Ridgetop Group’s Advanced Diagnostic and Prognostics Division

Ridgetop Group continues to be the leader in Prognostic Health Management and Advanced Diagnostic Detection, ensuring precise identification and isolation of system anomalies integral for successful aerospace and defense objectives. Understanding root causes of failure for ICs, cabling, backplanes, boards, subsystems and systems has allowed us to continue developing solutions to the challenging measurement tasks needed to answer the challenges associated with failure and prediction.

Ridgetop relies upon evidence of degradation through its background in physics-of-failure technologies. By extracting a distinct signature that precedes a failure, a meaningful and accurate indication of impending failure can be determined. Other issues of prognostics involve on-board vs. off-board implementations, and linkages with the Autonomic Logistics Information System (ALIS) to optimize the placement of spare parts in key areas to support deployed equipment.

A Prognostics Development Walkthrough

Prognostics development process

The following process should be followed when designing and implementing a prognostics-enabled system:

• Key failures are detected and ranked.
• Precursor signatures or multivariate inputs are extracted using prognostics & health management (PHM) software tool such as Sentinel Suite™.
• Historic and trending data are used to fine-tune the analysis.
• Prognostic metrics are generated, such as current state-of-health (SoH) and remaining useful life (RUL).

A Diagnostic Solution

Our innovative diagnostic framework of knowledge, and applied expertise, includes the development of electro-mechanical focused software technology that apply advanced diagnostic and prognostic algorithms to legacy radar systems, controls and vehicles operated under harsh or off-design conditions. We have a unique, collaborative design portfolio of IP, specializing in services and products whereby predictive reliability, diagnosis and prognostics software creates value-added results for products ranging from integrated circuits to complex system design that benefit an array of commercial and military aerospace applications.

Our diagnostic solutions extend throughout a number of applications including heavy industrial dual-use applications, diagnosis through profiling air temperature probes on aerospace vehicles, monitoring of rotational, vibrational and temperature parameters in X-Y-Z axis, photonics-based detection systems that measure stress and strain (relaying more accurate results than traditional strain gauge-based applications) and detection technology for determining open and shorts on cables quickly, aiding in the data logging or complex cable harnesses. Most recently, we have developed a Lithium-Ion battery monitoring system for aerospace applications, providing much needed capacity control for customers.

Ridgetop offers a number of sensors and sensor enabled measurement packages for applications based on our Sentinel Suite IoT-enabled products:

• SmartProbe4000 — Used to diagnose problems and profile air temperature probes on aerospace vehicles.
• RotoSense — A Microelectromechanical Sensor (MEMS)-based, NASA derived technology that can be inserted into the helicopter mechanical drive train, as well as rolling stock (trains) applications and other heavy equipment like tanks, sensing system failure before it occurs.
• CPT1000 — Our portable tester derived from Navy technology that detects open and shorts on cables quickly, aiding in data logging of complex cable harnesses.
• RidgeGator — Our Fiber Bragg Grating (FBG) Sensor photonics-based system that detects stress and strain and is easier to deploy and more accurate than traditional strain gauge-based technologies.

Our Prognostic Health Management Superiority

Ridgetop’s work in Prognostics and Diagnostics continues to build upon the advanced tools provided to prognostic-enable critical electronic modules and chips. Ridgetop has introduced innovative detection technologies to transmit monitored fault signatures, such as our Sentinel IoT Wireless Module, for collection and processing on our Sentinel Suite Platform. With the built-in flexibility to mix, match, and configure, we provide our customers with the state-of-the-art equipment needed to calculate State-of-Health and remaining useful life estimates.

The body of knowledge and applied expertise we maintain has resulted in significant technologies that predict impending failure, reduce troublesome ‘No Fault Found’ (NFF), costly ‘Could Not Duplicate’ (CND), or ‘NoTrouble Found’ (NTF) maintenance codes in fielded systems that reduce the life-cycle cost or critical systems. Based on extracted degradation signatures, these solutions can be applied to ICs using nanoscale, In-situ monitors that employ advanced eigenvalue extraction methods; and finally to the system level using algorithmic approaches.

A Continued Solution

Ridgetop’s Prognostic Health Management (PHM) systems address many requirements by providing appropriate support and solutions with an expert staff who specialize in the field of multivariate data; data-conditioning of Condition-based Data (CBD) — data fusion, data and domain transformation, and noise filtering and mitigation; and cost-maintenance and supply.

Ridgetop excels in providing its customers with advanced diagnostic and prognostic solutions for critical applications. Ridgetop relies upon evidence of degradation through its background in physics-of-failure technologies. By extracting a distinct signature that precedes a failure, a meaningful and accurate indication of impending failure can be determined. Other issues of prognostics involve on-board vs. off-board implementations, and linkages with the Autonomic Logistics Information System (ALIS) to optimize the placement of spare parts in key areas to support deployed equipment.

PHM and Support Ecosystem

The Ridgetop tools can also be linked into a holistic ecosystem, as shown below.

PHM Value Proposition

For more information on specific products, please contact us directly or click the following links: