Broken rotor bar fault detection of the grid and inverter-fed induction motor by effective attenuation of the fundamental component

Research output: Contribution to journalArticle


  • Bilal Asad
  • Toomas Vaimann
  • Anouar Belahcen

  • Ants Kallaste
  • Anton Rassõlkin
  • Muhammad Naveed Iqbal

Research units

  • Tallinn University of Technology


Since electrical machines are the largest consumer of electricity worldwide, their fault diagnostic at the incipient stage and condition monitoring is essential for better reliability, economy, and safety of operation. Out of several condition monitoring techniques, motor current signature analysis is gaining heightened popularity because of its non-invasive nature, the least number of sensors required and versatility of compatible algorithms. In this study, the best characteristics of infinite impulse response (IIR) filter are exploited to observe the broken rotor bar (BRB) frequencies with good legibility in current and voltage spectrum of the grid and inverter-fed motor, respectively. The causes of various harmonics in the stator current spectrum are first investigated for better understanding. The results are taken based on simulation and measurements taken from the laboratory setup. It is observed that a better tuning of IIR filters can make diagnostic algorithms capable of detecting the frequencies of interest by effectively attenuating the fundamental component and reducing its spectral leakage. Moreover, in case of direct torque control-based industrial inverter-fed motors, the current cannot be a good candidate for fault diagnostics rather the phase voltage can be effectively used for the detection of BRBs.


Original languageEnglish
Pages (from-to)2005-2014
Number of pages10
JournalIET Electric Power Applications
Issue number12
Publication statusPublished - 1 Dec 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Invertors, Condition monitoring, IIR filters, Stators, Rotors, Induction motors, Fault diagnosis, Torque control

ID: 39411406