ARULE, or the Adaptive Remaining Useful Life Estimator, is a sophisticated predictive analytics engine developed by Ridgetop Group for real-time health estimation of complex systems. It leverages adaptive Kalman filtering and dynamic stress modeling to deliver accurate predictions of Remaining Useful Life (RUL), State of Health (SoH), and Prognostic Horizon (PH) across diverse domains including aerospace and defense.
The platform is built around a robust two-stage Adaptive Prognostic Kernel (APK), which converts raw sensor data into actionable insights. ARULE's processing sequence—Feature Data (FD) to Fault-to-Failure Progression (FFP) to Degradation Progression Signatures (DPS) to Functional Failure Signatures (FFS)—ensures high accuracy in prognostic convergence. Its modular structure supports near-real-time RUL estimation, even in high-noise environments.
ARULE adheres to the IEEE 1856-2017 PHM standard, making it suitable for integration into software, firmware, or hardware contexts. Its open API supports seamless embedding into diverse platforms, such as Systems-on-Chip (SoC), enabling health-aware embedded systems to transition towards evidence-based maintenance strategies.
