Model Updating in Online Aircraft Prognosis Systems

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNA06BA09C
Agency Tracking Number: 053247
Amount: $69,498.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2005
Solicitation Topic Code: A1.04
Solicitation Number: N/A
Small Business Information
Sentient Corporation
850 Energy Drive, Idaho Falls, ID, 83401-1563
DUNS: 089822014
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Sean Marble
 Principal Investigator
 (208) 522-8560
 smarble@sentientscience.com
Business Contact
 Sean Marble
Title: Business Official
Phone: (208) 522-8560
Email: smarble@sentientscience.com
Research Institution
N/A
Abstract
Diagnostic and prognostic algorithms for many aircraft subsystems are steadily maturing. Unfortunately there is little experience integrating these technologies into a complete and practical on-board prognosis system, and integration often proceeds in an ad-hoc manner. Sentient Corporation proposes to develop a general-purpose architecture and set of reusable algorithms for integrating diagnostics and predictive models into an efficient and highly accurate prognostic system. The architecture is based on a flexible and powerful model updating algorithm that provides optimal fusion of diagnostics with model-based state indications and minimization of uncertainty in remaining life predictions. This project will focus on development of several key features of that algorithm, including automatic recognition of a failure that is not progressing according to the physical model, and practical considerations for on-board use such as minimizing computational and memory requirements. By the end of Phase II, Sentient will demonstrate a working prototype of an on-board prognostic system developed using the proposed architecture and tools. This demonstration will use diagnostic and model algorithms developed under the DARPA Prognosis Program, and will be compared to a large set of fault data for turbine engine and subscale bearings.

* information listed above is at the time of submission.

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