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Received June 15, 2019, accepted June 23, 2019, date of publication July 3, 2019, date of current version July 24, 2019.
DigitalObjectIdentifier10.1109/ACCESS.2019.2926527
A Comprehensive Review of Condition Based Prognostic
Maintenance (CBPM) for Induction Motor
SANJAY KUMAR
1
, (Student Member, IEEE), DEBOTTAM MUKHERJEE
1
,
PABITRA KUMAR GUCHHAIT
2
, RAMASHIS BANERJEE
3
, ANKIT KUMAR SRIVASTAVA
4
,
D. N. VISHWAKARMA
1
, (Senior Member, IEEE), AND R. K. SAKET
1
, (Senior Member, IEEE)
1
Electrical Engineering Department, IIT (BHU) Varanasi, Varanasi 221005, India
2
Electrical Engineering Department, National Institute of Technology at Arunachal Pradesh, Yupia 791112, India
3
Electrical Engineering Department, National Institute of Technology, Silchar 788010, India
4
Electrical Engineering Department, IET, Dr. Rammanohar Lohia Avadh University at Faizabad, Faizabad 224001, India
Corresponding author: R. K. Saket (rksaket.eee@iitbhu.ac.in)
ABSTRACT
This paper presents condition monitoring aspects of induction motor, its present status
with possible mitigation schemes and future maintenance challenges. The induction motors constitute the
major portion of motors in domestic and industrial applications. These motors experience different types
of failures and faults associated with insulation, bearing, stator, rotor, and eccentricity. As a matter of
fact, these faults may subsequently enhance the probability of failures unless proper introspection is not
accomplished. In order to reduce the failure time and operating cost, early detection is indispensable which
necessitates condition-based approach on contrary to scheduled maintenance. The condition monitoring is
a strong candidate to address the diagnosis of machinery failure problems and unreliability. In this context,
a comprehensive analysis is reported in the literature with a focus on different methodologies being addressed
for such objective. Utmost efforts are made to comprehensively analyze in the reported literature in a
sequential manner citing the advantage and limitations in this paper. The authors hopefully described and
illustrated the associated problems with possible mitigation in the context of condition monitoring which
would be immensely helpful for future researchers working in these aspects and the future roadmap would
be clearly reflected.
INDEX TERMS
Condition monitoring, induction motor, bearingless induction motor, fault diagnosis,
artificial intelligence, wavelet techniques, deep learning.
I. INTRODUCTION
Induction machines are the most frequently used electri-
cal machines in domestic and industrial processes. Around
85% of motors used in industrial appliances are induction
machines. The main reason behind it is lower cost, rugged-
ness, robust in structure, lower maintenance requirement,
easiness in availability and capability to work under severe
working atmosphere [1]. The fault in the induction motor
distracts the overall production of the industry, which may
lead to increase the idle time and losses of revenue. In order
to decrease the down time and for reliable and safe operation,
fault recognition in early stage is desirable which necessitates
condition-based monitoring of the induction motor.
The associate editor coordinating the review of this manuscript and
approving it for publication was Xiaodong Sun.
The basic principle of condition monitoring (CM) lies on
investigating the running characteristics of the machine such
that prediction for maintenance is done prior to breakdown or
deterioration to occur in order to introspect the health mon-
itoring of the machine. In this context, the individual part’s
life or the life of the whole machine is critically analyzed.
In this direction the correct data acquiring process and the
data analysis is done in order to capture the trends that might
occur [2]–[4]. The maintenance based on time investigate
the machines repair in offline mode in accordance with time
schedule are working hour that leads to avoid the probability
of failure. However unwanted shutdown or sudden accidents
that may occur in the stipulated period should be taken into
account in order to explore the health of the equipments [5].
Thus fast fault detection in early stage can improve the per-
formance of the motor and reduce the consequential harms,
breakdown repairs, decrease the cost of maintenance and
90690
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VOLUME 7, 2019
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
FIGURE 1.
General Over-view of fault-diagnosis systems.
unpredicted failure risk is remarkably reduced with the avail-
ability of the machine. Accurate identification scheme urges
the methodologies to be implemented in the direction of
condition-based preventive and predictive maintenance rather
than conventional time-based maintenance. In this context,
the focus of condition based repair is to illustrate the eval-
uation accurately and identifies the fault a prior. Condition
based maintenance leads to the set of information about
the machine’s state and focuses CM approach followed by
efficacy of the type of maintenance needed in order to reduce
the manpower. The said scheme would not lead to halt the
machine accidentally [6], [7].
