Condition-Based Machinery Maintenance

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$350,000.00
Award Year:
1993
Program:
SBIR
Phase:
Phase II
Contract:
n/a
Award Id:
18655
Agency Tracking Number:
18655
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Executive Place Iii 50 Mall, Rd, Burlington, MA, 01803
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
James C. Deckert
(617) 273-3388
Business Contact:
() -
Research Institution:
n/a
Abstract
Three new techniques have been developed over the past 36 months that offer significant potential for on-board monitoring of mechanical systems to detect and classify incipient failures. The first is the wavelet transform, and its extension to wave packets, as a means for isolating changes in signal structure in terms of scale as well as time. The second is an extension of classical time-series analysis to multiresolution representations of a time signal, with algorithms to estimate characteristic functions of the spectrum of the signal at each resolution. The third is the maturation of artificial neural network technology to the point where it may be reliably considered as an element of an on-board diagnostic system. This effort combines all three into a comprehensive approach to incipient failure detection and classification. The front-end of our proposed classifier extracts novel feature sets offered by the wavelet transform and the multiresolution tLme series spectral estimators. These define a feature space on which a classical neural net classifier may be trained and evaluated.

* information listed above is at the time of submission.

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