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: N69335-03-C-0042
Agency Tracking Number: N022-0989
Amount: $69,967.00
Phase: Phase I
Program: SBIR
Awards Year: 2002
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: 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
Principal Investigator
 Rolf Orsagh
 Project Manager
 (585) 424-1990
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Email: mark.redding@impact-tek.com
Research Institution
"In response to SBIR topic N02-195, Impact Technologies, in cooperation with RLW, propose to develop and demonstrate a multi-disciplinary prognostic and health management (PHM) approach specifically designed for real-time, automated remaining useful lifeassessments of propulsion system and drive train bearings. The proposed approach integrates collaborative diagnostic and prognostic (D&P) technologies from different engineering disciplines including statistical reliability modeling, physics-basedrotordynamic models, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Intelligent fusion of complementary data/information sources such as oil temperature trends, oildebris and condition information, and vibration features will be used to provide robust assessments of the bearing health state. In addition to detecting incipient bearing faults, the complementary diagnostic techniques provide an essential foundation forprognostics by distinguishing between potential failure modes. Based on the failure mode discrimination results, intelligent prognostic algorithms can be selected based on the specific bearing failure mode progression model needed. As with diagnostics,the prognostic approach will utilize knowledge from a variety of engineering disciplines including statistical progression rate models, component rolling contact fatigue models and sensed data ana

* information listed above is at the time of submission.

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