Early Detection of Insulation Failure in Electric Motors

摘要

A system capable of monitoring mine electrical power systems and detecting impending component failure could significantly improve power system safety and reduce unscheduled equipment downtime. Such monitoring would require a method of evaluating electrical parameters, calculated from terminal values, for indications of component deterioration. The U.S. Bureau of Mines has targeted electrical failure of squirrel cage induction motors and examined the use of mathematical models to aid in the evaluation. The initial stage of the work is complete, and has produced polynomial networks called adaptive learning networks (ALN's) that can detect and quantify winding insulation leakage simulated on laboratory motors. In the modeling process, empirical data from laboratory motors were used to select the electrical parameters most significant for assessing motor conditions, and mathematical expressions relating these parameters to simulated deterioration were formed.