Ridgetop’s Adaptive Remaining Useful Life Estimator (ARULE™) is a powerful Prognostic Predictive Analytics Kernel to determine the Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH) of complex systems.
ARULE™ is versatile and can be used for determining Electrical, Mechanical, and even Electro-Mechanical fatigue damage. ARULE™ relies on Condition-based RotoSense™ or custom sensor data and employs an advanced prediction method related to Extended Kalman Filtering (EKF) to produce new RUL, SoH, and PH estimates for each sensor data point. It requires a sensor to report Feature Data (FD) that are above a predefined “good-as-new” floor and below a “failed” ceiling. A new RUL, SoH, and PH estimate is produced after each data point and has been proven to have a fast convergence rate to the true time to system failure.
ARULE™ Interactive Graphical User Interface (GUI)
ARULE™ offers an intuitive GUI that allows users to upload and process Condition-based Data (CBD) streams for a target system or subsystem to be monitored for Condition-based Maintenance (CBM), Prognostic Health Management (PHM), or Integrated Vehicle Health Management (IVHM) applications. The software platform relies on user-specified definition files and key parameters to process the CBD set with the ARULE™ Prognostic Kernel. Once the CBD files and parameters are entered into the program, ARULE™ will output key prognostic estimates and data plots for RUL, SoH, and PH. The image shown below provides a visual representation of the GUI, and the corresponding data outputs after processing the Loss of Filtering Capacitance in a PC Power Supply System.
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 an arbitrary number of hours. Ridgetop’s approach to CBM, PHM, and IVHM has been to modularize common technology building blocks across a series of different frameworks as shown below.
ARULE™ is an intrinsic component of Ridgetop’s Sentinel Suite™ solution, which encompasses sensors, reasoners, and application software modules. Sentinel Power™, Sentinel Motion™, and Sentinel IT™ implement ARULE™ as a reasoner to provide accurate prognostic estimates for RUL, SoH, and PH for a wide variety of applications and use cases listed below.
- Power Supply Systems
- Battery Management Systems
- Gearbox & Power Transmission Systems
- Actuator Control Systems
- Industrial Automation Systems
Click here to contact us for a Software Evaluation Trial, or have a Ridgetop Group ARULE™ Applications Engineer reach out by filling in the contact form at the bottom of this page.
Ridgetop's PHM Reference Book
Prognostics and Health Management: A Practical Approach to Improving System Reliability Using Condition Based Data
by: Goodman, D. Hofmeister, J, and Szidorovszky, F.
Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics.
The authors include Doug Goodman, CEO and Ridgetop Group founder, James Hofmeister, Distinguished Engineer, and Ferenc Szidarovszky, PhD, a renowned authority in Algorithm design.