AI-Based Predictive Maintenance Strategies for Enhancing Reliability and Operational Efficiency in Rotating Machinery Systems

Abstract

Rotating machinery plays a vital role in industrial applications such as manufacturing, power generation, and transportation. Equipment failures in these systems often lead to costly downtime, safety risks, and production losses. Artificial Intelligence (AI)-based predictive maintenance (PdM) techniques have emerged as transformative tools to detect incipient faults, predict remaining useful life (RUL), and optimize maintenance schedules. This paper explores AI-based predictive maintenance approaches applied to rotating machinery, emphasizing data-driven diagnostics, machine learning algorithms, and intelligent decision-support frameworks. It also discusses the integration of the Internet of Things (IoT), digital twins, and cloud computing for real-time health monitoring. The study concludes by addressing existing challenges, research gaps, and future directions for achieving sustainable and intelligent maintenance ecosystems.

KEYWORDS: Artificial Intelligence, Predictive Maintenance, Rotating Machinery, Machine Learning, Fault Diagnosis, Condition Monitoring, Digital Twin, IoT