A COMPREHENSIVE FRAMEWORK FOR IOT-DRIVEN PREDICTIVE MAINTENANCE: LEVERAGING AI AND EDGE COMPUTING FOR ENHANCED EQUIPMENT RELIABILITY

Abstract

The convergence of the Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing has advanced predictive maintenance (PdM). The main two benefits of this integration are to enable real-time monitoring and proactive equipment management across industries. This paper presents a comprehensive framework for IoT-driven PdM, using AI-powered analytics and Edge Computing to enhance equipment reliability, reduce operational downtime, and optimize maintenance costs. Based on a comprehensive study of the previous work, we proposed a framework that integrates six key steps to use IoT, AI, and edge computing in preventive maintenance. The steps are IoT sensors and devices for data acquisition, Edge and cloud computing for efficient processing, AI-driven predictive analytics for fault detection, automated decision-making and alert systems, remote monitoring and automated control, and continuous learning for system optimization. The paper discussed the advantages of the proposed approach, such as reduced costs, and improved instrument utilization. However, challenges such as cybersecurity concerns, integration complexities, and computational resource requirements are also presented. A case study involving the implementation of an IoT-based PdM system for water tank trucks in a Civil Defense Directorate demonstrates the effectiveness of the proposed framework in real-world applications. Results show that real-time data analytics and predictive modeling improve problem detection accuracy, enabling prompt intervention and minimizing expensive mechanical breakdowns. This study proposes a systematic approach to AI-enabled PdM adoption, enabling scalable and cost-effective industrial maintenance strategy optimization.