Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions - Complex System Modelling and Control Through Intelligent Soft Computations - page 271

integrated in test benches at the end or in the middle of the production line in order
to test the machines quality. When the electric motors reach the test benches, the
sensors acquire measurements and the Fault Detection and Diagnosis procedures
detect if the motor is healthy or faulty, in this last case further inspections can
diagnose the fault.

1 Introduction

Mathematical process models describe the relationship between input signals u ð k Þ

and output signals y and are fundamental for model-based fault detection. In

many cases the process models are not known at all or some parameters are
unknown. Further, the models have to be rather precise in order to express devi-
ations as results of process faults. Therefore, process-identi

ð k Þ

cation methods have to
be applied frequently before applying any model-based fault detection method as
stated in Giantomassi ( 2012 ). But also the identi

cation method itself may be a
source to gain information on, e.g. process parameters which change under the
in fl uence of faults. First publications on fault detection with identi cation methods

are found in Isermann ( 1984 ) and Filbert and Metzger ( 1982 ).
For dynamic processes the input signals may be the normal operating signal or
may be arti cation

methods is that with only one input and one output signal several parameters can be
estimated, which give a detailed picture on internal process quantities. The gen-
erated features for fault detection are then impulse response values in the case of
correlation methods or parameter estimates [see Isermann ( 2006 )].
On-line process monitoring with fault detection and diagnosis can provide range
of processes, as stated in Cheng et al. ( 2008 ), Giantomassi et al. ( 2011 ) and Fer-
racuti et al. ( 2010 , 2011 ). A large number of applications have been reviewed, e.g.
Isermann and Balle ( 1997 ) and Patton et al. ( 2000 ). Venkatasubramanian et al.
( 2000a , b , c ) published an article series reviewing monitoring methods with
attention in the

cially introduced for testing. A considerable advantage of identi

ed the Fault Detection and
Diagnosis methods as model-based, signal-based and knowledge-based. Signal-
based approaches to fault detection and isolation (FDI) in large-scale process plants
are consolidated and well studied, because for these processes the development of
model-based FDI methods requires considerable and eventually too high effort, and
moreover because a large amount of data is collected, as stated in Chiang et al.
( 2000 ) and Isermann ( 2006 ).
Fault detection and diagnosis (FDD) in industrial applications regards two
important aspects: the FDD for the production plant and for the systems that work
for the plant; among these systems, induction motors are the most important
electrical machineries in many industrial applications, considering that, electric

field of chemical processes. They classi