ARULE™ – Adaptive Remaining Useful Life Estimator™

Ridgetop’s Adaptive Remaining Useful Life Estimator (ARULE) is a powerful reasoner to determine the remaining useful life (RUL) and state of health (SoH) of complex systems. Working from acquired sensor data, ARULE employs an advanced prediction method related to extended Kalman filtering (EKF) to produce new RUL and SoH estimates for each new sensor data point.

ARULE is versatile and can be used for determining electronic and mechanical fatigue damage. The reasoner calculates fault-to-failure progression (FFP) signatures, accurate RUL (time-to-failure) estimates, and SoH estimates, which provide an early warning indicator for system maintenance personnel to schedule service to the system prior to catastrophic failure.


Remaining useful life (RUL) representation of a system with ARULE API deployed in a Sentinel IT™ application

ARULE relies on diagnostic sensor data and a predefined model to produce an RUL estimate. It requires a sensor to “sense” data that are above a predefined “good-as-new” floor and below a “failed” ceiling. A new RUL estimate is produced based on changes to the model space; additionally, the new RUL estimate is used to produce a new SoH estimate.

ARULE is part of Ridgetop’s prognostics and health management (PHM) family of tools called Sentinel Suite™. In particular, ARULE is an integral part of Sentinel Power™, Sentinel Motion™ and Sentinel IT™, for advanced diagnostics and prognostics for power systems, rotational/vibrating systems, and networks, respectively.


  • Power systems
  • Battery management systems
  • Actuator control systems
  • Industrial automation systems

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IEEE and AutoTestCon conference presentations on this subject can be found on the respective conference pages.