Abstract
This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.
Highlights
Technical diagnostic is a process in which one checks the technical condition of an object and, from that, a decision is made about continuing to use it or subjecting it to a repair process enabling further use
The presented fault detection system containing a diagnostic procedure based on a genetic algorithm allowing for calculation of the matrices resulting from the population individuals’ values changes and objective function value changes can be applied in identification tests of various cases of inter-turn short circuit
The identification process was performed using the parameters varying in specified ranges defined for the samples of diagnostic signals
Summary
Technical diagnostic is a process in which one checks the technical condition of an object and, from that, a decision is made about continuing to use it or subjecting it to a repair process enabling further use. Mechanical, strongly nonlinear elements such as elastic-damper parts, bearing faults, or clearances occurring in elements have a strong influence on the operation of electromechanical systems in energy conversion In such complex objects, analysis of diagnostics signals in the time-frequency domain and their classification is possible due to the application of transform methods. The fourth section contains conclusions on the application of the genetic algorithm in induction motor fault identification
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