In this paper, we investigate and systematically evaluate two machine learning algorithms for analog fault detection and isolation: (1) restricted Coloumb energy (RCE) neural network, and (2) learning vector quantization (LVQ). The RCE and LVQ models excel at recognition and classification types of problems. In order to evaluate the efficacy of the two learning algorithms, we have developed a software tool, termed Virtual Test-Bench (VTB), which generates diagnostic information for analog circuits represented by SPICE descriptions. The RCE and LVQ models render themselves more naturally to online monitoring, where measurement data from various sensors is continuously available. The effectiveness of RCE and LVQ is demonstrated on illustrative example circuits.
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