ARULE, or Adaptive Remaining Useful Life Estimator, is an essential predictive tool for prognostic health management of complex systems. By estimating the Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH), ARULE assists in predicting when maintenance should occur, minimizing downtime and optimizing service schedules. The system operates using condition-based data, applying Extended Kalman Filtering to enhance the accuracy of its predictions. As conditions evolve, ARULE dynamically adjusts its predictions, providing highly accurate results in near real-time. ARULE is particularly useful in various applications, including power supply and battery management systems, actuator control, and industrial automation systems. Its intuitive graphical user interface simplifies the process of integrating and analyzing condition-based data. This versatility makes it a valuable component of Ridgetop’s Sentinel Suite, offering comprehensive insights into systems' operational health. By anticipating system failures, ARULE helps reduce wasteful maintenance practices and improve overall reliability and efficiency in operational settings.
