Data Reduction Techniques for Real-time Fault Detection and Diagnosis, and Multiple Fault Inference with Imperfect Tests

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,968.00
Award Year:
2008
Program:
STTR
Phase:
Phase I
Contract:
NNX08CD30P
Award Id:
87874
Agency Tracking Number:
070120
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
100 Great Meadow Road, Suite 603, Wethersfield, CT, 06109
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
808837496
Principal Investigator:
Sudipto Ghoshal
Principal Investigator
(860) 257-8014
sudipto@teamqsi.com
Business Contact:
Sudipto Ghoshal
Business Official
(860) 257-8014
Research Institute:
University of Connecticut

438 Whitney Road Ext., Unit 1133
Storrs, CT, 6269
(860) 486-3994
Nonprofit college or university
Abstract
The recent advances in data collection and storage capabilities have led to information overload in many applications, including on-line monitoring of spacecraft operations with time series data. Such datasets present new challenges in data analysis, especially for implementation in memory-constrained DECUs. Also, the traditional statistical methods break down partly because of the increase in the number of observations (measurements), but mostly due to an increase in the number of variables associated with each observation ("dimension of the data"). One of the problems with high-dimensional datasets is that not all the measured variables are "important" for understanding the underlying phenomena of interest. In addition to the computational cost, irrelevant features may also cause a reduction in the accuracy of some algorithms. The first key issue we propose to address is that of data reduction techniques for onboard implementation of data-driven classification techniques in memory-constrained onboard processing units. Some of the classification techniques we intend to use with the above data-reduction techniques include, support vector machine (SVM), probabilistic neural network (PNN), k-nearest neighbor (KNN), principal component Bayesian analysis (PCA). To improve the diagnostic accuracy and efficiency of the above classifiers, we will apply classifier fusion techniques such as AdaBoost, Error correcting output codes, Voting to find which architecture will enhance the accuracy and under what conditions. Finally we will investigate Dynamic Multiple Fault Diagnosis that can work with imperfect fault/anomaly detection tests. As part of this task, we will develop novel factorial hidden Markov model-based inferencing techniques such as Lagrangian relaxation and Viterbi decoding algorithms to solve this difficult combinatorial optimization problem, for on-board vehicle health monitoring and fault diagnosis.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government