Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Abstract

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

Full text

Citation: Khaneghah, M.Z.;
Alzayed, M.; Chaoui, H. Fault
Detection and Diagnosis of the
Electric Motor Drive and Battery
System of Electric Vehicles. Machines
2023,11, 713. https://doi.org/
10.3390/machines11070713
Academic Editor: Jose Alfonso
Antonino-Daviu
Received: 7 June 2023
Revised: 30 June 2023
Accepted: 3 July 2023
Published: 5 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
machines
Review
Fault Detection and Diagnosis of the Electric Motor Drive and
Battery System of Electric Vehicles
Mohammad Zamani Khaneghah 1, Mohamad Alzayed 1, * and Hicham Chaoui 1,2
1Intelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design,
Carleton University, Ottawa, ON K1S 5B6, Canada; mohammadzamanikhane@cmail.carleton.ca (M.Z.K.);
hicham.chaoui@carleton.ca (H.C.)
2Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
*Correspondence: mohamad.alzayed@carleton.ca; Tel.: +1-613-520-2600 (ext. 7467)
Abstract:
Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and
reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric
motor drive and battery system, are critical components that are susceptible to different types of
faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and
potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous
Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently,
fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have
become a prominent area of research. An effective FDD approach must possess qualities such as
accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based
and signal-based methods. However, data-driven approaches, including machine learning-based
methods, have recently gained traction due to their promising capabilities in fault detection. This
paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery
systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information
presented herein can serve as a valuable reference for future endeavors in this field.
Keywords:
fault detection and diagnosis (FDD); electric vehicles; PMSM; lithium-ion battery pack;
model based; data driven; machine learning; deep learning
1. Introduction
Electrified transportation is one of the main strategies to reduce carbon emissions
contributing to climate change and global warming. Additionally, limited fossil fuel sources
and instability in countries producing fossil fuels draw attention to electric vehicles (EVs).
The number of EVs is rising at a fast pace, and many governments are putting forth
legislation to increase the market share of EVs in the next decade. In this regard, the safety
and reliability of EVs become critical to gaining a considerable market share. EVs include
several components, all of which are prone to different types of faults. However, the electric
motor drive and battery system are its core components, and the main faults of an EV
usually occur in these components. As a result, the healthy operation of these parts is of
high importance and needs precise monitoring.
Electric motors are employed extensively in various industries and are especially
utilized as the powertrain of EVs. For the transportation industry, EVs’ reliability and
safety are crucial. However, due to their harsh working environment, various types of
faults can occur in the motor and its drive system that can degrade system performance and
reduce the reliability and safety of EVs. The interior permanent magnet synchronous motor
(IPMSM) is the most used in EVs based on high power density and efficiency [
1
]. On the
other hand, as the demand for PMSMs grows and the cost of their materials remains high,
designers are forced to optimize their designs, increasing complexity and making the PMSM
Machines 2023,11, 713. https://doi.org/10.3390/machines11070713 https://www.mdpi.com/journal/machines

Machines 2023,11, 713 2 of 33
more vulnerable to different types of faults. At the same time, the transportation industry
requires continuity despite the operating environment of EV motors. Electrical motor drive
faults may occur in the motor as the main part or in the inverter of the motor drive system,
and they can be classified into three main groups: electrical faults, mechanical faults, and
sensor faults [
2
]. Interturn short fault (ITSF), open- or short-phase faults, demagnetization
fault and open or short circuits of the switches in the inverter are considered electrical
faults. Faults related to the rotor, such as bearing faults, bent shaft and airgap eccentricity,
are mechanical faults. Defects in each of the various sensors are referred to as sensor faults.
If these faults are discovered early, proper measures can be taken to avoid costly damage
and catastrophic failures.
As the energy storage part of EVs, the lithium-ion battery system has taken the lead in
EV applications due to its outstanding features, including high power and energy density,
long lifespan and environmental factors [
3
]. A battery pack usually comprises hundreds
of cells connected in series and parallel configurations. However, different types of faults,
including battery abuse and actuator and sensor faults, may occur in battery systems
resulting in battery degradation and accelerated aging, EV failure and dangerous accidents.
It is reported that 30% of EV accidents stem from battery faults [4].
Thus, developing reliable online fault detection and fault tolerant control is needed
to guarantee safe and continuous EV operation. However, complex operations and other
unpredictable factors make early fault detection challenging. Fault detection and diagnosis
(FDD) is a technique to monitor and determine the operating state of an electric motor,
which allows early fault detection and prediction. With the use of FDD, various faults can
be detected and identified, and by taking proper measures, the safety and reliability of EVs
increase [5].
Many FDD methods have already been introduced to overcome the risk of poten-
tial faults in electric motor drives and battery systems. FDD methods can generally be
categorized into model-based, signal-based, data-driven (knowledge-based), and hybrid
methods. The model-based methods are based on the difference between the measured
and estimated values by the system model and observers. There are different model-based
techniques, such as state observer, parameter estimation, extended Kalman filter (EKF),
linear parameter varying and finite element analysis (FEA), to name a few [
6
]. In signal-
based methods, the fault symptoms are extracted from the output signals, and there is
no need for an accurate system model. The features can be extracted through the time
domain, frequency domain or time-frequency domain by analyzing the spectrum, phase,
magnitude, deviations, etc. [
7
]. Some of the feature extraction methods are fast Fourier
transform (FFT), Hilbert Huang transform (HHT), Wavelet transform (WT) and Winger
Ville [
6
]. Model-based and signal-based methods need prior motor knowledge, are sensitive
to load and are slow at fault detection. Data-driven methods differ from model-based and
signal-based methods, as they can be implemented without a pre-existing knowledge of
the model or signal pattern of traction systems, which is the main advantage of this type
of FDD. A considerable amount of historical data under healthy and faulty conditions
are required for the data-driven method to be performed effectively; however, it is not
considered an insurmountable challenge. Also, as in this method, the system model is
not required; it has more capability to generalize the FDD method to multiphase motors
with more complex models and more uncertainties. Some of the primary and most-used
approaches in data-driven methods include Hypothesis Test and Test Statistics, Principal
Component Analysis (PCA), Independent Component Analysis (ICA), Canonical Correla-
tion Analysis (CCA), Neural Networks (NN), Support Vector Machine (SVM), Bayesian
Network (BN), Deep learning and other machine learning methods.
This paper surveys different types of electric motor drives and battery system faults
to understand their basis and effects. Different FDD methods are introduced, and recent
works and state-of-the-art techniques are reviewed, including their advantages and limita-
tions. Section 2introduces the different types of faults in the electric motor drive. Section 3
presents the battery system faults. The existing model-based and signal-based FDD meth-

