In-Line Health Monitoring System for Aircraft Hydraulic Pumps & Motors

Award Information
Agency: Department of Defense
Branch: N/A
Contract: F33615-02-M-5000
Agency Tracking Number: 012ML-0348
Amount: $99,970.00
Phase: Phase I
Program: SBIR
Awards Year: 2001
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
DUNS: 073955507
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Carl Byington
 Manager of R&D
 (814) 861-6273
 carl.byington@impact-tek.com
Business Contact
 Mark Redding
Title: President
Phone: (716) 424-1990
Email: mark.redding@impact-tek.com
Research Institution
N/A
Abstract
Impact Technologies proposes to develop and demonstrate a monitoring system that assesses the health of aircraft hydraulic pumps and motors. The approach described herein includes performance models, feature level fusion, and adaptive modeling forestimating degradation through the collection of in-line pump data and onboard processing. This model-based and feature fusion approach will significantly improve the remaining life predictions over what is possible using single parameter trending. Theappropriate performance and degradation models will be developed within a probabilistic framework that inherently captures distributions in the data due to random processes and measurement error. Moreover, the failure probability framework will directlyidentify confidence bounds associated with specific component failure modes progression. By providing continuous, on-line updates/adjustments of the critical parameters used by the fatigue/damage models based on system level measurements, more accuratefailure rate predictions can be made throughout the life of the component. Impact Technologies proposes to develop and demonstrate a monitoring system that assesses the health of aircraft hydraulic pumps and motors. The approach described herein includesperformance models, feature level fusion, and adaptive modeling for estimating degradation through the collection of in-line pump data and onboard processing. This model-based and feature fusion approach will significantly improve the remaining lifepredictions over what is possible using single parameter trending. The appropriate performance and degradation models will be developed within a probabilistic framework that inherently captures distributions in the data due to random processes andmeasurement error. Moreover, the failure probability framework will directly identify confidence bounds associated with specific component failure modes progression. By providing continuous, on-line updates/adjustments of the critical parameters used bythe fatigue/damage models based on system level measurements, more accurate failure rate predictions can be made throughout the life of the component.

* information listed above is at the time of submission.

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