Real-Time Maintenance Optimization with Industrial Internet of Things

Abstract

Featured Application: The models and methods outlined in the article are highly applicable in the development of maintenance processes for industrial systems with high maintenance demands (paper industry, food and beverage production, compressor systems). Through the proposed IIoT solution and the associated real-time optimization, the system’s productivity and the revenue derived from it can be significantly increased. Efficient maintenance management is critical to ensuring the reliability and productivity of industrial systems. This article explores how the Industrial Internet of Things (IIoT) enables real-time maintenance optimization through data-driven decision-making. IIoT technologies, such as connected smart sensors and predictive analytics, provide continuous monitoring of equipment performance and state. Within the frame of this article, a novel mathematical model is proposed to support the real-time optimization of maintenance operations in production systems. The model makes this possible by using real-time state information to optimize maintenance operations, minimize maintenance costs, and maximize the efficiency of the production system. The results highlight the potential of IIoT to transform conventional maintenance strategies into dynamic, adaptive systems. This research contributes to advancing smart maintenance solutions for modern industrial applications.