Machines 2023,11, 713 3 of 33
ods for PMSM motor drives are studied in Section 4. Section 5focuses on the data-driven
methods, and the battery system FDD methods are reviewed in Section 6.
2. Electric Motor Drive Faults
Three main groups of faults in PMSM motor drives are categorized as electrical,
mechanical and sensor faults. These faults may occur in the motor part or the inverter part.
Figure 1shows a diagram of various electric motor drive faults.
Machines 2023, 11, x FOR PEER REVIEW 3 of 36
methods for PMSM motor drives are studied in Section 4. Section 5 focuses on the data-
driven methods, and the baery system FDD methods are reviewed in Section 6.
2. Electric Motor Drive Faults
Three main groups of faults in PMSM motor drives are categorized as electrical, me-
chanical and sensor faults. These faults may occur in the motor part or the inverter part.
Figure 1 shows a diagram of various electric motor drive faults.
Figure 1. Various electric motor drive faults.
2.1. Electrical Faults
The main electrical faults, as noted above, are winding interturn short-circuit faults
(ITSF) and open- or short-phase and demagnetization faults, which are related to the mo-
tor. Additionally, open circuits or short circuits in switches and DC-link capacitor failures
are associated with the inverter.
2.1.1. Interturn Short-Circuit Fault
Breakdown and degradation in the stator turn-to-turn windings insulation of a
PMSM are usually due to a power surge, moisture, or mechanical, electrical and thermal
stresses, resulting in a short circuit in the windings [8]. This failure is known as the fault
(ITSF) and has the highest failure rate among motor faults [9].
As shown in Figure 2, the shorted turns create an additional circuit loop connected
to flux linkages created by other motor windings and the rotor magnet. A high-fault cur-
rent is created in the ITSF windings because of the low impedance and high-coupled flux
linkage voltage leading to stator overcurrent and overheating [10,11]. At the early stages
of the ITSF, with failure in only a few percentages of turns, the motor can continue to
operate with degraded performance. However, the heat produced by the overcurrent can
damage the insulation of the nearby turns and expand to the whole phase at a high pace
[12] and lead to a phase-to-phase or phase-to-ground short circuit and severe motor failure
in a short time with high repair costs. Also, the rotor permanent magnet can potentially
be permanently demagnetized by the high fault current in the extra current route [13,14].
Therefore, incipient fault detection becomes critical for ITSF. Usually, the ratio of the
Electric motor drive
faul ts
Sensor faultsElectrical faults Mechanical
Faul ts
Inv erter
faults Moto r faults Curren t
sensor
Speed sensor Oth er
sensors
Voltage
sensor
Open
switch
Short
switch
ITSF
Demag n
etiza tion
Open
phase
Short
phase
DC-lin k
capacitor
Bearing fault Eccentricity
fault
Bent shaft
Bolt
loosening
Damaged
magnet
Figure 1. Various electric motor drive faults.
2.1. Electrical Faults
The main electrical faults, as noted above, are winding interturn short-circuit faults
(ITSF) and open- or short-phase and demagnetization faults, which are related to the motor.
Additionally, open circuits or short circuits in switches and DC-link capacitor failures are
associated with the inverter.
2.1.1. Interturn Short-Circuit Fault
Breakdown and degradation in the stator turn-to-turn windings insulation of a PMSM
are usually due to a power surge, moisture, or mechanical, electrical and thermal stresses,
resulting in a short circuit in the windings [
8
]. This failure is known as the fault (ITSF) and
has the highest failure rate among motor faults [9].
As shown in Figure 2, the shorted turns create an additional circuit loop connected to
flux linkages created by other motor windings and the rotor magnet. A high-fault current is
created in the ITSF windings because of the low impedance and high-coupled flux linkage
voltage leading to stator overcurrent and overheating [
10
,
11
]. At the early stages of the
ITSF, with failure in only a few percentages of turns, the motor can continue to operate
with degraded performance. However, the heat produced by the overcurrent can damage
the insulation of the nearby turns and expand to the whole phase at a high pace [
12
]
and lead to a phase-to-phase or phase-to-ground short circuit and severe motor failure
in a short time with high repair costs. Also, the rotor permanent magnet can potentially
be permanently demagnetized by the high fault current in the extra current route [
13
,
14
].
Therefore, incipient fault detection becomes critical for ITSF. Usually, the ratio of the shorted
turns to the number of turns in a coil is regarded as the severity of ITSF. As the severity