The schematic overview of the block diagram representa-
tion of the fault diagnostic scheme is mentioned in Fig. 1. The
procedure step of the condition monitoring is carried out in a
sequential manner as described in subsequent subsections.
(i) Signal measurement by sensors: Sensors or actuators
are integral components for transformation in order to convert
the physical quantity into an electrical signal. The physical
quantities will be monitored if there is any detectable physical
change in it which can interpret the failure due to incipient
fault much prior to failure leading to catastrophic scenario.
The sensor selection will solely depend upon the monitoring
methods as well as failure mechanism of the machine.
(ii) Data acquisition for subsequent retrieval of features:
Data acquisition is an important unit for pre-processing of
the signals followed by required amplification after the data
is retrieved from the sensors. The communication technology
for data transfer is vital for data acquisition system which
would be realized by microcomputer.
(iii) Fault detection and classification: The prime objective
of the fault detection is to analyze the incipient fault associ-
ated with any part of the machine so that further analysis can
be initiated. Feature extraction and Model-reference based
method are two important methods to accomplish the fault
diagnostic objective. For frequent methodology for extraction
of feature, the signal processing technique based on both time
and frequency would be essential to analyze the signature
in order to discriminate the faulty condition with that of the
healthy condition of the machine.
(iv) Diagnostics for discrimination of healthy and faulty
condition: The abnormal detected signals need to be post
processed for making a clear sign of repairs. Diagnostics is
carried by the experts both in off line as well as online which
can make a clear cut map for the implementation of advanced
technologies in this regards.
Followed by Section I; Section II of this paper describe
various faults that do exist in induction motor with the
probability of its causes and subsequent effects on frequency
spectrum. Further Section III illustrates the different tech-
niques of condition monitoring which would focus on the
fault monitoring associated with induction motor. Section
IV represents advanced fault diagnostic techniques being
implemented for induction motor. Subsequently, Section V
represents the motor fault diagnostic techniques using Arti-
ficial Intelligence and Deep Learning. Finally, Section VI
concludes the concatenation of the various sections and
further
discusses
the
roadmap
of
research
pertaining
to
induction
motor
analysis
with
thrust
of
condition
monitoring.
VOLUME 7, 2019
90691
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
FIGURE 2.
IEEE study results on induction motor faults.
FIGURE 3.
EPRI study results on induction motor faults.
II. INDUCTION MOTOR FAULTS AND
THEIR ROOT CAUSES
In order to introspect the weakest component in electrical
machines, which are susceptible to failure, some statistical
analysis about the failures of the machine are discussed in this
section. Different cases of motor failure have been analyzed
in two different agencies i.e. IEEE and EPRI which are:
(i) An analogous analysis on the vital failure of components of
motors in powerhouse has been studied by IEEE-IGA. This
study is reported by the motor manufacturers. (ii) Beneath
the sponsorship of EPRI about industry assessment, study has
been carried out by General Electric Company. for evaluating
the power house motor’s reliability and also to examine the
different operating characteristics [8]. This study gives the
cause for actual motor faults. The induction machine faults
comparative studies as per EPRI and IEEE standard are men-
tioned in Fig.2 and Fig.3 respectively.
Generally induction machine is susceptible to numerous
failures. The prime sources of induction motor faults due
to internal, external as well as environmental are depicted
in Fig. 4. Internal failures can be distinguished with respect to
their sources, i.e. mechanical and electrical or to their place,
i.e. rotor and stator. Fig. 5 represents the induction motor
fault tree and there causes, where the faults are categorized
according to their location.
These faults have been categorized according to the main
parts of a machine – faults associated with the stator, faults
associated with a rotor, bearing related faults and other faults.
A. BEARING FAULTS
About 42% of induction motor faults are due to failure
of bearing according to studies of the IEEE and EPRI as
depicted in Fig. 2 and Fig. 3. The induction motor bearing
comprises of an outer ring, inner ring and with a set of rolling
elements called balls placed in raceways spinning inside
the rings. The fatigue failures may causes because of the
continual stress on these bearing. Whenever there is failure
in bearing it results in certain vibration which influences
the eccentricity in air gap between the rotor and stator, also
increases the noise levels. Improper insulation, corrosion,
contamination, improper lubrication is the factors that are
also responsible for the bearing failures. Failures that occur in
bearing are cyclic as well as non-cyclic. Based on the position
of failure, the cyclic failures may further be categorize as
inner race defect, outer race defect, cage fault and defects in
ball. Acyclic failure produces an impact amidst raceway and
bearing results in a determinable vibration [9]. The vibration
frequencies produced by these failures can be expressed as
f
o
=
N
b
2
f
r
parenleftbigg
1
−
d
D
cos
∅
parenrightbigg
(1)
f
i
=
N
b
2
f
r
parenleftbigg
1
+
d
D
cos
∅
parenrightbigg
(2)
f
c
=
1
2
f
r
parenleftbigg
1
−
d
D
cos
∅
parenrightbigg
(3)
f
b
=
d
2
D
f
r
bracketleftBigg
1
−
parenleftbigg
d
D
cos
∅
parenrightbigg
2
bracketrightBigg
(4)
where
f
o
is the frequency of outer race,
f
i
is the frequency of inner race,
f
b
is the frequency of ball defect,
f
c
is frequency of cage failure.
