AI-Based Fault Detection in Electrical Engineering Research

Design a complete AI fault-diagnosis workflow with datasets, features, model selection, metrics, and interpretation.

AI-based fault detection is useful for motors, power systems, converters, transformers, batteries, and smart-grid monitoring.

A strong workflow begins with fault-case generation, feature extraction, train-test split, classifier design, and confusion-matrix analysis.

For a better research contribution, compare classical machine learning with deep learning and add robustness testing under noise or varying load conditions.

Recommended Research Workflow

• Define the research gap before selecting tools.
• Connect every simulation output to a measurable research objective.
• Keep figures, code, and documentation reproducible for review.
• Prepare thesis-ready explanations for graphs and tables.