Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor

We have developed an analog circuit fault diagnostic system based on Bayesi an neural networks using wavelet transform, normalization and principal com ponent analysis as preprocessors. Our proposed system uses these preprocess ing techniques to extract optimal features from the output(s) of an analog circuit. These features are then used to train and test a neural network to identify faulty components using Bayesian learning of network weights. For sample circuits simulated using SPICE, our neural network can correctly cl assify faulty components with 96% accuracy.