Machines 2023,11, 713 4 of 33
increases, the induced back-EMF voltage of the shorted turns rises and subsequently, the
short-circuit current rises rapidly, resulting in more system imbalance [15].
Machines 2023, 11, x FOR PEER REVIEW 4 of 36
shorted turns to the number of turns in a coil is regarded as the severity of ITSF. As the
severity increases, the induced back-EMF voltage of the shorted turns rises and subse-
quently, the short-circuit current rises rapidly, resulting in more system imbalance [15].
Figure 2. Interturn short-circuit fault in one phase winding of PMSM [8].
2.1.2. Demagnetization Fault
Physical damage, high-temperature operation, aging or an inverse magnetic field can
all induce demagnetization, which reduces the strength of the permanent magnet (PM)
inside the IPMSM. Also, an ITSF, if not detected and tolerated in time, can result in partial
demagnetization due to the induced reverse magnetitic field [16]. Reversible and irreversi-
ble demagnetization are the two forms of demagnetization. The former is caused by a field
weakening control, whereas the second suffers from permanent demagnetization. An in-
appropriate operating point of the IPMSM because of the combined influence of temper-
ature and a shift in the permeance curve [17] is a key cause of irreversible demagnetiza-
tion. If demagnetization happens, it lowers the torque of the PMSM due to the reduced
PM flux linkage. Consequently, it negatively impacts the motor’s characteristics and effi-
ciency [18]. The current in demagnetized PMSMs must increase to compensate for the
effect of a weakened PM and produce the same torque as a healthy state [19]; nevertheless,
this means increasing copper losses and temperature [20]. On the other hand, high tem-
peratures can result in far more severe irreversible demagnetization [21]. Consequently,
the reliability and safety of the system would be decreased. Utilizing fault detection and
diagnosis technologies is vital to avoid such consequences. Demagnetization fault can re-
sult in additional frequency components in stator current and the vibration and result in
pulsation in torque and speed. These signatures can be used for demagnetization fault
detection [22,23].
2.1.3. Open or Short Switches in the Inverter
Inverters are used in electric motor drive systems as a core component, as shown in
Figure 3. Due to the high-frequency operation, high power stresses, aging and other con-
ditions, the switching devices are the components most expected to fail while in use (about
38% of faults in drivers [24]), which commonly appear as a short-circuit or open-circuit
failure. Open-circuit faults usually occur because of a gate signal failure or disconnecting
of the wire. Such a fault does not stop the drive system from operating [25]. As an open-
circuit fault stops the defective phase winding stimulation in a switching device, the sys-
tem operates in phase-locking mode. As a result, the drive system loses equilibrium, and
the rotor is subjected to an imbalanced force, resulting in a considerable reduction in sys-
tem performance [2] and noticeable vibrations and can end in secondary faults in the
Figure 2. Interturn short-circuit fault in one phase winding of PMSM [8].
2.1.2. Demagnetization Fault
Physical damage, high-temperature operation, aging or an inverse magnetic field
can all induce demagnetization, which reduces the strength of the permanent magnet
(PM) inside the IPMSM. Also, an ITSF, if not detected and tolerated in time, can result in
partial demagnetization due to the induced reverse magnetitic field [
16
]. Reversible and
irreversible demagnetization are the two forms of demagnetization. The former is caused
by a field weakening control, whereas the second suffers from permanent demagnetization.
An inappropriate operating point of the IPMSM because of the combined influence of
temperature and a shift in the permeance curve [
17
] is a key cause of irreversible demag-
netization. If demagnetization happens, it lowers the torque of the PMSM due to the
reduced PM flux linkage. Consequently, it negatively impacts the motor’s characteristics
and efficiency [
18
]. The current in demagnetized PMSMs must increase to compensate for
the effect of a weakened PM and produce the same torque as a healthy state [
19
]; neverthe-
less, this means increasing copper losses and temperature [
20
]. On the other hand, high
temperatures can result in far more severe irreversible demagnetization [
21
]. Consequently,
the reliability and safety of the system would be decreased. Utilizing fault detection and
diagnosis technologies is vital to avoid such consequences. Demagnetization fault can
result in additional frequency components in stator current and the vibration and result
in pulsation in torque and speed. These signatures can be used for demagnetization fault
detection [22,23].
2.1.3. Open or Short Switches in the Inverter
Inverters are used in electric motor drive systems as a core component, as shown in
Figure 3. Due to the high-frequency operation, high power stresses, aging and other condi-
tions, the switching devices are the components most expected to fail while in use (about
38% of faults in drivers [
24
]), which commonly appear as a short-circuit or open-circuit
failure. Open-circuit faults usually occur because of a gate signal failure or disconnecting
of the wire. Such a fault does not stop the drive system from operating [
25
]. As an open-
circuit fault stops the defective phase winding stimulation in a switching device, the system
operates in phase-locking mode. As a result, the drive system loses equilibrium, and the
rotor is subjected to an imbalanced force, resulting in a considerable reduction in system
performance [
2
] and noticeable vibrations and can end in secondary faults in the motor due
to the lack of FDD. Short-circuit faults are usually the result of overvoltage, overheating,
breakdown of the protection components or a wrong gate signal [
25
]. Furthermore, when

Machines 2023,11, 713 5 of 33
a power switch is short-circuited, the defective phase winding is constantly stimulated,
regardless of the rotor position, and causes instant overcurrent. Consequently, the faulty
phase creates a significant, reversed braking torque during its demagnetization period, and
the drive system’s stability is significantly damaged, resulting in a subsequent failure of
the entire system [
26
]. In this case, the protective circuits come into effect as an overcur-
rent is produced immediately, making the inverter shut down; it needs to be repaired to
operate again. Hence, identifying and isolating power transistor faults and their locations
accurately and quickly is critical for the safe functioning of a PMSM drive.
Machines 2023, 11, x FOR PEER REVIEW 5 of 36
motor due to the lack of FDD. Short-circuit faults are usually the result of overvoltage,
overheating, breakdown of the protection components or a wrong gate signal [25]. Fur-
thermore, when a power switch is short-circuited, the defective phase winding is con-
stantly stimulated, regardless of the rotor position, and causes instant overcurrent. Con-
sequently, the faulty phase creates a significant, reversed braking torque during its de-
magnetization period, and the drive system’s stability is significantly damaged, resulting
in a subsequent failure of the entire system [26]. In this case, the protective circuits come
into effect as an overcurrent is produced immediately, making the inverter shut down; it
needs to be repaired to operate again. Hence, identifying and isolating power transistor
faults and their locations accurately and quickly is critical for the safe functioning of a
PMSM drive.
Figure 3. m-phase inverter of an electric motor [27].
2.2. Mechanical Faults
Mechanical faults are as important as electrical faults and need in-time detection. The
main mechanical faults are bearing faults and air–gap eccentricity. Some other mechanical
faults include a bent shaft, damaged magnet and bolt loosening [28].
2.2.1. Bearing Faults
A bearing fault is the most common fault among all possible motor faults, contrib-
uting about 40–50% [29]. Bearing faults can be in the inner raceway, outer raceway, cage
or ball bearings. The main reasons behind the bearing fault are poor lubrication, mechan-
ical vibrations, shaft misalignment, overload, corrosion and eventually fatigue, even un-
der normal conditions. If the bearing defect is not detected and repaired in time, other
forms of faults, such as air–gap eccentricity, ITSF and even complete motor failure, are
expected [30]. Figure 1 in [31] illustrates the rolling bearing structure.
2.2.2. Air–Gap Eccentricity Faults
Some mechanical problems, such as unbalanced loads, shaft misalignments, rotor
imbalance, missing bolt and bearing faults, result in a rotor eccentricity fault within the
motor [28]. In fact, it is the uneven air gap between the stator and rotor and is categorized
into three types: static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity
(ME). SE refers to the condition that the minimum air gap has a fixed value and hardly
ever alters with time, mainly caused during the manufacturing stage. DE occurs where
the minimum air gap location rotates along with the rotor and is brought on by rotor
flaws, worn bearings, and bent shafts. The ME has both SE and DE defects simultaneously
[32].
2.3. Sensor Faults
Different types of sensors, including current, voltage, speed, or position sensors, are
needed to provide a motor drive control system with different feedback signals. A sensor
fault refers to any defect or failure in such sensors which can happen due to vibration,
temperature, moisture, etc. [33]. Sensor faults can be open circuits, gain deviation or high
noise [34]. If a fault occurs in any of these sensors, incorrect information is fed to the
Figure 3. m-phase inverter of an electric motor [27].
2.2. Mechanical Faults
Mechanical faults are as important as electrical faults and need in-time detection. The
main mechanical faults are bearing faults and air–gap eccentricity. Some other mechanical
faults include a bent shaft, damaged magnet and bolt loosening [28].
2.2.1. Bearing Faults
A bearing fault is the most common fault among all possible motor faults, contributing
about 40–50% [
29
]. Bearing faults can be in the inner raceway, outer raceway, cage or
ball bearings. The main reasons behind the bearing fault are poor lubrication, mechanical
vibrations, shaft misalignment, overload, corrosion and eventually fatigue, even under
normal conditions. If the bearing defect is not detected and repaired in time, other forms of
faults, such as air–gap eccentricity, ITSF and even complete motor failure, are expected [
30
].
Figure 1in [31] illustrates the rolling bearing structure.
2.2.2. Air–Gap Eccentricity Faults
Some mechanical problems, such as unbalanced loads, shaft misalignments, rotor
imbalance, missing bolt and bearing faults, result in a rotor eccentricity fault within the
motor [
28
]. In fact, it is the uneven air gap between the stator and rotor and is categorized
into three types: static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity
(ME). SE refers to the condition that the minimum air gap has a fixed value and hardly
ever alters with time, mainly caused during the manufacturing stage. DE occurs where the
minimum air gap location rotates along with the rotor and is brought on by rotor flaws,
worn bearings, and bent shafts. The ME has both SE and DE defects simultaneously [32].
2.3. Sensor Faults
Different types of sensors, including current, voltage, speed, or position sensors, are
needed to provide a motor drive control system with different feedback signals. A sensor
fault refers to any defect or failure in such sensors which can happen due to vibration,
temperature, moisture, etc. [
33
]. Sensor faults can be open circuits, gain deviation or high
noise [
34
]. If a fault occurs in any of these sensors, incorrect information is fed to the
motor’s monitoring and controller system, leading to degraded performance and even
complete motor failure. Therefore, fault detection and diagnosis are essential to avoiding
such failure and reduced reliability [35].