N
b
is the number of balls in bearing,
f
r
is rotor speed,
d
is diameter of ball and
D
is pitch diameter. Since the
bearing vibration leads to ripple in output torque of the
motor and therefore the current harmonic spectrum at definite
frequency, the spectrum of the current may also be used to
inspect failures in bearing [10]:
f
cur
=
vextendsingle
vextendsingle
f
i
±
nf
c
vextendsingle
vextendsingle
(5)
where f
cur
is current harmonic, f
i
is source frequency to the
motor; f
c
is characteristic vibration frequency as given in
eq. 5; and n is an integer. Also, it is mentioned in [11] the
bearing faults also produce rotor eccentricity, thus produces
other harmonic components in the spectrum of current signa-
ture. Various causes of the bearing failure are (i) Overload,
tight fits and immense temperature rise, thus strengthening
ball materials as well as races.
They may further destroy the bearing lubrication. (ii)
Fatigue failure: Fatigue failure occurs due to the prolonged
run of the bearings causing the fracture of balls. These types
of bearing failures are catastrophic in nature. It can increase
vibration as well as the level of noise in motor [12]. (iii) Bear-
ings, when exposed to corrosive atmosphere, may deteriorate
the lubricants of the bearing [12]. (iv) Contamination: it is
the most important factor of bearing failure of the motor. The
lubricants get polluted by dirt, other particles which generally
exist in environment of the industries. Contamination may
result in high vibration and wear. (v) Lubrication failure: it is
due to the bearing temperature excessively high; the lubricant
runs out from the bearing. (vi) Misalignment of bearings: due
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S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
FIGURE 4.
Prime sources of induction motor faults.
VOLUME 7, 2019
90693
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
FIGURE 5.
Conventional induction motor faults and their causes.
90694
VOLUME 7, 2019
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
to misalignment of bearings, wear and tear on the surfaces of
races and balls takes place, leads to further temperature rise of
the motor bearings. It is viewed that any faults in the bearing
as discussed above leads to increase in bearing temperature
and further raises the vibration in motors. Bearing vibration
and temperature information states the bearing condition and
therefore machine health [12], [10].
Some authors have proposed bearing less motor in order
to solve the problems related to bearing faults. Authors [13]
have suggested in enhancing the control precision and
dynamic performance of a permanent magnet biased active
magnet bearing system in a magnetically suspended directly
driven spindle system. The 4-DOF PMBAMB as proposed
have inherent character of parameter variation and is more
prone to external disturbances. Novel control architecture
based on neural network inverse and 2 –DOF internal model
control has been put forward. The proposed methodology
when cascaded with the inverse of the 4-DOF original system
gives better tracking; disturbance rejection also mitigates the
effects of unmodelled dynamics improving accuracy outdo-
ing the traditional decentralized PID control scheme. The
authors [14] have put forward an innovative methodology for
bearing-less permanent-magnet synchronous motor on con-
trary to permanent magnet motors prone to bearing failures.
Authors have suggested a unique control scheme incorporat-
ing neural network inverse and 2-degree of freedom internal
control architecture. Conventional control schemes compris-
ing of PID controllers are more prone to get affected by load
disturbances, changes in speed as well as by parameter varia-
tions. The proposed control framework poses better precision
in set point tracking as well as disturbance rejection criteria as
compared to traditional internal controls. Decoupling control
of BPSM is proposed by NNI and 2–DOF internal controllers.
