Machine Learning Algorithms for Fault Diagnosis in Analog
نویسندگان
چکیده
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 classiication types of problems. In order to evaluate the eecacy 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 on-line monitoring, where measurement data from various sensors is continuously available. The eeectiveness of RCE and LVQ is demonstrated on illustrative example circuits.
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