ARTICLE 84 FEED & ADDITIVE MAGAZINE December 2025 “The shift toward intelligent operations marks the final evolution of manufacturing excellence. By embracing Predictive Maintenance, Machine Learning, and Agentic AI today, leaders are doing more than simply reducing costs; they are investing in the core predictability and resilience of their business for years to come. This data-driven foresight transforms the hidden risks of component failure into manageable events, guaranteeing maximum uptime and operational efficiency.” The unexpected failure of a critical asset is the single greatest threat to margins and supply chain stability in the modern milling operation. When a component—say, a bearing in a key conveyor or a high-wear part in an extruder—fails, the ripple effect is immediate chaos and a loss of production control. The true competitive edge today belongs to the leader who can turn this operational threat into a source of predictable performance by mastering Predictive Maintenance (PreMa). PreMa is no longer optional; it is the definitive strategy for moving operations from reactive firefighting to proactive industrial foresight. It centers on deploying AI-driven intelligence across the entire facility, ensuring that every asset, from the primary hammer mills to essential handling equipment, is continuously monitored and understood. THE UNIVERSAL AI: BEYOND CRITICAL ASSETS The challenge of downtime isn't isolated to just the most expensive machinery. Simple bottlenecks on a conveyor belt or a failure in an industrial motor can halt an entire line. The new standard for PreMa demands universal coverage, achieved through integrated, smart sensor technology that tracks: • Mechanical Stress: Real-time analysis of vibration and heat (temperature spikes) in rolling elements (bearings) and gearboxes to detect minor anomalies that precede catastrophic failure. • Operational Load: Monitoring of motor and driver run-hours to precisely calculate component fatigue and accurately forecast remaining useful life. • Process Flow: Tracking process variables to ensure the overall system operates within normal parameters, defined by a constant stream of learning data. This diagnostic capability relies on sophisticated Machine Learning (ML) models that calculate a Compound Anomaly Index (CAI), learning the "normal" state of every asset and flagging deviations the moment they appear. PREDICTIVE MAINTENANCE: HOW AI IS ELIMINATING DOWNTIME IN THE FEEDMILL André Magrini Chief Revenue Officer OGI Systems
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