Abstract

Mining mobile machinery in non-stationary operations faces high levels of wear and unpredictable stress, posing significant challenges for predictive maintenance (PdM). This paper introduces a hierarchical inference network for PdM consisting on edge sensor devices, gateways, and cloud services for real-time condition monitoring. The system dynamically can adjusts inference locations - on-device, on-gateway, or on-cloud - based on trade-offs between real-time demands and conditions such as accuracy, latency, and battery range. The edge-based architecture enables rapid decision-making directly on-sensor or on-gateway, achieving classification accuracies above 90% while reducing latency up to 30% and power consumption on sensor nodes by approximately 45% regarding the cloud inference mode. This is critical to ensure machinery uptime in remote, rugged environments. The use of Tiny-Machine-Learning (TinyML) optimization approaches allow optimal accuracy and model compression for efficient deployment of deep learning models on IoT edge devices with limited hardware resources. The ESN-PdM hierarchical framework offers a scalable and adaptive solution for reliable condition monitoring and anomaly detection, contributing to advancing technology in PdM frameworks for real-world industrial applications.