Real-time Sensor Data Streaming for deployment in Edge AI for Health Index Construction and Remaining Useful Life Prediction

Abstract

This paper introduces a real-time predictive analytics framework that integrates edge artificial intelligence for remaining useful life estimation and health index construction using turbofan engine sensor data. A MATLAB/Simulink model was designed to stream 14 critical sensor signals, derived from the NASA C-MAPSS dataset, into an Opal-RT OP5707XG simulator for real-time emulation. These signals were output as analog voltages, converted into digital values using ADS1115 converters, and processed on an Nvidia Jetson AGX Orin edge-computing platform. A CatBoost regressor, trained on a feature-rich time-series dataset and refined through SHapley Additive Explanations-based feature selection was employed as the predictive model. System performance was benchmarked on two hardware platforms: a mid-tier desktop computer and the Jetson AGX Orin. The mid-tier desktop computer completed training in 18 minutes, while the Jetson required around 3 hours. Inference speed was also faster on the computer at 2.8 ms versus 7.5 ms, though both satisfied the 33 ms requirement for real-time processing of a 30 Hz data stream. The Jetson demonstrated a significant efficiency advantage, consuming 20—40 W compared to 250-350 W for the computer. The framework achieved high accuracy with strong generalization and transparent explainability through SHapley Additive Explanations-based feature selection confirming the feasibility of deploying advanced prognostics on edge AI hardware for real-time health monitoring.