TITLE:
A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals
AUTHORS:
Ahmed Alwodai, Tie Wang, Zhi Chen, Fengshou Gu, Robert Cattley, Andrew Ball
KEYWORDS:
Induction Motor; Motor Current Signature; Power Spectrum Bispectrum; Motor Bearing
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
16,
2013
ABSTRACT: Failure of induction
motors are a large concern due to its influence over industrial production.
Motor current signature analysis (MCSA) is common practice in industry to find
motor faults. This paper presents a new approach to detection and diagnosis of
motor bearing faults based on induction motor stator current analysis. Tests
were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the
signals associated with faults produce small modulations to supply component
and high nose levels, a modulation signal bispectrum (MSB) is used in this
paper to detect and diagnose different motor bearing defects. The results show
that bearing faults can induced a detestable amplitude increases at its
characteristic frequencies. MSB peaks show a clear difference at these
frequencies whereas conventional power spectrum provides change evidences only
at some of the frequencies. This shows that MSB has a better and reliable
performance in extract small changes from the faulty bearing for fault detection
and diagnosis. In addition, the study also show that current signals from
motors with variable frequency drive controller have too much noise and it is
unlikely to discriminate the small bearing fault component.