Abstract :

Predictive maintenance is an emerging approach in industrial environments to minimize equipment failures and reduce maintenance costs by predicting breakdowns before they occur. This work focuses on developing a predictive maintenance framework using Python-based machine learning techniques to analyze sensor data and equipment behavior. By leveraging real-time monitoring and feature-driven analytics, the system predicts failures with improved accuracy while optimizing operational reliability. Various algorithms like Random Forest, SVM, and LSTM are utilized for equipment health prediction. The system provides automated alerts for maintenance scheduling, preventing unexpected downtime. This framework significantly enhances productivity, extends equipment life, and reduces operational disruptions. Experimental results demonstrate effective performance and highlight the feasibility of Python as a powerful predictive analytics tool.