BPSM being mathematically complex to model has been
addressed by model inversion followed by cascading the orig-
inal model creating a decoupled control framework. Tangent
activation function used to train the feed forward neural net-
work by back propagation learning algorithm, prove to make
the system more precise and disturbance free. Authors [15]
have proposed a novel rotor architecture framework including
V shaped permanent magnets so as to enhance the torque
density as well as suspension performance of bearingless per-
manent magnet synchronous motor. The paper also focuses
on the current research on flywheel battery storage for electric
vehicles. Static electrical as well as magnetic characteristics
like inductance and electromagnetic torque have been studied
as well showing low cogging torque as well as large reluc-
tance torque. Finite element method is used for the analysis
of the proposed IBPMSM (Interior bearingless permanent
magnet synchronous motor).Experimental results prove to be
noteworthy in stabilising suspension operation with control
architecture framework of optimized BPMSM. Authors [16]
have put forward the concept of flywheel energy storage
containing a bearingless five phase flux switching permanent
magnet machine consisting of E-core stator. Topology as well
as the structure of the machine is established. Based on the
FIGURE 6.
Different types of failures in stator winding.
trial and error method as well as simple variable the sta-
tor/rotor parameters are optimized. Simulation results based
on the proposed methodology shows an increase in torque by
about 16.7% and suspension forces by 15(130 to 145).The
variations of not only torque but also suspension have shown
a clear indication of diminishing behavior as well.
B. STATOR FAULTS
The most frequent failures in induction motor are the stator
failure. The winding faults associated with stator winding are
often caused by failure of insulation of winding, which leads
to local heating. If unnoticed, this local heating further dam-
age the insulation of stator winding till catastrophic failure
may occur [17].
Stator failure may be categorized as (i) failure in stator
winding (ii) faults in stator frame and (iii) failure in lamina-
tions of the stator core. Among these, stator winding failure
is the most severe failure. As per IEEE and EPRI studies
as shown in Fig. 2 and Fig. 3, faults in stator winding are
about 28-36% of total faults that may occur in the induc-
tion machine. This failure is because of the failure of stator
winding insulation. This fault is also known as short circuit
inter-turn fault. Faults in stator winding are categorized as
(i) short circuit in turns of the same phase (ii)short circuit
between two phases (phase to phase fault) (iii) short circuit
into coils of same phase (coil to coil fault) (iv) short circuit
that occurs in the phases of entire turns (v) open winding
failure when winding get split. Stator winding failures of
different categories are revealed in Fig.6 [12].
Various origins of the faults in the winding of stator are
(i) Mechanical stresses, which are caused by stator coil move-
ment and striking of rotor on the surface of stator [18]. Move-
ment of coil produced the current in the stator coil (since the
force is proportionate to square of current in stator coil) [19]
might destruct the insulation of the copper conductor and
rotor might too strike the stator of the motor due to misalign-
ment of rotor-to-stator / deflection in shaft / failure in bearing
then this striking force leads to puncture of insulation of coil
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S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
TABLE 1.
Life of winding with rise in temperature.
follow on coil to ground failure. Higher mechanical vibration
in stator winding results to open-circuit failure [20].
(ii) Electrical stresses, which are due to transients in
supply because of various faults that exist which is line-to-
ground/line-to-line/three phase fault, circuit breaker open-
ing/closing operation, lightning and drives with variable
frequency [18]. These transient voltages degrade the life of
stator winding; can also cause turn-to-ground/turn-to-turn
failure. (iii) Thermal stresses, which are caused by thermal
overloading are the key root for insulation degradation of
winding of the stator. It happens due to the excess current,
which is in turn caused by continuous overload/unbalance in
supply voltage/ obstructed ventilation/ higher ambient tem-
perature etc. [18]. In presence of unbalance per phase in
supply voltage of 3.5%, the winding which is carrying the
highest current may see a temperature rise of 25% [22].The
winding temperature may also increase due to frequent start
and stop of the machine. It has been shown in [21] and [22]
that the life of insulation of the machine winding may
reduce by half for every 10
◦
C increase above the temper-
ature limit. The change in life of insulation of the winding
with rise in temperature above ambient [22] is depicted in
table 1.
(iv) Environmental stresses are due to the operation of
a motor in conditions which is too cold/humid/hot. Also,
the stator winding insulation may get contaminated in such
operating conditions causing degraded heat dissipation which
may finally reduce the insulation life of the machine [23].
C. ROTOR FAULTS
Rotor cage failure (broken rotor bar/end-ring) occurs around
5–10% of total faults occur in induction machine [24]. There
are numerous reasons for which faults in the rotor may arise
in an induction machine [25]. For medium voltage motors,
faults in cage of the rotor are more prevalent than in case
of motors of small size because of immense thermal stress
on rotor of the machine. Usually rotor broken bar may be
originating by the following reasons: (i) Thermal stresses
which are produced by thermal overloading and overheating
of the rotor thus leading to thermal expansion of the cage.
