Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

    Authors

    B. M. Ebrahimi; J. Fair; S. Lotfi-Fard;P. Pillay

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Mech. Syst. Signal Proc.

    Keywords

    Fault diagnosis; Induction motors; Time stepping finite element method; Wavelet transforms; FINITE-ELEMENT-METHOD; PACKET DECOMPOSITION; STATOR CURRENT; STEADY-STATE; PERFORMANCE; Engineering, Mechanical

    Abstract

    This paper introduces novel indices for broken rotor bars diagnosis in three-phase induction motors based on wavelet coefficients of stator current in a specific frequency band. These indices enable to diagnose occurrence and determine number of broken bars in different loads precisely. Besides thanks to the suitability of wavelet transform in transient conditions, it is possible to detect the fault during the start-up of the motor. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. Furthermore, broken rotor bars in induction motor are detected using spectra analysis of the stator current. It is also shown that rise of number of broken bars and load levels increases amplitude of the particular side-band components of the stator currents in the faulty case. An induction motor with 1, 2, 3 and 4 broken bars at the rated load and the motor with 4 broken bars at no-load, 33%, 66%, 100% and 133% rated load are investigated. Time stepping finite element method is used for modeling broken rotor bars faults in induction motors. In this modeling, effects of the stator winding distribution, stator and rotor slots, geometrical and physical characteristics of different parts of the motor and nonlinearity of the core materials are taken into account. The simulation results are are verified by the experimental results. (C) 2012 Elsevier Ltd. All rights reserved.

    Journal Title

    Mechanical Systems and Signal Processing

    Volume

    30

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    131

    Last Page

    145

    WOS Identifier

    WOS:000304226500010

    ISSN

    0888-3270

    Share

    COinS