Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,999.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N68335-10-C-0441
Agency Tracking Number:
N10A-008-0586
Solicitation Year:
2010
Solicitation Topic Code:
N10A-T008
Solicitation Number:
2010.A
Small Business Information
Frontier Technology, Inc.
75 Aero Camino, Suite A, Goleta, CA, 93117
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
153927827
Principal Investigator:
Gary Key
Principal Investigator
(937) 429-3302
gkey@fti-net.com
Business Contact:
Rhonda Adawi
Contracts Manager
(805) 685-6672
radawi@fti-net.com
Research Institution:
Northeastern University
Ibrahim Zeid
360 Huntington Ave
Boston, MA, 2115
(617) 373-3817
Nonprofit college or university
Abstract
Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used to detect and predict the stall. Vector Quantization and transfer function models will be used to create the models that estimate engine current conditions. These conditions and in-situ sensor readings are provided to a Neural Network (NN) to predict the occurrence of a stall. Engine data will be provided from GE Aviation will be sued perform both the vector quantization and to train the NN model. The research team has extensive experience working with engine data to detect and diagnose faults and to predict impact on engine performance. Northeastern University has performed a GE-sponsored project to predict engine stalls and other fault events that is closely related to the proposed technology. This effort extends FTI’s research into engine failure detection and prediction analysis which has been performed in support of the US Navy and US Air Force.

* information listed above is at the time of submission.

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