Condition monitoring of Induction motors through Simulation of Bearing Fault and Air Gap Eccentricity Fault

Abstract

Induction motors have an important role in the industry on account of their advantages over other electrical motors. Consequently, there is a huge demand for their safe and sound operation. But it is not free from failures, which result in unnecessary downtimes and create great losses with regards to both revenue and maintenance. For that reason, early fault detection is considered necessary for the safety maintenance of the motor. In the present circumstances, the health monitoring of the induction motors are progressively increasing due to its potential to enhance operating costs, increase the reliability of function and so does the current paper emerge. Also, this paper deals with a novel effective technique for detecting the bearing fault and air gap eccentricity fault of the induction motor. Summarization and analysis of the findings are done based on percentage error and fitness function Value. Comparison results of bad bearing faults and air gap eccentricity are given separately in the paper. The findings of the study concluded that particle swarm optimization (PSO) can be considered as better optimization for bad bearing fault whereas modified particle swarm optimization is concluded as better optimization for air gap eccentricity fault.