Machine Learning Based Predictive Maintenance Framework for Rotating Machinery - A Case Study Using Vibration and Temperature Data

Predictive maintenance is essential for ensuring the reliability and efficiency of mechanical systems, particularly in industries where unexpected equipment failures can cause costly downtimes. Traditional approaches are often reactive or based on fixed schedules and lack real-time insights into system health. This study presents an interdisciplinary framework that integrates mechanical system diagnostics with machine learning to implement predictive maintenance. Sensor data, such as vibration and temperature, which are key indicators of mechanical condition, are analyzed using supervised learning algorithms including Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). These models are trained to classify equipment states and predict potential failures. The proposed method demonstrates that combining mechanical domain knowledge with computational intelligence enables early fault detection and improves maintenance planning. The results highlight the effectiveness of this hybrid approach in delivering scalable, data-driven solutions for condition-based monitoring, emphasizing the synergy between mechanical engineering and computer science in solving realworld industrial challenges.