Open AccessDWT based bearing fault detection in induction motor using noise cancellation
Author(s) -
Kireeti Kompella,
Venu Gopala Rao Mannam,
Srinivasa Rao Rayapudi
Publication year - 2016
Publication title -
journal of electrical systems and information technology
Language(s) - English
Resource type - Journals
ISSN - 2314-7172
DOI - 10.1016/j.jesit.2016.07.002
Subject(s) - wavelet , fault (geology) , noise (video) , bearing (navigation) , stator , discrete wavelet transform , induction motor , signature (topology) , fault detection and isolation , control theory (sociology) , engineering , algorithm , computer science , wavelet transform , pattern recognition (psychology) , mathematics , artificial intelligence , geology , electrical engineering , seismology , geometry , control (management) , voltage , actuator , image (mathematics)
This paper presents an approach to detect the bearing faults experienced by induction machine using motor current signature analysis (MCSA). At the incipient stage of bearing fault, the current signature analysis has shown poor performance due to domination of pre fault components in the stator current. Therefore, in this paper domination of pre fault components is suppressed using noise cancellation by Wiener filter. The spectral analysis is carried out using discrete wavelet transform (DWT). The fault severity is estimated by calculating fault indexing parameter of wavelet coefficients. It is further proposed that, the fault indexing parameter of power spectral density (PSD) based wavelet coefficients gives better results. The proposed method is examined using simulation and experiment on 2.2 kW test bed