Machinery Diagnostics Using Polynomial Neural Networks

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N/A
Agency Tracking Number: 18646
Amount: $250,000.00
Phase: Phase II
Program: SBIR
Awards Year: 1993
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
Route 1, Box 159, Stanardsville, VA, 22973
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. S. Eugene Parker, Phd
 (804) 985-4400
Business Contact
Phone: () -
Research Institution
The vibration signatures (mechanical, acoustic, and electromagnetic) produced by machine components may be used for machinery diagnostics. By regularly measuring vibration levels, defects can be detected and diagnosed before causing extensive damage or failure, a process known as predictive maintenance. The main advantage of predictive maintenance is that problems can be identified without disassembling a machine, or even removing it from service. Conventional machinery diagnostics generally require significant human involvement and expertise. Essential requirements include data pre-processing for feature extraction, detection of signals of interest, and classification of these signals. Automated systems often utilize deductive approaches that fail to capitalize on the benefits offered by inductive techniques such as neural networks; these benefits include performance advantages and reduced development and maintenance. Machine diagnostics is essentially a pattern- recognition task, for which neural networks are ideally suited due to their speed and ability to recognize complex high-dimensional relationships. Classification polynomial neural networks emphasize discrimination among fault classes and thereby offer advantages over estimation neural networks for diagnostics applications. Signal-processing and pattern-recognition algorithms, and the hardware on which they run, are sufficiently advanced for rapid on-line detection and classification of changes in the condition of electro-mechanical systems. -

* 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
Environmental Protection Agency logo
National Aeronautics and Space Administration logo
National Science Foundation logo
US Flag An Official Website of the United States Government