When deploying predictive maintenance (PdM) AI at the edge, several key constraints and considerations come into play, primarily due to the limited resources and specific operational environments of edge devices. These constraints often dictate the choice of AI models, deployment strategies, and overall system architecture:

Deploying predictive maintenance AI at the edge presents several key constraints. First, limited computational resources on edge devices restrict the complexity of AI models that can be run locally. Second, power and energy efficiency are critical, especially in remote or mobile environments. Third, storage constraints may limit the amount of sensor or historical data retained for real-time processing. Fourth, connectivity limitations can prevent constant cloud synchronization, making real-time local inference essential. Fifth, model update and deployment at the edge can be challenging, requiring secure and efficient over-the-air updates. Sixth, data privacy and security must be ensured as sensitive operational data remains on-site. Lastly, hardware diversity at the edge requires adaptable AI models compatible across various platforms.

Predictive maintenance in Electric Arc Furnace (EAF) steel plants using AI/ML involves collecting sensor data from equipment (e.g., electrodes, transformers), applying anomaly detection and failure prediction algorithms to prevent downtime, and integrating models with plant control systems for real-time intervention and optimization.