Distributed Rocket Engine Testing Health Monitoring System

Award Information
Agency:
National Aeronautics and Space Administration
Amount:
$99,988.00
Program:
SBIR
Contract:
NNX09CF48P
Solitcitation Year:
N/A
Solicitation Number:
N/A
Branch:
N/A
Award Year:
2009
Phase:
Phase I
Agency Tracking Number:
085376
Solicitation Topic Code:
N/A
Small Business Information
American GNC Corporation
888 Easy Street, Simi Valley, CA, 93065
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
611466855
Principal Investigator
 Tasso Politopoulos
 Principal Investigator
 (805) 582-0582
 tpolito@americangnc.com
Business Contact
 Lina Greenberg
Title: Business Official
Phone: (805) 582-0582
Email: lgreenberg@americangnc.com
Research Institution
N/A
Abstract
The on-ground and Distributed Rocket Engine Testing Health Monitoring System (DiRETHMS) provides a system architecture and software tools for performing diagnostics and prognostics for supporting NASA's Integrated System Health Management (ISHM) capability for rocket engine testing and ground operations. DiRETHMS architecture consists of a hierarchical, modular, scalable, and flexible system structure for performing ISHM. A core version of the system will be demonstrated during the Phase I effort by performing diagnostics of auxiliary components in rocket engines. The building blocks of the DiRETHMS are: (a) Advanced Embedded Smart Sensors (AESS); (b) Health Monitoring Nodes (HMN), (c) Health Manager Unit (HMaU), and (d) Application Server with Man Machine Interface Man Machine Interface (AS-MMI). DiRETHMS architecture will provide a logic organization for embedding diagnostics at the following levels: (1) smart sensors based on UNCU; ( (3) Robust monitoring/diagnosis subsystem; and (4) system level Prognosis. The significant innovations of this project are: (1) Capability to provide the user with an integrated awareness about the condition of every element in the system, (2) Very flexible architecture of smart sensors that comply with state of the art standards for easy integration and customization, (3) System configuration for support root-cause analysis, and (4) Object-Oriented Bayesian Network for Uncertain Inference.

* information listed above is at the time of submission.

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