(06)60089-3

• ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

Wavelet neural network based fault diagnosis in nonlinear analog circuits

Yin Shirong, Chen Guangju & Xie Yongle

  1. School of Automation Engineering, Univ. of Electronic Science and Technology of China, Chengdu 610054, P. R. China
  • Online:2006-09-25 Published:2006-09-25


The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The
fault dictionary of nonlinear circuits is constructed based on improved back-propagation (BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification
and deducibility.

Key words: