Fault diagnosis of nonlinear analog circuits using neural networks with wavelet and Fourier transforms as preprocessors

A neural-network based analog fault diagnostic system is developed for nonl inear circuits. This system uses wavelet and Fourier transforms, normalizat ion and principal component analysis as preprocessors to extract an optimal number of features from the circuit node voltages. These features are then used to train a neural network to diagnose soft and hard faulty components in nonlinear circuits. Our neural network architecture has as many outputs as there are fault classes where these outputs estimate the probabilities that input features belong to different fault classes. Application of this system to two sample circuits using SPICE simulations shows its capability to correctly classify soft and hard faulty components in 95% of the test da ta. The accuracy of our proposed system on test data to diagnose a circuit as faulty or fault-free, without identifying the fault classes, is 99%. Bec ause of poor diagnostic accuracy of backpropagation neural networks reporte d in the literature (Yu et al., Electron. Lett., Vol. 30, 1994), it has bee n suggested that such an architecture is not suitable for analog fault diag nosis (Yang et al., IEEE Trans. on CAD, Vol. 19, 2000). The results of the work presented here clearly do not support this claim and indicate this arc hitecture can provide a robust fault diagnostic system.