Machines 2023,11, 713 6 of 33
2.3.1. Current Sensor Faults
At least two current sensors are used to measure the phase currents of a three-phase
PMSM. Current sensor faults can be found in three types, zero output, incorrect gain and
dc offset, none of which need rapid detection and repair but can lead to reduced efficiency
and overheating [2].
2.3.2. Voltage Sensor Faults
If the voltage sensor fault causes a rapid increase in the measured DC-link voltage, it
can lead to system failure in a small period. In this situation, fast fault detection and repair
are critical. Sometimes a fault can cause slight changes and deviations in the measured
value, allowing the motor to operate for some time with reduced performance. Eventually,
any fault in the voltage sensor must be detected and tolerated [2].
2.3.3. Speed or Position Sensor Faults
The rotor position and speed are measured by the position and speed sensors in the
motor drive to feed the control system. Photoelectric incremental encoders are mostly
used for this object. Any fault in this sensor can affect motor functionality. It can result in
wrong-direction rotation, reducing the speed from the desired speed to zero, making the
motor stop, or, most dangerously, increasing the speed more than desired to the maximum
possible motor speed. The last situation results in persistent overload and even catastrophic
accidents. As a result, FDD has a crucial role in preventing such conditions [2].
3. Battery System Faults
The potential faults of the battery pack can be classified into three main groups: battery
abuse, connection faults, and sensor faults. The occurrence of each of these faults can result
in heat generation and, if they are not detected or tolerated in time, can increase the aging
speed and even result in thermal runaway and explosion [
36
]. Figure 4shows a diagram of
battery system faults.
Machines 2023, 11, x FOR PEER REVIEW 6 of 36
motor’s monitoring and controller system, leading to degraded performance and even
complete motor failure. Therefore, fault detection and diagnosis are essential to avoiding
such failure and reduced reliability [35].
2.3.1. Current Sensor Faults
At least two current sensors are used to measure the phase currents of a three-phase
PMSM. Current sensor faults can be found in three types, zero output, incorrect gain and
dc offset, none of which need rapid detection and repair but can lead to reduced efficiency
and overheating [2].
2.3.2. Voltage Sensor Faults
If the voltage sensor fault causes a rapid increase in the measured DC-link voltage, it
can lead to system failure in a small period. In this situation, fast fault detection and repair
are critical. Sometimes a fault can cause slight changes and deviations in the measured
value, allowing the motor to operate for some time with reduced performance. Eventually,
any fault in the voltage sensor must be detected and tolerated [2].
2.3.3. Speed or Position Sensor Faults
The rotor position and speed are measured by the position and speed sensors in the
motor drive to feed the control system. Photoelectric incremental encoders are mostly
used for this object. Any fault in this sensor can affect motor functionality. It can result in
wrong-direction rotation, reducing the speed from the desired speed to zero, making the
motor stop, or, most dangerously, increasing the speed more than desired to the maxi-
mum possible motor speed. The last situation results in persistent overload and even cat-
astrophic accidents. As a result, FDD has a crucial role in preventing such conditions [2].
3. Baery System Faults
The potential faults of the baery pack can be classified into three main groups: bat-
tery abuse, connection faults, and sensor faults. The occurrence of each of these faults can
result in heat generation and, if they are not detected or tolerated in time, can increase the
aging speed and even result in thermal runaway and explosion [36]. Figure 4 shows a
diagram of baery system faults
Figure 4. Var i ou s ba ery faults.
Actuat or faults
Battery abuse
faults Sensor Faults
Batt ery faults
Over charge
Int ernal s hort-
circuit
External
short-circuit
Over dishchar
ge
...
Thermal
runaway
Conne ction
faul ts
Cooling
system faults
...
Area network
bus faults
Voltage
sensor
Temperature
sensor
Current
sensor
Figure 4. Various battery faults.
3.1. Battery Abuse Faults
This group of faults contains overcharge, over-discharge, internal short circuit, external
short circuit, thermal runaway, etc., which can happen inside the battery. Errors in the
battery management systems and cell capacity degradation can result in overcharge and
over-discharge faults. These faults can lead to chemical and physical damage to the battery,
degrading the battery’s performance and safe operation [
37
]. The internal short circuit
refers to the insulation failure between the layers inside the battery, while the external one

