Distributed Rocket Engine Testing Health Monitoring System

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,988.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
NNX09CF48P
Agency Tracking Number:
085376
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
American GNC Corporation
888 Easy Street, Simi Valley, CA, 93065
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
611466855
Principal Investigator:
Tasso Politopoulos
Principal Investigator
(805) 582-0582
tpolito@americangnc.com
Business Contact:
Lina Greenberg
Business Official
(805) 582-0582
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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