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Signal Based Fault Detection
and Diagnosis for Rotating Electrical
Machines: Issues and Solutions
Andrea Giantomassi, Francesco Ferracuti, Sabrina Iarlori,
Gianluca Ippoliti and Sauro Longhi
Abstract Complex systems are found in almost all
field of contemporary science
and are associated with a wide variety of
financial, physical, biological, information
and social systems. Complex systems modelling could be addressed by signal based
procedures, which are able to learn the complex system dynamics from data pro-
vided by sensors, which are installed on the system in order to monitor its physical
variables. In this chapter the aim of diagnosis is to detect if the electrical machine is
healthy or a change is occurring due to abnormal events and, in addition, the
probable causes of the abnormal events. Diagnosis is addressed by developing
machine learning procedures in order to classify the probable causes of deviations
from system normal events. This chapter presents two Fault Detection and Diag-
nosis solutions for rotating electrical machines by signal based approaches. The
rst
one uses a current signature analysis technique based on Kernel Density Estimation
and Kullback
Liebler divergence. The second one presents a vibration signature
analysis technique based on Multi-Scale Principal Component Analysis. Several
simulations and experimentations on real electric motors are carried out in order to
verify the effectiveness of the proposed solutions. The results show that the pro-
posed signal based diagnosis procedures are able to detect and diagnose different
electric motor faults and defects, improving the reliability of electrical machines.
Fault Detection and Diagnosis algorithms could be used not only with the fault
diagnosis purpose but also in a Quality Control scenario. In fact, they can be
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