Machines 2023,11, 713 7 of 33
notices the shorted positive and negative terminals [
38
]. An external short circuit is a more
dangerous and noticeable fault than an internal short circuit, which is negligible in the early
stages. However, the internal short circuit can turn into an intense fault after a while [
39
].
Rapid voltage drop and thermal runaway are expected when a short circuit occurs.
3.2. Actuator Faults
Connection faults, cooling system faults, controller area network bus faults, etc. belong
to this group of faults. Due to the need for a high level of energy in EV applications,
the battery system usually consists of many battery cells connected in a parallel–series
configuration. Due to the working environment of EV, temperature changes, vibration and
aging, the connection can become defective. Loose connections can reduce the available
power, resulting in potential accidents. Increasing the resistance of the connection can cause
heat production and affect the battery performance [
40
]. If the cooling system fails, the
battery temperature may exceed the allowed temperature range and even lead to thermal
runaway, so it is one of the considerable battery faults.
3.3. Sensor Faults
Battery management system (BMS) plays a crucial role in the safe, reliable and effective
performance of EVs. This unit is responsible for several tasks, including estimating the
state of charge (SOC) and state of health (SOH) of the battery, thermal management, cell
balancing, etc., by monitoring the voltage, current and temperature of the cells [
41
]. In this
regard, many current, voltage and temperature sensors are utilized in the battery system.
Any defect and fault in these sensors can be reflected in the BMS performance and lead to
further faults such as battery abuse faults and significant failures, all of which reduce the
battery lifespan and safety.
4. Fault Detection and Diagnosis of Electric Motor Drives
Reliability and safety are always of high priority in every application, but in trans-
portation systems, they are even more critical, as transportation needs continuity and safety,
notwithstanding the operating environment of EV motors. As discussed, the electric motor
and its drive system are always vulnerable to different types of faults, which inevitably
occur [
42
]. Undetected faults can lead to performance degradation, high repair expenses
and even catastrophic accidents. To overcome such risks, increase reliability, avoid unex-
pected EV stops and high repair costs, and increase safety, FDD is considered in many
systems with different applications. FDD is a method of keeping track of motor performance
to detect, identify and locate faults as early as possible. FDD provides the opportunity to
take proper measures as soon as the fault occurs and tolerate the faults. An FDD technique
needs to comply with certain requirements to be considered effective, such as: (i) fast
detection time, (ii) robust to varying operating conditions, (iii) sensitive enough but with
no false alarm, and (iv) requiring no additional hardware (due to cost and complexity).
Selecting the proper fault index plays the most critical role in fault detection. Since the fault
can alter a motor’s parameters, utilizing multiparameter fault indicators can improve the
detection method’s robustness and accuracy [
43
]. Figure 5indicates the overall schematic
of the EV motor drive system with FDD and fault-tolerant control.
As indicated in Figure 6, the FDD methods utilized in PMSM motor drives are di-
vided into three main classes: model-based, signal-based (or signal processing) and data-
driven [
44
,
45
]. Also, in some applications, combining these methods is used to take
advantage of different methods simultaneously, referring to hybrid FDD methods. Table 1
indicates a summary of FDD categories.

Machines 2023,11, 713 8 of 33
Machines 2023, 11, x FOR PEER REVIEW 8 of 36
Figure 5. PMSM motor drive schematic with FDD and fault-tolerant control.
As indicated in Figure 6, the FDD methods utilized in PMSM motor drives are di-
vided into three main classes: model-based, signal-based (or signal processing) and data-
driven [44,45]. Also, in some applications, combining these methods is used to take ad-
vantage of different methods simultaneously, referring to hybrid FDD methods. Table 1
indicates a summary of FDD categories.
Figure 6. Different classes of FDD methods.
Fault Detectio n and
Diagnosis methods
Signal-basedModel-based Data-driv en
State observer
Model
predictive
control
Finite element
analysis
Parameter
estim ation
Current-ba sed Voltage-based
Vibration-
based Flux-bas ed
Deep lear ning
Neural
network
Machine
learning
Fuzz y logic
Finite element
analysis
Parity space
equation
...
...
Time-
freq uency
domain
Freq uency
domain
Time domain
Figure 5. PMSM motor drive schematic with FDD and fault-tolerant control.
Machines 2023, 11, x FOR PEER REVIEW 8 of 36
Figure 5. PMSM motor drive schematic with FDD and fault-tolerant control.
As indicated in Figure 6, the FDD methods utilized in PMSM motor drives are di-
vided into three main classes: model-based, signal-based (or signal processing) and data-
driven [44,45]. Also, in some applications, combining these methods is used to take ad-
vantage of different methods simultaneously, referring to hybrid FDD methods. Table 1
indicates a summary of FDD categories.
Figure 6. Different classes of FDD methods.
Fault Det ectio n and
Diagnosis methods
Signal-basedModel-based Data-driven
State observer
Model
predictive
control
Finite element
analysis
Parameter
estim ation
Current-ba sed Voltage-based
Vibration-
based Flux- based
Deep l earning
Neural
network
Machine
learning
Fuzz y logic
Finite element
analysis
Parity space
equation
...
...
Time-
freq uency
domain
Freq uency
domain
Time domain
Figure 6. Different classes of FDD methods.

