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

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-05-C-0247
Agency Tracking Number: N051-024-0326
Amount: $80,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2005
Solicitation Year: 2005
Solicitation Topic Code: N05-024
Solicitation Number: 2005.1
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Ravindra Patankar
 Senior Research Scientist
 (301) 294-5248
Business Contact
 Mark James
Title: Contracts and Proposals Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
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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