Exploring Proactive Maintenance through Fault Detection Techniques for Rotating Machinery

Rotating machinery plays a crucial role in industrial production, where reliability and efficiency are essential for minimizing downtime and operational losses. This systematic review explores proactive maintenance through advanced failure detection techniques, assessing their effectiveness in optimizing maintenance strategies. The study identifies key fault detection methods, including vibration analysis, electrical signature analysis, thermal imaging, acoustic emission monitoring, oil analysis, and IoT-enabled real-time monitoring. While vibration analysis remains the most widely researched and applied method, emerging AI-driven predictive maintenance models and IoT-based real-time diagnostics are increasingly transforming industrial maintenance practices. Findings indicate that proactive maintenance enhances equipment reliability, reduces downtime, and improves safety. However, significant challenges hinder widespread adoption, including high implementation costs, data management complexities, and the need for specialized expertise. Additionally, research gaps persist in comparative evaluations of fault detection methods, cost-benefit analyses, and the standardization of key performance metrics for proactive maintenance effectiveness. The study emphasizes the need for integrating multiple fault detection techniques to improve accuracy and reliability.