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Table 1. Summary of FDD categories.
Type Basis Features
Model-based Using the system model and the
estimated parameters for fault detection
Very effective and reliable for simple systems
Low cost and fast detection
Modelling complex systems is difficult
Uncertainties affect the model
Sensitive to load and parameters variations
Prior knowledge and model are needed
Signal-based
Using output signal and
signal-processing methods for
fault detection
Easy implementation
Suitable for complex systems
Slow detection speed or high cost for faster detection
methods due to the need for extra hardware
Data-driven Using historical data for training the
system and fault detection
No prior knowledge needed
No system model or signal pattern needed
Suitable for complex systems
Generalization capability
High accuracy, even for incipient fault detection
Quality and quantity of the historical data can
affect FDD performance
4.1. Model-Based FDD Methods
Model-based techniques are developed by comparing the measured values with the
estimated values produced by the system model. In the first stage, the mathematical model
of the motor is used to estimate the expected signal values in a healthy condition. Then,
these estimated values are compared with the actual measured signals and the residual
signals are generated. The signal (signals) considered for fault detection can differ based on
the desired fault type and fault detection methodology. In the second stage of model-based
FDD, the residual signals reveal if there is a fault or if the motor is operating in a healthy
condition [
46
,
47
]. Model-based approaches are fast and effective, but they need an accurate
system model, which brings limitations and reduces the efficiency of the FDD method for
complex systems with many uncertainties. Furthermore, expert knowledge is needed [
48
].
There are different model-based techniques [
49
], such as state observer [
50
], parameter
estimation [
51
], parity space equations [
52
], extended Kalman filter (EKF), linear parameter
varying, finite element analysis (FEA) and model predictive control (MPC), to name a few.
Different types of model-based FDD techniques have been introduced and some of them
have been studied as follows. Figure 7shows general schematic of model-based method
where the green cycle is the fault detection unit.
Machines 2023, 11, x FOR PEER REVIEW 10 of 36
Figure 7. General diagram of model-based FDD workflow [53].
The state-observer method, as one of the most-used techniques with the general di-
agram shown in Figure 8, is usually divided into two main subgroups: voltage-based ob-
server [54] and current-based observer [55]. The voltage-based methods are fast diagnosis
techniques and can be used to increase the fault detection speed, but usually, extra voltage
sensors are needed. Consequently, adding voltage sensors increases the system’s cost, vol-
ume and complexity, which is regarded as a drawback for FDD techniques [56]. A stator
flux linkage (SFL) DC-offset observer is proposed in [57] for stator fault detection. It is
analyzed in an antisynchronous reference frame (ASRF) after SFL is estimated in the stator
reference frame and transformed into ASRF. This method is simple and is unaffected by
operating conditions and stator connection type (delta or star). In [54], a voltage-based
observer is utilized for robust open fault detection to estimate the converter voltages and
takes advantage of obtaining reference voltage from the control system. In this case, there
is no need for extra hardware, reducing the cost of the FDD method. On the other hand,
as current sensors are usually utilized for motor control, using current-based observers
does not require additional sensors. In [58], a current state observer is used to generate a
residual current vector (RCV) by comparing the estimated value with the stator current.
To prevent false alarms caused by disturbances, the RCV is separated into different refer-
ence frames to accurately detect and measure the severity of interturn short-circuit faults
in any stator-phase winding. Also, the electrical angular speed is estimated using the sta-
tor voltages, eliminating the need for a speed sensor. Using state observers to detect sensor
faults needs robustness to parameter uncertainties and load variation as they can affect
the residual signal and cause nonzero values under healthy conditions. Based on the dy-
namic characteristics of the EV, using adaptive thresholds can noticeably increase the ef-
ficiency and performance of the FDD [59–61]. It is highly important to design an adaptive
threshold to avoid false or missing alarms.
Figure 8. General diagram of state-observer FDD configuration [62].
The theory of interval observer has introduced fresh concepts for detecting faults and
incorporating them into control. In contrast to the conventional scheme for observer-based
fault detection, the interval observer scheme eliminates the need for designing a residual
evaluator and threshold selector, reducing computational load. An improved interval ob-
server relying on the established mathematical model of the motor was used in [63], which
Figure 7. General diagram of model-based FDD workflow [53].
The
state-observer
method, as one of the most-used techniques with the general
diagram shown in Figure 8, is usually divided into two main subgroups: voltage-based
observer [
54
] and current-based observer [
55
]. The voltage-based methods are fast diagnosis
techniques and can be used to increase the fault detection speed, but usually, extra voltage
sensors are needed. Consequently, adding voltage sensors increases the system’s cost,
volume and complexity, which is regarded as a drawback for FDD techniques [
56
]. A stator

Machines 2023,11, 713 10 of 33
flux linkage (SFL) DC-offset observer is proposed in [
57
] for stator fault detection. It is
analyzed in an antisynchronous reference frame (ASRF) after SFL is estimated in the stator
reference frame and transformed into ASRF. This method is simple and is unaffected by
operating conditions and stator connection type (delta or star). In [
54
], a voltage-based
observer is utilized for robust open fault detection to estimate the converter voltages and
takes advantage of obtaining reference voltage from the control system. In this case, there
is no need for extra hardware, reducing the cost of the FDD method. On the other hand,
as current sensors are usually utilized for motor control, using current-based observers
does not require additional sensors. In [
58
], a current state observer is used to generate a
residual current vector (RCV) by comparing the estimated value with the stator current. To
prevent false alarms caused by disturbances, the RCV is separated into different reference
frames to accurately detect and measure the severity of interturn short-circuit faults in
any stator-phase winding. Also, the electrical angular speed is estimated using the stator
voltages, eliminating the need for a speed sensor. Using state observers to detect sensor
faults needs robustness to parameter uncertainties and load variation as they can affect the
residual signal and cause nonzero values under healthy conditions. Based on the dynamic
characteristics of the EV, using adaptive thresholds can noticeably increase the efficiency
and performance of the FDD [
59
–
61
]. It is highly important to design an adaptive threshold
to avoid false or missing alarms.
Machines 2023, 11, x FOR PEER REVIEW 10 of 36
Figure 7. General diagram of model-based FDD workflow [53].
The state-observer method, as one of the most-used techniques with the general di-
agram shown in Figure 8, is usually divided into two main subgroups: voltage-based ob-
server [54] and current-based observer [55]. The voltage-based methods are fast diagnosis
techniques and can be used to increase the fault detection speed, but usually, extra voltage
sensors are needed. Consequently, adding voltage sensors increases the system’s cost, vol-
ume and complexity, which is regarded as a drawback for FDD techniques [56]. A stator
flux linkage (SFL) DC-offset observer is proposed in [57] for stator fault detection. It is
analyzed in an antisynchronous reference frame (ASRF) after SFL is estimated in the stator
reference frame and transformed into ASRF. This method is simple and is unaffected by
operating conditions and stator connection type (delta or star). In [54], a voltage-based
observer is utilized for robust open fault detection to estimate the converter voltages and
takes advantage of obtaining reference voltage from the control system. In this case, there
is no need for extra hardware, reducing the cost of the FDD method. On the other hand,
as current sensors are usually utilized for motor control, using current-based observers
does not require additional sensors. In [58], a current state observer is used to generate a
residual current vector (RCV) by comparing the estimated value with the stator current.
To prevent false alarms caused by disturbances, the RCV is separated into different refer-
ence frames to accurately detect and measure the severity of interturn short-circuit faults
in any stator-phase winding. Also, the electrical angular speed is estimated using the sta-
tor voltages, eliminating the need for a speed sensor. Using state observers to detect sensor
faults needs robustness to parameter uncertainties and load variation as they can affect
the residual signal and cause nonzero values under healthy conditions. Based on the dy-
namic characteristics of the EV, using adaptive thresholds can noticeably increase the ef-
ficiency and performance of the FDD [59–61]. It is highly important to design an adaptive
threshold to avoid false or missing alarms.
Figure 8. General diagram of state-observer FDD configuration [62].
The theory of interval observer has introduced fresh concepts for detecting faults and
incorporating them into control. In contrast to the conventional scheme for observer-based
fault detection, the interval observer scheme eliminates the need for designing a residual
evaluator and threshold selector, reducing computational load. An improved interval ob-
server relying on the established mathematical model of the motor was used in [63], which
Figure 8. General diagram of state-observer FDD configuration [62].
The theory of interval observer has introduced fresh concepts for detecting faults and
incorporating them into control. In contrast to the conventional scheme for observer-based
fault detection, the interval observer scheme eliminates the need for designing a residual
evaluator and threshold selector, reducing computational load. An improved interval
observer relying on the established mathematical model of the motor was used in [
63
],
which shows better robustness to electromagnetic perturbation and enables incipient ITSF
fault detection.
The
Luenberger observer
is another effective residual observer gaining attention and
improving observer-based FDD techniques. In [
64
], the Luenberger observer is utilized
for encoder fault detection for very low- to high-speed ranges. However, the Luenberger
observer has the drawback of sensitivity to motor parameter variations. To overcome
the nonlinearity of complex systems, the sliding mode control system is widely utilized,
which shows more robustness comparing the Luenberger observer-based methods. The
sliding mode observer for fault detection was first introduced and triggered attention [
65
].
In [
66
], sliding mode observer parameters are selected using linear matrix inequalities
so that the residual signal is affected only by the fault signals. It is used for detecting
PMSM demagnetization faults with high accuracy. In [
67
], a sliding mode observer is
used along with an exact differentiator to estimate the PMSM stator resistor for online
ITSF fault detection. It needs low tuning effort and is applicable for measurements under
noisy conditions.
Parameter estimation
is the other model-based technique used for detecting faults. In
this technique, different motor and inverter parameters, such as current, voltage, back-EMF,
resistance and speed, are estimated based on the system models, and they are considered

