Identification of bearing fault in induction motor using random forest algorithm

¹ Assistant Professor (Sr. Gr.), Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India
² UG Student, Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India

^* Corresponding author: krishnaveni.ks@mepcoeng.ac.in

Abstract

In day-to-day life 90% of industries use induction motors due toless maintenance, high efficiency, good Power factor and low cost. Maintenance of the induction motor is important for continuous operation in industries.40-60% of the fault in Induction motors is due to bearing failure. Unexpected bearing failures could cause industries to spend money on repairing and replacing the bearing, along with that other nearby components might damaged. Failure in bearing, decrease the plant’s operating efficiency, increases downtime, raises operating costs and in the worst case, it may cause injuries to workers. The proposed method detects and diagnoses the bearing fault using vibration signals. The fault gets detected by using the Machine learning classifier. The proposed method achieves high accuracy in detecting and diagnosing the bearing fault. The proposed work is implemented using Google Colab (colaboratory) software. The result demonstrates the usefulness of the suggested of strategy enhancing the maintenance of bearing in good condition and safe operation in the induction motor.