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Application of adaptive learning networks for the detection of failing power system components.

Authors
Homce-GT
Source
IEEE Trans Ind Appl 1989 Nov/Dec; 25(6):986-991
NIOSHTIC No.
10007518
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 Bureau of Mines is examining the use of mathematical models to aid in this evaluation by creating polynomial networks called 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. Models developed thus far can process readily measured terminal information and quantify deterioration power and current to within 3 pct of motor full-load values.
Keywords
Mining-industry; Mining-equipment; Electrical-equipment; Electrical-systems; Electricity; Equipment-design; Equipment-reliability; Machine-operation
CODEN
ITIACR
Publication Date
19891101
Document Type
OP; Journal Article
Fiscal Year
1990
NTIS Accession No.
NTIS Price
Identifying No.
OP 42-90
Issue of Publication
6
ISSN
0093-9994
NIOSH Division
PRC
Source Name
IEEE Transactions on Industry Applications
State
PA
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