Abstract:
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Mostly the faults in electrical machines are
related with the bearings. Thus, a reliable bearing condition
monitoring scheme able to detect either local or distributed
defects are mandatory to avoid a breakdown in the machine.
So far, the research has been carried out mainly in the
detection of local faults, such as balls and raceways faults, but
surface roughness is not so reported. This paper deals with a
novel and reliable scheme capable to detect any fault that may
occur in a bearing, based on EXIN Curvilinear Component
Analysis, CCA, and Neural Network. The EXIN CCA, which
is an improvement of the Curvilinear Component Analysis,
has been conceived for data visualization, interpretation and
classification for real time industrial applications. The
effectiveness of this condition monitoring scheme has been
verified by experimental results obtained from different
operation conditions. |