(ii) Magnetic stresses due to electromagnetic forces as well
as pull due to magnetic unbalance. (iii) Dynamic stresses
owing to torques at shaft.(iv)Environmental stresses because
of contamination in rotor material. (v) Mechanical stresses
caused by loose laminations etc. In medium voltage motors,
broken bar in rotor or failures of end rings are prime cause
of excessive thermal stresses in initial period [26]. Now if
one of the bar break, sidebars will bear larger currents that
may lead to huge thermal and mechanical stresses which are
imposed on these side bars. Now if rotor still rotates, the side
bars get damaged [27] and it may extend, leading to rupture
of multiple rotor bars. These broken bars fault may create a
train of sideband frequencies [28], [29] in the stator current
signature which is shown by eq. 6 below
:
f
br
=
f
in
(
1
+
2
ns
)
(6)
where f
in
is the frequency of supply, slip is s, and n is an inte-
ger. It is shown as a effect of ripple in [29] demonstrates that
the lower side band of frequency f
in
(1–2s) is the mightiest that
may generate ripples in speed and torque at frequency of 2sf
in
,
thus inducing an upper side band at a frequency of f
in
(1
+
2s)
and this outcome will lead to continuation in creation of
the above string of sideband frequencies f
in
(
1
±
2ns
).
The
lower sideband frequency f
in
(1–2s) above the fundamen-
tal one can be employed to monitor the rotor broken bar
fault [30].
D. OTHER FAULTS
Eccentricity arises due to non uniform air-gap amidst the sta-
tor and rotor [28]. This may be generated by bearing defects
or manufacturing failure. Extreme air gap eccentricity may
commence inequitable radial forces and finally results in
friction between stator and rotor, which may lead to severe
damage to core of stator and rotor further causing breakdown
of the motors. Eccentricity in induction machine may be
categorized as static, dynamic as well as mixed air-gap [29].
Inherent static eccentricity is present in recently manufac-
tured motors [31]. In static air gap eccentricity, location of
minimum air gap radial length remains constant where as
for dynamic air-gap eccentricity; it revolves with the speed
of rotor. Static eccentricity and dynamic eccentricity concur
in mixed eccentricity. Literature [30] illustrates that 10 per-
centage air-gap eccentricity maximum is allowed in induction
machine. For medium voltage motors, as the air gap is com-
paratively lesser compared to motors of small size, a minute
eccentricity may cause to an extreme motor failure. Thus in
premature stage the recognition of eccentricity in air gap is
requisite. In the presence of static as well as dynamic eccen-
tricity, which do exists in practical applications, the current
harmonics may be determined at the frequencies [31]–[34] as
given by eq. 7 below
:
f
ef
=
f
bracketleftbigg
1
±
m
parenleftbigg
1
−
s
p
/
2
parenrightbiggbracketrightbigg
(7)
where f
ef
is the frequency related to eccentricity,
f
is the
principal frequency, slip is
s
,
m
is an integer and
p
is the pole
pairs.
III. CONDITION MONITORING TECHNIQUES
There is a challenging task for experts as well as researchers
to analyze unhealthy induction motor performance and its
analysis beneath abnormal condition. Therefore, numerous
90696
VOLUME 7, 2019
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
condition monitoring approaches have been recognized to
identify the health of an induction machine. Various condi-
tion monitoring methods of induction machines have been
discussed in subsequent sub-sections.
A. VIBRATION MONITORING
Vibration monitoring is usually used for detection of mechan-
ical faults like mechanical imbalance or bearing faults [35].
The vibration in the stator frame is related to winding faults
due to inter-turn failure, supply-voltage unbalance and single
phasing. A piezo-electric sensor provides the voltage sig-
nal in proportion to acceleration which is frequently used.
This signal can be used to measure the position or velocity.
An absolute measurement is accomplished by seismic vibra-
tion sensors that are relative to free space [36]. The compara-
tive vibration which is correlative to a stationary point usually
limited to displacement measurement [37], [38].
B. MOTOR CURRENT SIGNATURE ANALYSIS (MCSA)
MCSA is an un-intrusive, online monitoring procedure for
diagnosis of induction motor faults. In major applications
current in stator winding is examined for diagnosis of various
types of faults in induction motor [39]. It is also referred
to as the predictive maintenance tool for the detection of
induction motor failures at burn in stage which leads to
non-occurrence of the catastrophic failures and hence induc-
tion motor life extends. The most popular techniques under
MCSA are (i) current signature analysis (CSA), (ii) volt-
age signature analysis (VSA), (iii) extended Park’s vector
approach (EPPVA), and (iv) instantaneous power signature
analysis (IPSA). MCSA is the process of getting signal like
current and voltage of motor by carrying out signal condi-
tioning. These voltage and current signals are acquired by
a potential transformer (PT) and current transformer (CT).
