Fault-to-Failure Progression Modeling of Propulsion System and Drive Train Bearings for Prognostic and Useful Performance Life Remaining Predictions

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-04-C-0003
Agency Tracking Number: N022-0819
Amount: $1,497,320.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N02-195
Solicitation Number: 2002.2
Solicitation Year: 2002
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-01-13
Award End Date (Contract End Date): 2006-01-13
Small Business Information
2539 Channing Way, Suite 102, Idaho Falls, ID, 83404
DUNS: 089822014
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Sean Marble
 (208) 522-8560
Business Contact
 Sean Marble
Title: President
Phone: (208) 522-8560
Email: smarble@sentientscience.com
Research Institution
Military and commercial aircraft operators are moving toward predictive maintenance to minimize costs while maximizing asset availability and safety. Key technological prerequisites for this new approach are the ability to monitor the condition of critical components, and the ability to accurately forecast their remaining useful life. Phase I of this project clearly demonstrated the feasibility of accurately predicting the remaining life of rolling element bearings. During Phase II, Sentient Corporation will complement in-house test capabilities by teaming with The Timken Company to assemble a comprehensive, high quality database of bearing spall initiation and propagation for tapered roller, angular contact, and radial bearings, while Purdue University will research material and component level phenomena. New understanding of bearing failure processes resulting from this data will be used to further develop the physics-based model created during Phase I. The goal is for the model to represent the physics of failure accurately enough that it will work in virtually any bearing application without modification. This will be demonstrated by evaluating model performance across the entire experimental database. In addition, the model will be enhanced to utilize diagnostic data for refining model predictions and estimating uncertainty.

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

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