A Study on Health Indicator for Machine-Independent Electric Motor Fault Diagnosis

Abstract

Electric motors are widely used in industry applications such as manufacturing lines, high-speed trains, and electric vehicles. Especially, motor failures can lead to human, fㅁmulation model and artificial fault seeded testbed with different fault severities. Second, the health indicator is designed based on the characteristic of the winding inter-turn short faults and the crest factor. Finally, to evaluate the feasibility of the proposed health indicator, a case study with measured electric current signals is presented. This study shows the potential of machine-independent fault diagnosis for electric motors with different power capacities.