This repository is dedicated to the detection of faults in electric motors through machine learning techniques, specifically utilizing the K-Nearest Neighbors (KNN) algorithm. It contains datasets, Python scripts, and accompanying documentation necessary for implementing and understanding the fault detection process.

Electric Motor Temperature Prediction Using Machine Learning is a project that predicts the operating temperature of an electric motor using sensor data and ML algorithms. It helps in early fault detection, improves motor efficiency, and supports predictive maintenance by preventing overheating and unexpected failures.

A machine learning project for predicting electric motor temperature using sensor data, enabling real-time monitoring, fault detection, and performance optimization.

This project uses Machine Learning to predict the operating temperature of an electric motor based on sensor data such as speed, torque, voltage, and current. By forecasting temperature variations, the system helps in early fault detection and preventive maintenance, reducing the risk of equipment failure and improving operational efficiency.