AI-ENABLED MECHANICAL HEALTH MONITORING AND FAILURE FORECASTING SYSTEM

Abstract

Failure prediction in mechanical components is essential to enhance reliability, safety, and operational efficiency in industrial systems. Traditionally, maintenance strategies have relied on fixed schedules and rule-based systems, which often lead to unexpected breakdowns or unnecessary part replacements. These conventional methods are limited by their inability to adapt to dynamic operating conditions, process real-time data effectively, or detect early signs of failure. As industrial systems grow in complexity and scale, the need for more accurate and intelligent failure prediction methods has become critical. This project addresses the shortcomings of traditional systems by adopting machine learning-based approaches that can learn from historical and real-time data to identify hidden patterns and predict failures before they occur. The significance of this work lies in its potential to enable proactive maintenance strategies, reduce unplanned downtime, extend the lifespan of components, and optimize overall maintenance efforts. By transitioning from reactive to predictive maintenance, industries can achieve greater cost savings and operational resilience. This study aims to explore the application of machine learning techniques in developing efficient, adaptive models for failure prediction, ultimately contributing to smarter and more dependable industrial systems