Real-Time Predictive Maintenance via Edge AI

Abstract

Predictive maintenance has become an important strategy for industries aiming to reduce downtime, maintenance cost, and equipment failure. Traditional cloud-based monitoring systems often suffer from latency, bandwidth consumption, and privacy issues when dealing with continuous sensor data from industrial equipment. With the emergence of Edge Artificial Intelligence (Edge AI), it is now possible to process data near the source, enabling real-time analytics and decision-making. This paper presents a comprehensive review of real-time predictive maintenance using Edge AI technologies. It discusses architecture models, machine learning techniques, deployment strategies, communication protocols, and challenges in implementing Edge AI for predictive maintenance. The paper also highlights the advantages of Edge AI over cloud-centric approaches in terms of latency, reliability, and security. Several case studies and application scenarios from manufacturing, energy, transportation, and smart industries are analyzed. Finally, future directions and research opportunities are discussed.

KEYWORDS: Predictive Maintenance, Edge AI, IoT, Real-Time Monitoring, Machine Learning, Industrial IoT, Fault Detection