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Agent-Based Health Monitoring System

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNA05AA05C
Agency Tracking Number: 033835
Amount: $600,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: F2.02
Solicitation Number: N/A
Timeline
Solicitation Year: 2003
Award Year: 2005
Award Start Date (Proposal Award Date): 2004-11-23
Award End Date (Contract End Date): 2006-11-22
Small Business Information
15400 Calhoun Drive, Suite 400
Rockville, MD 20855-2785
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Leonard Haynes
 Principal Investigator
 (301) 294-5250
 lhaynes@i-a-i.com
Business Contact
 Mark James
Title: Contracts and Proposal Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
N/A
Abstract

We propose combination of software intelligent agents to achieve decentralized reasoning, with fault detection and diagnosis using PCA, neural nets, and maximum entropy methods. The goal of the work is to achieve integrated system health management and self-reliant systems, including integration with the maintenance and logistics scheduling systems to achieve fully automated end-to-end solutions. At low levels the agents will evaluate raw sensor signals to detect and diagnose the cause of anomalies. At the next higher level, the agents will combine the diagnostic results from multiple lower level agents to detect and diagnose anomalies in the interaction between components or subsystems. If there is a maintenance action or a spare part indicated by the prognosis, a Task Agent and/or a Spare Parts agent will be spawned to interact with the appropriate agent-based Scheduling System to insure that the requirements are met. Agents at each level are also responsible for performing graceful degradation in the event of a failure at their level. At the low level, we have demonstrated that the PCA algorithm can greatly reduce the amount of diagnostic data that must be shared between hierarchical levels. We have also demonstrated other algorithms for anomaly detection, diagnosis, and diagnostic data-fusion.

* Information listed above is at the time of submission. *

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