Authors: Dr. Anil Rajput, Prof. Swati Nair
Abstract: With the increasing complexity of electrical circuits, traditional fault detection methods have become inefficient in identifying and rectifying failures. This paper presents a machine learning-based approach for intelligent fault diagnosis in electrical circuits. Various algorithms, including decision trees, support vector machines, and deep neural networks, are employed to classify and predict circuit faults. The research evaluates the effectiveness of these techniques through experimental data and real-time fault detection scenarios. The findings demonstrate that machine learning models significantly enhance diagnostic accuracy, reduce downtime, and improve system reliability.
Keywords: Fault Diagnosis, Machine Learning, Neural Networks, Electrical Circuit Analysis, Predictive Maintenance
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