These acquired data are then examined by the advanced
tools [40], [41] to extract more information from the cap-
tured signals. Numerous faults including core damages, loose
wedges, defective bearings, foundation looseness, shots in
inter turn, static eccentricity, damages in rotor bar as well
as dynamic eccentricity are noticeable by MCSA technique
without offensive the motor operation [42]. The faults which
can be analyzed by MCSA are (i) static and / or dynamic air
gap irregularities, (ii) broken rotor bar / cracked rotor end
rings, (iii) stator faults (opening or shorting of one coil or
more of a stator phase winding), (iv) abnormal connections
of the stator windings, (v) bent shaft (akin to dynamic eccen-
tricity) results in a friction between stator and rotor, causing
severe harm to the core of the stator as well as its windings,
and (vi) bearing and gearbox failures.
C. TORQUE MONITORING
In a rotating machine, torque is created by both the current as
well as flux linkage is defined as air gap torque. Torque mon-
itoring is used in cement, marine and power industries. This
technique provides a clear understanding to the users when it
is difficult to identify a problem through standard vibration
analysis. Different kind of failures in induction machine
produces harmonics that contains distinct frequencies in the
air-gap torque. Rotor, shaft, and moreover mechanical load-
ing on rotating machine maps to a spring system in analogous
that has its unique inherent frequency, any attenuation in air
– gap torque imparted through this spring system are distinct
for distinct harmonic orders of components of torque. Air-gap
torque which is delicate to any asymmetry created by faults as
well as by unbalance in voltages in the supply system. Air-gap
torque reflects specifically whether the unbalance is produced
by cracked rotor bars or by stator unbalance linked with
defects in winding and unbalanced voltages. Air-gap torque
can be measured in the operating condition of the motor. This
is majorly used in industries, where an unexpected motor
down time leads to immense loss of production [43]–[45].
D. TEMPERATURE MONITORING
Electrical motor’s thermal monitoring can be accomplished
with the measurements of motor’s local temperature. These
can also be done by parameter estimation of the motor. In case
of stator winding shorted turns of motor, the current in the
stator will be tremendously high, and thus generates excessive
heat, unless appropriate action is not taken in time can results
into motor winding failure. Few researchers introduced the
thermal model (Finite element analysis and Lumped parame-
ter based model) of electrical machine. Finite element analy-
sis is more appropriate than lumped parameter based model,
but it is highly time consuming and has more computational
burden [46], [47]. The lumped parameter based model which
is analogous to thermal network and is comprised of capac-
itances, thermal resistances, and there relative power losses.
In case of inter turn failure, the increase in temperature in
the neighborhood zone of failure, but this might be very slow
to determine the incipient failure before it propagates into
a catastrophic failure [48], [49]. The temperature estimating
methods have already been used for stator fault and bearing
fault detection objective. This technique gives a meaningful
sign of over-heating of the machine but this approach has
limited fault analysis potential [50].
E. NOISE / ACOUSTIC NOISE MONITORING
Noise in electrical machines is due to the combination of
sound signal which is generated by the fast changes in
air pressure. These modifications generate most commonly:
(i) Machine parts vibration on the whole surface of the
machine (ii) Aerodynamic phenomena which leads to vibra-
tion of pressure near the motor. The prime sources of noise in
induction motors are represented in Fig.7.
The spectrums of noise in induction motors are predomi-
nant by electromagnetic, acoustic noise and ventilation. Air
turbulence produces ventilation noise. Periodic disturbance
in air pressure is the cause of air turbulence. Air pressure
is caused owing to rotating parts of induction machine. The
electromagnetic noise is generated due to the influence of
Maxwell’s stresses acting on surfaces of the iron of the
machine parts in the existence of the magnetic field. As a
VOLUME 7, 2019
90697
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
FIGURE 7.
Prime sources of noise in induction motors.
matter of fact magnetic force produces vibrations in the struc-
ture of the stator, which leads to radiated noise. The sound
level owing to mechanical and aerodynamic noise rise at a
rate of twelve decibel per doubling the speed of the machine.
Increased motor speed furnishes in growth of electromag-
netic noise [51]. Inspection of the insulation of the ground
wall is done by most appropriately embarking an ultrasonic
wave upon the stator bar and utilizing the conductor like a
waveguide [52]. Acoustic noise can be estimated by the noise
monitoring from induction machine air-gap eccentricity. It is
seen from the review of the literature that noise monitoring
technique is less effective as in contrast to other monitoring
approaches.