Machines 2023,11, 713 11 of 33
the expected healthy values or references. Then, these values are compared to the real
parameter values acquired online from the system. Deviations from the reference values
reveal the fault occurrence. In [
68
], the estimated DC-link current is used as the reference
value and compared with the actual measured value to detect and distinguish single
and multiple sensor and nonsensor faults. Also, the phase signal residual is utilized to
isolate the detected faults. The suggested technique in [
69
] utilizes the resistance and
inductance obtained from online parameter estimation, along with the second harmonic of
control voltage, as fault indicators. In the case of the demagnetization fault, flux linkage is
usually considered a fault indicator, and it is mainly estimated based on d-axis and q-axis
inductances. However, flux density variations under the demagnetization fault result
in PMSM inductance variations that affect the accurate demagnetization fault detection
and severity identification. In [
70
], structural analysis is utilized to estimate the changing
inductance values considering the saturation effect and, consequently, along with the
least square method, to estimate flux linkage to detect and estimate demagnetization fault
severity. In [
71
], a detailed magnetic equivalent circuit (MEC) model was derived, and it
used current, voltage and rotor angular signals to detect an ITSF fault and estimate the
ITSF severity and short-circuit resistance.
The
parity space equation
produces the residual vectors utilizing mathematical equa-
tions using past measurements in a finite period. These residuals are then analyzed to
detect faults. However, they are affected by noise and model uncertainties [72].
Extended Kalman filter (EKF)
is another powerful mathematical algorithm based
on minimizing the variance of estimation error applicable in nonlinear systems used to
estimate motor parameters such as stator current, rotor speed and torque in case of fault
detection. They show robust estimation against noise, have a low false alarm and have good
detection speed. They need the last estimated values and measured signals to estimate the
next step parameters. The Kalman filter can be used for different applications; in [
73
,
74
],
the Kalman filter is used for autonomous driving vehicle state estimation and removing
noise and outliers, and detailed information about the Kalman filter is provided due to
its importance on state estimation, generating residuals and signal innovation. Figure 9
indicates the Kalman filter procedure. In [
75
], EKF is used to estimate the PMSM driver
inverter switches R
on
to detect open-switch faults. The high value of the estimated on-state
switch resistance reveals the open-circuit fault.
Machines 2023, 11, x FOR PEER REVIEW 12 of 36
Figure 9. Kalman filter flowchart [76].
The finite element method (FEM) is a highly effective computational technique for
determining parameters (inductance, flux density and linkage, torque, etc.) of electromag-
netic devices such as motors. It obtains precise results by dividing a large electromagnetic
device into smaller elements and using complex mathematical equations, and it has been
used for detecting PMSM faults, especially eccentricity, demagnetization and ITSF [77,78].
Figures 2 and 3 in [79] show the finite element model of the PMSM.
Model predictive control is a motor drive control technique, which, due to simplicity
and superior performance, is aracting araction. MPC and cost functions have been used
for fault detection recently. MPC for PMSM motor drive can be divided into two catego-
ries based on the control objective: model predictive current control (MPCC) and model
predictive torque control (MPTC) [80], where MPCC shows priority over MPTC due to
less computational efforts and its cost function with no weighting factors, which make it
simpler and more effective [81]. In [82], open-phase fault (OPF) is detected based on a cost
function in a PMSM motor drive with MPCC. The DC component and second harmonic
component in the cost function designed for the current to track the references are in-
volved in fault detection, and the phase angle difference of the stator current is utilized
for locating the fault phase. This method is simple, and the operating condition and pa-
rameter variations do not affect its performance. Twenty-one combinations of open-switch
faults in the inverter of the PMSM motor drive can be detected in the proposed method
[83] based on the cost function and normalized αβ-current characteristics. In the case of
ITSF in a PMSM motor drive with MPC, in [84], the fault signature is revealed by applying
Wavelet transform to the MPC cost function. Table 2 shows a summary of some recent
model-based FDD methods.
Figure 9. Kalman filter flowchart [76].

Machines 2023,11, 713 12 of 33
The finite element method (FEM)
is a highly effective computational technique for
determining parameters (inductance, flux density and linkage, torque, etc.) of electromag-
netic devices such as motors. It obtains precise results by dividing a large electromagnetic
device into smaller elements and using complex mathematical equations, and it has been
used for detecting PMSM faults, especially eccentricity, demagnetization and ITSF [77,78].
Figures 2and 3in [79] show the finite element model of the PMSM.
Model predictive control
is a motor drive control technique, which, due to simplicity
and superior performance, is attracting attraction. MPC and cost functions have been
used for fault detection recently. MPC for PMSM motor drive can be divided into two
categories based on the control objective: model predictive current control (MPCC) and
model predictive torque control (MPTC) [
80
], where MPCC shows priority over MPTC
due to less computational efforts and its cost function with no weighting factors, which
make it simpler and more effective [
81
]. In [
82
], open-phase fault (OPF) is detected based
on a cost function in a PMSM motor drive with MPCC. The DC component and second
harmonic component in the cost function designed for the current to track the references are
involved in fault detection, and the phase angle difference of the stator current is utilized for
locating the fault phase. This method is simple, and the operating condition and parameter
variations do not affect its performance. Twenty-one combinations of open-switch faults in
the inverter of the PMSM motor drive can be detected in the proposed method [
83
] based
on the cost function and normalized
αβ
-current characteristics. In the case of ITSF in a
PMSM motor drive with MPC, in [
84
], the fault signature is revealed by applying Wavelet
transform to the MPC cost function. Table 2shows a summary of some recent model-based
FDD methods.
Table 2. Summary of the reviewed model-based FDD methods.
Method Fault Index Fault(s) Features Ref.
Voltage observer voltage Open circuit No extra hardware needed [54]
Stator flux linkage
DC-offset observer Flux linkage Stator faults
High accuracy
Low computational complexity
Suitable for real-time FDD
[57]
Current observer current ITSF Stationary and transient condition
High accuracy and low false alarm [58]
Luenberger Observer current Open switch
Current sensor fault
Adaptive threshold
Stationary and transient condition
Robust to machine parameter and
load variations
High accuracy and low false alarm
[46]
Current observer current Open switch
Adaptive threshold
Stationary and transient condition
Robust to machine parameter and
load variations
Fast detection
High accuracy
[61]
Luenberger observer Current Encoder fault Different speed range [64]
Sliding mode observer Flux Demagnetization Operating condition independent
Suitable for real-time FDD [66]
Sliding mode observer Resistance ITSF Locating and estimating fault severity [67]
Parameter estimation Current Current sensor fault Multiple sensor fault detection
Robust to motor imbalance [68]
Parameter estimation +
Machine learning
Resistance
Inductance Voltage ITSF Combine model-based and machine
learning for fault detection [69]

