Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions - Complex System Modelling and Control Through Intelligent Soft Computations - page 273

rotor circuits of induction machines. The proposed technique measures the stator
currents to compute its representation before and after a fault condition. These
patterns are used to construct a characteristic image of the machine operating
condition. Moreover MCSA procedures are used to detect and diagnose not only
classic motor faults (i.e. rotor eccentricity), but also gear faults (i.e. tooth spall), as
presented in Feki et al. ( 2013 ). Fault Tolerant Control (FTC) as well as robust
control systems have been applied in electric drive systems Ciabattoni et al. ( 2011a ,
2011b , 2014 ). In Abdelmadjid et al. ( 2013 ) a FTC procedure is proposed for stator
winding fault of induction motors. It consists of an algorithm which can detect an
incipient fault in closed loop and switches itself between a nominal control strategy
for healthy condition and a robust control for faulty condition. Samsi et al. ( 2009 )
validated a technique, called Symbolic Dynamic Filtering (SDF), for early detection
of stator voltage imbalance in three-phase induction motors that involves Wavelet
Transform (WT) of current signals. In Baccarini et al. ( 2010 ) a sensor-less approach
has been proposed to detect one broken rotor bar in induction motors. This method
is not affected by load and other asymmetries. The technique estimates stator and
rotor flux and analyses the differences obtained in torque. A new saturation model

that explains the experimental data is investigated in Pedra et al. ( 2009 ). The model
has three different saturation effects, which have been characterized in four
induction motors.
As possible solutions of the FDI problem for electrical machines, two different
approaches are proposed: the

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first one uses vibration signals provided by acceler-
ometer sensors placed on the machine, and the second one uses current signals
provided by inverters.
In the first solution, based on current signal analysis of rotating electrical

machines, different algorithms are applied for FDD: PCA is used to reduce the three-
phase current space in two dimensions. Then, Kernel Density Estimation (KDE) is
adopted to estimate the probability density function (PDF) of each healthy and faulty
motor, which are typical features that can be used to identify each fault [see Ferracuti
et al. ( 2013a )]. Kullback - Leibler (K - L) divergence is used as a distance between

two PDF obtained by KDE. K L allows to identify the dissimilarity between two

probability distributions (that can also be multidimensional): one is related to the
modelled signatures and the other one is related to the acquired data samples. The
classi

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cation of each motor condition is performed by K-L divergence.
In the second approach, based on vibration analysis of rotating electrical
machines, MSPCA is applied for fault detection and diagnosis (Ferracuti et al.
2013b ; Lachouri et al., 2008 ; Misra et al. 2002 ). Fault identi

cation is evaluated
by calculating the contributions of each variable in the principal component sub-
space and in the residual space. KDE, which allows to estimate the PDF of random
variables is introduced, in Odiowei and Cao ( 2010 ), to improve fault detection and
isolation. The contributions PDFs are estimated by KDE, the thresholds are com-
puted for each signal in order to improve fault detection. Faults are classi ed

by using the contribution plots by Linear Discriminant Analysis (LDA).