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Machine Learning for Analog Circuit Faults by d22e703 is a document available to read on EtoBox.

The project report presents a machine learning-based approach for fault analysis in analog circuits, specifically focusing on an RC phase shift oscillator. By utilizing the K-Nearest Neighbors (K-NN) algorithm, the model effectively distinguishes between normal and faulty conditions based on sine wave outputs, identifying specific faulty resistors. The findings indicate that this method enhances fault detection accuracy and supports real-time monitoring, ultimately improving the maintenance of electronic sy

Author

d22e703

Language

EN