F. SPEED FLUCTUATION MONITORING
The speed fluctuations monitoring is a technique that can
detect failure or defects using speed monitoring technique
in the operational time span of the rotating machines. It is
used for identification of faults in the rotor, vibration, air-gap
eccentricity, asymmetries in rotor, damaged bearing etc.
There are four types of sensor-less speed monitoring schemes
which are (i) speed estimator, (ii) modal reference adaptive
system, (iii) Luenberger speed observer, and (iv) Kalman
filter technique [29]. In normal rotor bar, current fluctuates
periodically in the form of sine wave with slip frequency, pro-
vide a contribution which will be developing the torque which
will vary periodically in the form of sine wave with twice
of slip frequency [53]. Shaft torque cannot be contributed by
the defective rotor bar. For the rotor, the resulting torque can
be divided into two components in which one is constant;
another one varies with twice of the slip frequency. Mostly,
the induction motor has variable load torque; the used instru-
ments are capable of differentiating between load variations
and variations of twice of the slip frequency showing rotor
faults in induction motor [54].
G. INDUCED VOLTAGE MONITORING
The reasons of failures of rotating machines are (i) man-
ufacturing tolerance (ii) origin in design (iii) assembly
(iv) installation (v) working atmosphere (vi) schedule of
maintenance (vii) nature of the load imposed on the rotating
machine. Induction motor which is similar to other electrical
rotating motors is also subjected to electromagnetic as well
as mechanical forces [55]. So, the design and improvement
of the induction machine leads to the interaction between
these two forces under equilibrium states giving noiseless
operation. While in the case of a fault, the equilibrium is lost
that leads to further enhancement of the fault [56].
The voltage induced in healthy motor produces minimum
noise/vibrations, whenever any failure or fault occurs in
induction machine; the rotating motor shaft provides the
information to the winding or stator core [57]. The induced
rotor voltage parameter has not yet given significant results
for condition monitoring due to its non-reliability and com-
plexity.
H. SURGE TESTING
Winding failure can also be identified by surge testing tech-
nique. In this surge comparison test technique, two similar
pulses of high voltage and frequency are concurrently applied
on the winding of the two phases with third phase of the
motor being grounded. An oscilloscope has been used for the
indication of insulation failure between the windings, coils
and group of the coils by comparing reflected pulses [63].
Pulse-pulse surge test method gives predictive information to
identify the insulation deformation before the occurrence of
turn-turn short circuit. The ‘‘Surge Tester’’ which is a handy
and electronic piece of equipment used to find the insulation
failure and dissymmetry of winding [58], [59], [62].
I. MAGNETIC FLUX MONITORING
The air-gap magnetic flux of an induction machine during
healthy condition changes sinusoidally with time as well as
space. If there is any occurrence of fault in rotor / stator,
there may be variations in the air-gap flux [63]. The rotor
faults can be identified by using a search coil which will
be connected to the stator. The variations in the air-gap flux
density which is caused by the stator or rotor produces a
flux axially which can be investigated by a measuring coil
around the rotating shaft either by other sensors [64]. The
monitoring of the axial leakage of flux is probable to identify
the different asymmetries and abnormalities such as related
problems associated with stator inter-turn failure, eccentricity
faults broken rotor bars, misalignment etc. [65].
J. PARTIAL DISCHARGE
Imperfection in insulation produces a little electrical dis-
charge. Like delimitations within the insulation of the ground
wall, resulting from excessive heating or poor manufac-
turing may lead to air pockets or voids, that may get
discharged [66]–[72]. It is useful in recognition of the
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: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
insulation problem in induction motor for precaution before
the catastrophic failure occurs. This concept of PD involves
material analysis, arcing characteristics, electrical fields,
propagation of pulse wave as well as its attenuation, spatial
sensor sensitivity, data and noise interpretation. The partial
discharge analyzer (PDA) has evolved as the first technique
which was used for the period of healthy machine (hydro
generators) operation. Another method has been evolved by
the use of dedicated sensors. The PD activity in a dete-
riorated winding has around thirty times or even more in
comparison to a winding which is in healthy state. So PD
is an incredibly beneficial practice to inspect the winding
effectiveness as well as the motor health [73]. With online
PD test [74]–[77] the stator winding effectiveness can be
easily monitored. PD influences the motor which is a cause
of long term degradation and failure of stator-rotor/electrical
machine insulation. Rupture of stator winding insulation is
one of the major causes of large sized IM rated 6 kV or above.
