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Application of Adaptive Learning Networks for the Detection of Failing Power System Components.

Authors
Homce-GT
Source
Proc the 9th WVU Int'l Mining Electrotechnology Conf 1988 :13 pages
Link
NIOSHTIC No.
10006522
Abstract
A system capable of monitoring mine electrical power systems and detecting component failure in early stages could significantly improve power system safety and availability. Such monitoring would require a method of evaluating electrical features, calculated from terminal values, for indications of component deterioration. Research conducted by the U.S. Bureau of Mines is examining the use of mathematical models to aid in this evaluation, by creating polynomial networks or adaptive learning networks that can indicate deteriorated conditions in cable-connected motor systems. This process uses laboratory "training" data to select the electrical features most significant for accurately modeling cable-motor system conditions, and forms mathematical expressions relating these features to the presence and severity of deterioration. Adaptive learning networks developed thus far are capable of correctly identifying deterioration levels to within 1 pct of motor full-load current.
Publication Date
19880101
Document Type
OP;
Fiscal Year
1988
NTIS Accession No.
NTIS Price
Identifying No.
OP 45-89
NIOSH Division
PRC;
Source Name
Proc. the 9th WVU Int'l Mining Electrotechnology Conf., 1988, PP. 210-222
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