AI-Powered Predictive Maintenance in Industrial IoT: A Framework for Reducing Downtime and Enhancing Productivity

Industrial Internet of Things (IIoT) has revolutionized the manufacturing sector by enabling real-time data acquisition, remote monitoring, and intelligent automation. Predictive maintenance, as an integral part of IIoT, utilizes Artificial Intelligence (AI) to foresee equipment failures, thereby minimizing unexpected downtimes and improving operational productivity. This research paper presents a comprehensive framework for implementing AI-powered predictive maintenance in industrial settings. The study investigates machine learning algorithms, data acquisition techniques, and integration protocols to build an efficient predictive system. A case study conducted on a medium-scale manufacturing unit illustrates the system’s effectiveness in detecting potential equipment faults in advance. Furthermore, the paper includes empirical insights from a structured questionnaire survey among industrial stakeholders. The findings support the significant role of AI in reducing maintenance costs, optimizing asset utilization, and enhancing overall productivity