Wavelet neural network based fault diagnosis in nonlinear analog circuits

1 .INTRODUCTIONWith recent advancement in digital signal process-ing and wireless communication, nonlinear analogintegrated circuits , such as logarithmic amplifierand power amplifier and PLL,are being used ag-gressivelyinindustry . Nevertheless ,fault diagno-sis continues to be the bottleneckin reducing ti me-to-market of these ICs . Since the methods whichwere used to diagnose linear analog circuits oftencan’t direaly be used in nonlinear analog circuitsand building nonlinear model is…

Wavelet neural network based fault diagnosis in nonlinear analog circuits

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.