Title : HALP system: An edge-AI and two-tier architecture for cost-effective structural health monitoring in extreme industrial environments
Abstract:
Structural Health Monitoring (SHM) in high-risk industrial environments, such as oil refineries, is critically hindered by extreme temperatures (500°C+) and corrosive atmospheres that cause conventional sensor electronics to fail precisely when needed most. This paper presents the HALP (Hybrid Articulated Low Profile) System, a novel two-tier architecture designed to bridge the gap between high-end industrial safety and cost-effective commercial viability. The Industrial-Grade tier utilizes 316L stainless steel and Aerogel thermal insulation, while the Commercial-Grade tier employs cost-effective materials (ABS/FRP) supported by dynamic software calibration. The system leverages Edge Machine Learning (XGBoost and Autoencoders) for real-time structural classification, achieving 97% accuracy in damage detection and 93% accuracy in anomaly detection. Furthermore, a 5-year simulation under worst-case weather conditions demonstrates high energy autonomy (89.74% battery health) via a triple-harvesting energy matrix (wind, solar, and thermal). Comprehensive hardware simulations validated the system's sub second Emergency Shutdown (ESD) and Haptic Alert protocols. By reducing material and sensor costs by up to 80% for the commercial tier, HALP offers a scalable, economically viable solution for preventing catastrophic structural failures across diverse industrial sectors.
