An Affordable Health and Usage Monitoring System (HUMS) for UAVs

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,919.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
W911W6-04-C-0016
Award Id:
68153
Agency Tracking Number:
A032-0720
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
125 Tech Park Drive, Rochester, NY, 14623
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
073955507
Principal Investigator:
MichaelRoemer
Director of Engineering
(585) 424-1990
mike.roemer@impact-tek.com
Business Contact:
MarkRedding
President
(585) 424-1990
mark.redding@impact-tek.com
Research Institute:
n/a
Abstract
Impact Technologies, in collaboration with Lord Corporation and the Georgia Institute of Technology, propose to develop an affordable/miniaturized health and usage monitoring system (HUMS) for unmanned aerial vehicles (UAVs). Based upon the unique requirements of UAVs, an innovative hardware/software solution will be developed and demonstrated that includes embedding advanced vehicle health management algorithms for anomaly detection, fault classification and prediction into a low-cost, aerospace-certified digital signal processing (DSP) based hardware platform. All critical parameters related to UAV performance and mechanical health will be monitored including aero-thermal flight parameters, propulsion system information, structural/vibrations and vehicle subsystem performance data to address condition-based maintenance activities that are required to ensure UAV mission success. Key UAV subsystems including the gas turbine engine, drive train (shaft/clutch), fan, louvers, actuators (fight control) and avionics will be monitored by the proposed system. The proposed UAV-HUMS will continuously assess and track the health of all critical UAV subsystems using a combination of anomaly detection and diagnostic reasoning software that can communicate the health status of the vehicle through a low-bandwidth air-to-ground link. The anomaly detection software will implement proven signal processing and automatic signal feature extraction techniques that are capable of identifying abnormal system behavior and reporting it to the diagnostic reasoning software. The diagnostic reasoning modules will utilize a combination of fuzzy reasoning and robust fault classifiers such as neural networks and support vector machines to obtain the fault diagnosis of the UAV. The development and integration of advanced diagnostic and prognostic algorithms into a real-time health monitoring system is expected to improve UAV mission success rates and positively impact total ownership costs. The proposed Phase I development includes a hardware/software demonstration of the next generation UAV-HUMS including embedded anomaly detection and diagnostic reasoning algorithms, DSP hardware specification and an on-board/off-board design trade study. This work will leverage Impact's existing expertise in air vehicle prognostic health management systems, Lord's expertise in flight-qualified electronics hardware and Georgia Tech's helicopter UAV test bed.

* information listed above is at the time of submission.

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