TECHNOLOGY 84 FEED & ADDITIVE MAGAZINE July 2025 WHY AI-DRIVEN PREDICTIVE MAINTENANCE FOR FEED MILLS IS A GAME-CHANGER Dr. Dejan Miladinovic Head of Research Centre for Feed Technology Norwegian University of Life Sciences Predictive Maintenance for Feed Mills leverages Artificial Intelligence and Internet of Things to move beyond reactive repairs, enabling early fault detection, improved energy efficiency, and optimized machine performance. With autonomous systems on the horizon, these technologies offer real-time insights and strategic decisionmaking. But how can feed mills begin implementation while overcoming integration and environmental challenges? In today’s rapidly evolving feed manufacturing industry, where operational efficiency and sustainability are essential, digital transformation is reshaping how facilities manage their assets. At the forefront of this shift is predictive maintenance for feed mills, a forward-looking strategy powered by artificial intelligence (AI) and the Internet of Things (IoT). This approach goes beyond traditional maintenance by enabling feed mills to anticipate issues, optimize performance, and build more resilient operations. SMARTER MONITORING FOR COMPLEX EQUIPMENT Feed mills operate a wide range of machinery such as conveyors, pumps, hammer mills, mixers, pellet presses, extruders, and expanders, all of which are subject to continuous mechanical stress. Historically, maintenance was reactive, with teams responding only after equipment failed. Today, embedded sensors collect real-time data on temperature, vibration, and electrical activity. AI algorithms analyze this data to detect subtle deviations like increased motor heat or irregular shaft movement, that may indicate early-stage faults. This allows maintenance teams to act proactively, reducing downtime and maintaining consistent production output. BOOSTING ENERGY EFFICIENCY IN PELLETING AND EXTRUSION Pelleting, extrusion, and expanding are among the most energy-intensive processes in feed production. Wear or misalignment in components such as rollers, dies, or screw elements can lead to inefficiencies that are difficult to detect manually. AI-powered analytics continuously monitor equipment performance, identifying patterns that suggest energy waste or mechanical imbalance. When anomalies are detected, the system can recommend timely interventions such as recalibration or part replacement helping to reduce energy consumption, extend equipment life, and lower operational costs.
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