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

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,970.00
Award Year:
2001
Program:
SBIR
Phase:
Phase I
Contract:
F33615-02-M-5000
Agency Tracking Number:
012ML-0348
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
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
073955507
Principal Investigator:
Carl Byington
Manager of R&D
(814) 861-6273
carl.byington@impact-tek.com
Business Contact:
Mark Redding
President
(716) 424-1990
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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