Early Detection of Failure Precursors Using Symbolic Dynamics, Neural Networks, and Principal Component Analysis

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$80,000.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
N68335-05-C-0247
Award Id:
75094
Agency Tracking Number:
N051-024-0326
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
161911532
Principal Investigator:
RavindraPatankar
Senior Research Scientist
(301) 294-5248
rpatankar@i-a-i.com
Business Contact:
MarkJames
Contracts and Proposals Manager
(301) 294-5221
mjames@i-a-i.com
Research Institute:
n/a
Abstract
For early detection and monitoring of failure precursors in mechanical transmission couplings, we propose to develop signal processing capabilities that can map patterns in accelerometer data to an anomaly measure. Toward this end, Professor Asok Ray at Penn-State University has pioneered an elaborate mathematical theory based on symbolic time series analysis (STSA), statistical mechanics, and information theory. An anomaly detection algorithm is formulated by applying this novel STSA theory to create a robust statistical pattern recognition technique. For anomaly detection, this STSA technique has been shown to be superior to conventional pattern recognition techniques, such as artificial neural networks (ANN) and principal component analysis (PCA) because it exploits a common physical fact underling most anomalies which conventional techniques do not. This superiority has recently been demonstrated on electrical circuits, fatigue testing machines, and mechanical components undergoing fatigue due to vibrations. The research objectives are: (i) to develop a coupling model where gradually evolving damage phenomena can be introduced, (ii) to formulate and compare real-time algorithms for early detection and monitoring of failure precursors in model simulations based on three principal techniques and their variants - STSA, ANN, and PCA, and (iii) to demonstrate these algorithms on fatigue damage accumulating parts of a vibrating machine experiment.

* information listed above is at the time of submission.

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