Vibration-Driven Predictive Maintenance of Rotating Equipment Using Machine Learning

Rotating machinery is fundamental to a wide range of industrial applications, making its reliability and performance critical for operational efficiency. This research focuses on condition monitoring of such machinery through vibration analysis integrated with predictive maintenance strategies powered by machine learning. Traditional maintenance approaches often rely on scheduled inspections or reactive responses to failures, leading to increased downtime and operational costs. In contrast, this study explores the implementation of advanced vibration signal processing techniques combined with machine learning algorithms to enable early fault detection and prognosis. Key features are extracted from time-domain and frequency-domain vibration data, which are then used to train supervised and unsupervised learning models for anomaly detection, classification of fault types, and prediction of remaining useful life (RUL). The proposed approach demonstrates significant improvements in accuracy and timeliness of fault detection compared to conventional methods. Results indicate that integrating intelligent datadriven techniques with vibration-based monitoring systems can enhance the reliability, safety, and cost-effectiveness of maintenance practices for rotating machinery. This research contributes toward developing robust, scalable predictive maintenance frameworks essential for modern Industry 4.0 environments.