Investigation of bearing faults in three phase induction motor using wavelet de-noising with improved Wiener filtering

Abstract

Bearing fault diagnosis in an induction motor, especially at nascent stage has become inevitable and captious to avoid unexpected shut down of the industrial process. Many researchers have concentrated on various monitoring techniques including vibration, temperature, chemical and current monitoring. In this paper, an improved bearing fault detection using motor current signature analysis (MCSA) has been presented. In the proposed work, the bearing fault signature is extracted from stator current using improved Wiener filter cancellation. Performance of Wiener filter is improved using two stage process. The side band effects of filter is removed using Kaiser window and the higher order noise due to filtering process is removed with wavelet de-noising technique. Different categories of bearing failures are examined with and without de-nosing using pre-fault component cancellation (noise cancellation). Moreover, fault indexing based on standard deviation (SD) and energy (E) value of noise canceled stator current is proposed. The proposed bearing fault detection topology is examined using simulations and experiments on a 2HP induction motor under different load condition.