Ridgetop’s Adaptive Remaining Useful Life Estimator (ARULE) is a powerful prognostic reasoner to determine the Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH) of complex systems. Working from acquired sensor data, ARULE employs an advanced prediction method related to extended Kalman filtering (EKF) to produce new RUL, SoH, and PH estimates for each new sensor data point. ARULE is versatile and can be used for determining electrical, mechanical, and even electro-mechanical fatigue damage. The reasoner calculates fault-to-failure progression (FFP) signatures, and the accurate estimates for SoH, PH, and RUL (time-to-failure) provide an early warning indicator for maintenance personnel to schedule service prior to catastrophic system failure.
Interactive Graphical User Interface
ARULE offers an intuitive graphical user interface (GUI) that allows a user to upload and process Condition-based Data (CBD) streams for a target system or subsystem to be monitored for CBM, PHM, or IVHM applications. The software platform relies on user specified definition files and key parameters to process the CBD set with the ARULE predictive analytic engine. Once the CBD files and parameters are entered in the program, ARULE will output key prognostic estimates and data plots for RUL, SoH, and PH. The following image provides a visual of the GUI, and the corresponding data outputs after processing the loss of filtering capacitance in a PC-power supply system:
ARULE is a key component in Ridgetop’s Sentinel Suite™ family of products. In particular, ARULE is an integral part of Sentinel Power™, Sentinel Motion™ and Sentinel IT™, for advanced diagnostics and prognostics.
Ridgetop’s approach to Condition-Based Maintenance (CBM), Prognostic Health Management (PHM), and Integrated Vehicle Health Management (IVHM) has been to modularize common technology building building blocks across a series of different frameworks as shown below:
A framework is a conceptual structure that can be realized in any number of ways for a diverse set of CBM, PHM, and IVHM applications. For many applications, a common practice is to prognostic enable sensor data streams to determine the RUL, SoH, and PH of a target system or subsystem. Using this approach, systems and subsystems can be replaced based on actual evidence of degradation, as opposed to using using an arbitrary number of hours.
- Power supply systems
- Battery management systems
- Transmission gearbox systems
- Actuator control systems
- Industrial automation systems
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PHM Reference Book Written Ridgetop Group
- “Prognostics and Health Management: A Practical Approach to Improving System Reliability Using Condition Based data”, Goodman, D. Hofmeister, J, and Szidorovszky, F. Published by Wiley.
- Available on Amazon.com.
Selected White Papers
- Adaptive Remaining Useful Life Estimator (ARULE™)
- Practical Application of PHM-Prognostics to COTS Power Converters
- A Model-based Avionic Prognostic Reasoner (MAPR)
- Prognostic-Enabling of an Electrohydrostatic Actuator (EHA) System
- Ridgetop Group Develops Fast and Accurate Prognostic Algorithm to Reduce Costs of Aviation Electronics Maintenance
Related Webinars & Videos
- ARULE Webinar May 2022
- RGI Overview with ARULE Demonstration
- Prognostic Health Monitoring Webinar April 2020
- ARULE (Adaptive Remaining Useful Life Estimator) – ATTF (Advanced Time-to-Failure) to Diagnose and Predict System Health