Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects

Jaehoon Shin, Yonghyun Park, Sang Bin Lee

Research output: Contribution to journalArticlepeer-review


Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting faults in the rotor cage of induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as an alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magneto-motive force or airgap can be directly observed. In this paper, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectra is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide detection of rotor faults that is insensitive to the load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between the proposed flux monitoring method and existing methods (MCSA, vibration analysis) is also given.

Original languageEnglish
JournalIEEE Transactions on Industry Applications
Publication statusAccepted/In press - 2021


  • Airgap Flux
  • Eccentricity
  • Fault Diagnostics
  • Leakage Flux
  • Load Unbalance
  • Misalignment
  • Search Coil
  • Spectral Analysis
  • Squirrel Cage Induction Motor
  • Vibration

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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