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Application of adaptive learning networks for the detection of failing power system components.
IEEE Trans Ind Appl 1989 Nov/Dec; 25(6):986-991
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.
Mining-industry; Mining-equipment; Electrical-equipment; Electrical-systems; Electricity; Equipment-design; Equipment-reliability; Machine-operation
Gerald T. Homce, Pittsburgh Research Center, Bureau of Mines, U.S. Department of the Interior, Cochran Mill Road, P.O. Box 18070, Pittsburgh, PA, 15236-0070
OP; Journal Article
Issue of Publication
IEEE Transactions on Industry Applications
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division