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Fig. 8 Contribution plots. a Healthy motor. b Rolling element fault of drive end bearing. c Inner
raceway fault of drive end bearing
show the contribution plots of outer raceway fault located at 3, 6, 12 of
'
clock
respectively.
The contribution plots could be used as signatures of the electric motor condi-
tions, so a supervised machine learning algorithm, with the PCA contributions as
inputs, can be used to diagnose each motor fault. The Figs.
8
and
9
show that the
identi
ed signatures by PCA contributions are features for each fault. The acceler-
ometers are involved in these signatures at different scales with different amplitudes.
As shown by Figs.
8
and
9
, all faults affect the accelerometer placed at the drive end.
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