摘要：About sixty percent of the power in industries is consumed by induction machines, which implies
induction machines are an integral part of industries. Even though these motors are stalwart and
rugged in construction, they often experiences faults due to long time usage without maintenance.
Bearing damage accounts 40% in the total faults and cause severe damage to the machine if
unnoticed at nascent stage. So these faults should be continuously monitored for efficient
operation, otherwise may cause severe damage to the machine. Conventional vibration
monitoring is difficult due to requirement of high manpower and costly sensors. So motor current
signature analysis (MCSA) is widely used for detection and localization of these faults. In this
paper, the bearing faults are estimated by means of current frequency spectral subtraction using
discrete wavelet transform. In addition to this, the current signature analysis after spectral
subtraction is carried out using Discrete Wavelet Transform (DWT), Stationary Wavelet
Transform (SWT) and Wavelet Packet Decomposition (WPD) and a comparative analysis is
presented to estimate fault severity using statistical parameters. The proposed method is assessed
based on current signatures obtained from a 2.2kW induction machine. The experimental results
acknowledged the effectiveness of proposed method.
关键词：Bearing faults; induction machines; current monitoring; motor current signature analysis;;
spectral subtraction.
