Condition-based Maintenance (CBM+) Tools for Critical Applications

Saving Costs with Ridgetop’s Condition-based Maintenance (CBM) and Sustainment


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

Complex equipment requires maximum up-time, and Ridgetop provides the CBM+ technologies to adjust maintenance actions based on physical condition of individual assets and subsystems instead of relying on an arbitrary, and possibly less than effective number of hours.

Ridgetop’s deployment of more than 750 instruments — comprising of more than 90 customers presently — include our Sentinel Network prognostics analysis platform, which is a web application that combines network monitoring and PHM technologies, predicting the RUL of network assets such as power supply devices (UPS) and battery backups. In addition, we excel at providing prognostic-enabling of military power supplies, prognostic-enabling of  electromechanical actuator (EMA) components, prognostic-enabling of electrohydrostatic actuator (EHA) components and integration framework; State-of-Health (SoH) algorithms; and remaining useful life (RUL) algorithms such as ARULE — our Adaptive Remaining Useful Life Estimator  that works from acquired sensor data, employing advanced prediction method related to extended Kalman filtering (EKF) to produce new RUL and SoH estimates for each new sensor point.

The Benefits from Condition-Based Maintenance (CBM)

A good way to look at the benefits of prognostics is to review the figure below.

Figure 1. CBM – also referred to as integrated vehicle health management (IVHM) and PHM

CBM – also referred to as integrated vehicle health management (IVHM) and PHM

Identical systems may age and degrade differently in the field, depending on whether they have been stressed and subtle differences in their original manufacture. CBM is designed to allow the system to tell you its health, rather than basing your maintenance events solely on a schedule derived from statistics or an idealized model.

Ridgetop’s work in Condition-based Maintenance using condition-based data (CBD) for system maintenance and sustainment operations has enveloped a variety of prognostic analysis platforms. Our cumulative developments, one of which resulted from a Model-based Analysis and Prognostic Reasoner (MAPR) which was developed for a NASA-sponsored SBIR program, extend to Industrial Internet and proprietary bus applications.

Our motivations to provide PHM derived from CBD comprises of many factors that benefit the customer directly:

  1. Prognostics provides advanced warning of impending failure conditions on critical systems and avoidance of expensive system downtime.
  2. Electrical evidence of degradation is the basis for maintenance on the component or system — NOT and arbitrary time table.
  3. PHM can reduce support costs
  4. An Automatic Logistics Information System (ALIS) can be established, placing spare parts and provisions where needed.

How Ridgetop Measures Condition-based Data

Ridegtop’s predictive diagnostics incorporate sensors (existing or additive), data collection routines, and algorithms that provide SoH or RUL of the system under observation. We understand the varying characteristics of degradation that can effect a complex system and reduce overall life-cycle cost of the system by incorporating our predictive capabilities to schedule maintenance when the system is taken off the line.

We understand that systems can assume a multilevel hierarchy spanning at least five levels: 1)Integrated circuit (IC) (or die); 2) components; 3)boards; 4)module/assembly; 5) system and system-of-systems. We can resolve challenges posed on any of these levels through our comprehensive PHM solutions, as seen in an architectural block diagram of a test bed with executable PHM software below.


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