Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor

We have developed an analog-circuit fault diagnostic system based on backpropagation neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. The proposed system has the capability to detect and identify faulty components in an analog electronic circuit by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically reduces the number of inputs to the neural network, simplifying its architecture and minimizing its training and processing time. The second preprocessing by principal component analysis can further reduce the dimensionality of the input space and/or select input features that minimize diagnostic errors. Input normalization removes large dynamic variances over one or more dimensions in input space, which tend to obscure the relevant data fed to the neural network. A comparison of our work with that of Spina and Upadhyaya (see ibid., vol. 44, p. 188-196, 1997), which also employs backpropagation neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.