Techniques and Models to Enhance RUL Prognostics and Fault Detection in Mechanical Systems

Award Information
Agency:
Department of Defense
Amount:
$69,993.00
Program:
SBIR
Contract:
N68335-03-C-0053
Solitcitation Year:
N/A
Solicitation Number:
N/A
Branch:
Navy
Award Year:
2002
Phase:
Phase I
Agency Tracking Number:
N022-0871
Solicitation Topic Code:
N/A
Small Business Information
Impact Technologies, Llc
125 Tech Park Drive, Rochester, NY, 14623
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
073955507
Principal Investigator
 Gregory Kacprzynski
 Project Manager
 (585) 424-1990
 greg.kacprzynski@impact-tek.com
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Email: mark.redding@impact-tek.com
Research Institution
N/A
Abstract
"Impact Technologies proposes to develop a suite of Statistical Influence Models (SIMs) and fusion techniques for enhancing physics-based prognostic models and calibrating them in the presence of various forms of fault detection or state awareness inaircraft mechanical systems. While the robustness and accuracy of physics-based Remaining Useful Life (RUL) Prognostic models and incipient fault detection tools have been improving, the improvements have thus far been largely independent of each other.Through state-of-the-art knowledge fusion of various Statistical Influence Models (SIMs) focused on usage profiles, manufacturing defects, random damage events, build tolerances, material condition and inspection capability, the integration of stateawareness and predictive prognostics promises to be significantly improved.The enhancement capabilities of the statistical models and fusion techniques will be demonstrated with simulations in a Prognostics Testbench focused on the STOVL lift fan transmission on the F-35 aircraft. The Prognostics Testbench architecture will besuch that generic bearing, shaft and clutch prognostic models will be simulated with pre-defined usage profiles. Statistical Influence Models (SIMs) addressing fault detection updates, damage and defect likelihoods, and manufacturing and maintenanceinduced conditions will be "plugged" into the Testbench to investigate their influence on the RUL predictions and associated c

* information listed above is at the time of submission.

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