Abstract:
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Bearing degradation is the most common source of
faults in electrical machines. In this context, this work presents a
novel monitoring scheme applied to diagnose bearing faults. Apart
from detecting local defects, i.e., single-point ball and raceway
faults, it takes also into account the detection of distributed defects,
such as roughness. The development of diagnosis methodologies
considering both kinds of bearing faults is, nowadays, subject of
concern in fault diagnosis of electrical machines. First, the method
analyzes the most significant statistical-time features calculated
from vibration signal. Then, it uses a variant of the curvilinear
component analysis, a nonlinear manifold learning technique, for
compression and visualization of the feature behavior. It allows
interpreting the underlying physical phenomenon. This technique
has demonstrated to be a very powerful and promising tool in the
diagnosis area. Finally, a hierarchical neural network structure is
used to perform the classification stage. The effectiveness of this
condition-monitoring scheme has been verified by experimental
results obtained from different operating conditions. |