Fault Diagnosis of Analog Circuits Using Bayesian Neural Networks with Wavelet Transform as Preprocessor |
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Authors: | Farzan Aminian Mehran Aminian |
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Affiliation: | (1) Trinity University, 715 Stadium Dr., San Antonio, TX, 78212, USA.;(2) One Camino Santa Maria, St. Mary's University, San Antonio, TX, 78228, USA. |
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Abstract: | We have developed an analog circuit fault diagnostic system based on Bayesian neural networks using wavelet transform, normalization and principal component analysis as preprocessors. Our proposed system uses these preprocessing 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 classify faulty components with 96% accuracy. |
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Keywords: | analog fault diagnosis neural networks analog circuits Bayesiasn learning |
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