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Table 2. Cont.
Method Fault Index Fault(s) Features Ref.
Parameter estimation Flux linkage Demagnetization
Flux linkage estimation based on the
varying inductance to
improve reliability
[70]
Parameter estimation Current Voltage
Rotor angle ITSF Locating and estimating fault severity [71]
EKF Resistance Open switch Fault detection and isolation [75]
FEA Reactance ITSC Incipient fault detection
High computational cost [77]
FEA + ANN Current
Torque Eccentricity Robust to noise [78]
MPC Current Open switch
Fast detection
Single and multiple switch
fault detection
Robust to the motor parameter and
operation condition
[80]
MPC Current Open phase
Robust against operation point and
parameter variations
Simple implementation
[81]
MPC current Open switch
Fast detection
Robust against operation point
Detection of 21 combinations of
open-switch fault in 3-phase inverters
[82]
MPC Voltage ITSF Low computational load [83]
4.2. Signal-Based FDD Methods
Unlike model-based strategies, signal-based methods do not need an accurate system
model. As a result, signal-based FDD approaches show superior performance in complex
systems with inaccurate models and parameter uncertainties. The principle of such methods
is extracting the fault features from the motor output signals, including current, voltage,
magnetic flux density [
85
], torque [
86
], vibration, etc. Different types of faults can cause
changes in output signals from the expected values under healthy conditions. One or more
signals can be chosen as fault indicators based on the fault symptoms. Then, by applying
signal feature extraction techniques to the measured values, the fault features are extracted,
and by comparing them to a reference or threshold, the fault occurrence is detected, and
the type of fault can be identified. Figure 10 presents the summary of signal-based method
workflow in general.
Machines 2023, 11, x FOR PEER REVIEW 14 of 36
4.2. Signal-Based FDD Methods
Unlike model-based strategies, signal-based methods do not need an accurate system
model. As a result, signal-based FDD approaches show superior performance in complex
systems with inaccurate models and parameter uncertainties. The principle of such meth-
ods is extracting the fault features from the motor output signals, including current, volt-
age, magnetic flux density [85], torque [86], vibration, etc. Different types of faults can
cause changes in output signals from the expected values under healthy conditions. One
or more signals can be chosen as fault indicators based on the fault symptoms. Then, by
applying signal feature extraction techniques to the measured values, the fault features
are extracted, and by comparing them to a reference or threshold, the fault occurrence is
detected, and the type of fault can be identified. Figure 10 presents the summary of signal-
based method workflow in general.
Figure 10. General signal-based FDD methods workflow.
Different types of faults can result in the same symptoms, so choosing the proper
signal or signals as fault indicators is crucial. Stator current is needed in most motor drive
controls, so it is always available without the need for extra sensors. Motor current signa-
ture analysis (MCSA) is the mostly used signal-based FDD technique [87]. In this tech-
nique, the stator current is usually transformed to the frequency domain using signal-
processing techniques such as discrete furrier transform (FTT). The frequency domain can
be utilized for fault detection under stationary and steady-state operations. On the other
hand, the EV motor has dynamic nature, so the frequency domain is not applicable during
transient motor operations. Therefore, using time-frequency domain feature extraction
techniques provide the FDD with the capability of fault detection in non-stationary con-
dition and improves the performance and reliability of EV motor FDD. Totally, signal-
processing methods are divided into the time domain, frequency domain and time-fre-
quency domain methods. Hilbert–Huang transform (HHT) [88], continuous and discrete
wavelet transform (CWT and DWT) [89], short-time Fourier transform (STFT), empirical
mode decomposition (EMD) [90] and Winger–Vile distribution are the most-used time-
frequency domain signal-processing methods [91].
Current signal-based methods are widely used for fault detection as a current is easy
and cheap to measure, and it is usually available as it is needed for motor drive control.
MCSA-based diagnosis methods, dq-frame current analysis, negative- and zero-sequence
current and Park’s vector approach are some of the methods. Generally, phase current-
based methods are easy to implement, and there is no need for extra hardware, but they
have a slow detection drawback (at least one fundamental period). In [92], MSCA-based
partial demagnetization fault detection was proposed. In this technique, the additional
even harmonics in the stator current caused by partial demagnetization were taken as
fault indicators. In [93], the zero-sequence current (ZSC) is analyzed for open-switch fault
detection in a dual inverter five-phase PMSM motor drive. ZSC is zero under the healthy
condition, while under open-switch fault, it deviates from zero and is used as the fault
indicator. The ratio of phase current positive sequence to negative sequence is considered
as the open-switch fault indicator in [94] and analyzed using the Fourier series. Different
open-switch faults are detected by seing a proper threshold for this fault indicator. Then
the fault location is revealed using the current DC component. A simple method for open
circuit and current sensor fault detection and identification is proposed in [95] where the
normalized average current is utilized. Comparing other current-based methods, the FDD
proposed in this paper has beer rapidity in fault detection. An approach based on the
mean value of the harmonic of the secondary subspace and current magnitude was
Signal aquisitoiion Com paring with the
reference
Feature extr action Decision makin g
Figure 10. General signal-based FDD methods workflow.
Different types of faults can result in the same symptoms, so choosing the proper
signal or signals as fault indicators is crucial. Stator current is needed in most motor drive
controls, so it is always available without the need for extra sensors. Motor current signature
analysis (MCSA) is the mostly used signal-based FDD technique [
87
]. In this technique,
the stator current is usually transformed to the frequency domain using signal-processing
techniques such as discrete furrier transform (FTT). The frequency domain can be utilized
for fault detection under stationary and steady-state operations. On the other hand, the
EV motor has dynamic nature, so the frequency domain is not applicable during transient