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motors account about 65 % of energy use. In the
ciency, the
monitoring activity of rotating electrical machines by fault detection and diagnosis
is in-depth investigated: Benloucif and Balaska ( 2006 ), Ran and Penman ( 2008 ),
Singh and Ahmed ( 2004 ), Taniguchi et al. ( 1999 ), Tavner ( 2008 ), Verucchi and
Acosta ( 2008 ). Vibration analysis is widely accepted as a tool to detect faults of a
machine since it is nondestructive, reliable and it permits continuous monitoring
without stopping the machine [see Ciandrini et al. ( 2010 ), Gani and Salami ( 2002 ),
Hua et al. ( 2009 ); Immovilli et al. ( 2010 ), Shuting et al. ( 2002 ); Zhaoxia et al.
( 2009 )]. In particular analysing the vibration power spectrum it is possible to detect
different faults that arise in rotating machines. In traditional machine vibration
signature analysis (MVSA), the Fourier transform is used to determine the vibration
power spectrum and the signature at different frequencies are identi
field of operational ef
ed and com-
pared with those related to healthy motors to detect faults in the machine, as in
Lachouri et al. ( 2008 ). The shortcoming of this approach is that the Fourier analysis
is limited to stationary signals while vibrations are not stationary by its nature.
The use of Soft Computing methods is considered an important extension to the
model-based approach Patton et al. ( 2000 ). It allows to improve residual generation
in FDD when process signals show complex behaviours. Multi-scale principal
component analysis (MSPCA) deals with processes that operate at different scales:
events occurring at different localizations in time and frequency, stochastic pro-
cesses and variables measured at different sampling rate, as reported in Bakshi
( 1998 ) and Li et al. ( 2000 ). PCA, treated in Jolliffe ( 2002 ) and Jackson ( 2003 ),
decorrelates the variables by extracting a linear relationship in order to transform
the multivariate space into a subspace which preserves maximum variance of the
original space. Wavelets extract deterministic features and approximately decor-
relate autocorrelated measurements. MSPCA combines these two techniques to
extract maximum information from multivariate sensor data (Misra et al. 2002 ).
Rotating electrical machines are well known systems with accurate analytical
models and extensive results in literature. Failure surveys, as Thomson and Fenger
( 2001 ), report that failures, in induction motors, are: stator related (38 %), rotor
related (10 %), bearing related (40 %) and others (12 %). Fast and accurate diag-
nosis of incipient faults allows actions to protect the power system, the process
leaded by the machine and the machine itself.
FDD techniques based on MVSA have received great attention in literature
because by vibrations it is possible to identify directly mechanical faults regarding
rotating electrical machines. In recent years, many methodologies have been
developed to detect and diagnose mechanical faults of electrical machines by
current measurements. In this context motor current signature analysis (MCSA)
involves detection and identi
cation of current signature patterns that are indicative
of normal and abnormal motor conditions. However, the motor current is in
uenced
by many factors such as electric supply, static and dynamic load conditions, noise,
motor geometry and faults. In Chilengue et al. ( 2011 ) an arti
fl
cial immune system
approach is investigated for the detection and diagnosis of faults in the stator and
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