Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Abstract

This paper presented a comprehensive investigation into the detection of electrical abnormalities in a 3-phase alternating current (AC) induction motor (IM) rated at 1.5 kW under simulated single phasing and overloading test conditions. The findings from data analysis on electrical abnormalities simulated physically on an IM were reported, employing a non-invasive technique. Sensors were deployed to monitor the IM and measurements were taken. The data logging and control were designed using an industrial-grade graphical system design software, LabVIEW, and NI PXIe-1071 embedded controller hardware. A novel combination of in-situ on-line current measurements, infrared temperature detection, and 3-axes micro-electro-mechanical systems (MEMS) accelerometers were utilized for measurements. The paper described an internet of things (IoT) monitoring system capable of alerting the operator of the motor to any electrical abnormalities. Experimental results suggested that an IM subjected to single phasing experienced observable z-plane vibration, with a standard deviation of 0.24 G. For overload tests (i.e., 50 Hz and 30 Hz) on the IM based on the national electrical manufacturers association (NEMA) standard, the percentage of heating ranged between 103.98% and 109.67%, respectively; however, no significant increase in z-plane vibrations was observed.