K. MOTOR CIRCUIT ANALYSIS
Motor circuit analysis by detecting the electro-magnetic
behavior of the machine analogous to an electric circuit,
identifies the fluctuations inside the motor as well as detects
the faults. In this analysis a little quantity of energy with
magnified outcome is imposed. The outcomes evaluate the
state of the rotor as well as windings by the comparative
observations [78]–[80].
L. GAS ANALYSIS
The carbon monoxide is produced owing to the degradation of
electrical insulation which passes to the cooling circuit con-
taining air in it. The carbon monoxide is detected by infrared
absorption method [36]. The pulse width modulated pulses
which are of high frequency generate too much peaks of the
voltage that leads to the beginning of insulation breakdown of
the motor. It happens due to electrostatic fields which are sur-
rounded by opposite polarized electrical conductors, begins
to strip electron from that neighboring air gap and leaves
the positive charge generating ozone gas which reacts with
the nitrogen present in air, further produces various forms
of oxides of nitrogen. Nitrous oxide attacks the insulation
of the winding that causes animosity and finally leading to
fracture. The Ozone sniffing methods have been used for
finding ozone [81]–[83].
IV. ADVANCED DIAGNOSTIC TECHNIQUES
The very fast evolution in the area of very-large-scale inte-
grated (VLSI) design in addition with the development in the
area of parallel computing architectures has rendered on-line
digital signal processing (DSP) feasible for fault recognition
in motor. The technique of digital signal processing habitually
needed for the acquisition of sensor data and renovate rate
for meticulous prediction of motor failures. This technique
for diagnostics of faults is normally categorized as para-
metric, non parametric as well as high resolution spectrum
investigation. Non-parametric approaches are the classical
approaches that actuated by assessment the autocorrelation
series of specific data, followed by the assessment of spec-
trum of the power by applying Fourier Transform. Similarly,
the Fast Fourier Transform, which is an effective technique
for computation, gives a conceptually easy method for induc-
tion machine current signature analysis [84]. In parametric
approaches, a process model is specified by the enough
preliminary knowledge and from these processed data, the
model parameters are evaluated. Lastly by the use of these
calculated parameters the power spectrum is then estimated.
The autoregressive (AR), moving average (MA) as well
as an autoregressive moving average (ARMA) models are
persistent implementation for the presentation of the time
series data. Since the parameters which are estimated are
not significant it is efficacious to store or transmit rather
signal values. This signal then reforms from the use of these
parameters. There are numerous applications like cement
industry processes, steel rolling mills etc., where the point
of operation of the induction motor is oscillating. This leads
to be an extremely dynamic signal like the voltage, current,
voltage and power signals. The short time Fourier trans-
forms (STFTs) has been used to process these non-stationary
signals. STFT along with the pattern-recognition techniques
has been accomplished for detecting the induction motor
failures in such oscillating conditions. Simulation results
reflect the methodology, best suited for discriminating mul-
tiple attribute of frequencies. CWT technique has been used
to detect bearing fault in induction machine [85]. The main
advantage of CWT is its applicability to extract information
with high resolution and redundancy. In other words, scales
of narrow range can be utilized to extract information from
the band of a particular frequency. Very encouraging results
are obtained with the scope of further applying this technique
for recognition of other induction machine fault types. The
support vector machine (SVM) is proven to be the best clas-
sification technique and this fact can be leveraged by using a
hybrid CWT-SVM as a more efficient alternative to conven-
tional classification techniques such as DWT/ANN. Wavelet
analysis is another important technique for fault detection
objective. STFTs have been used for analyzing signals which
are non-stationary in a window of short signal as discussed
earlier. In contrast to this in wavelet analysis non-stationary
signals are concomitantly analyzed in time as well as in fre-
quency domain at contrasting resolutions. STFT’s application
assumes the analysis to be pseudo stationary like speed of the
motor and load on contrast to non-stationary signal. Precise
detection of faults are associated with frequency has been
accomplished by the use of finite impulse response (FIR)
filter bank collectively with the spectrum of high-resolution
exploration [86]. For estimating the dynamics of stationary
signal large window has been utilized, on the other hand
for transients smaller window has been used. This multi-
resolution or multi scale view of the signal is the basis of the
wavelet analysis [87]. Starting transient currents in induction
machine have associated with the Non-stationary signals.
Wavelet analysis by using the start-up transient current has
VOLUME 7, 2019
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S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
proven its effectiveness for the fault detection at no load
operation of the induction motor [88], [89]. Analytic wavelet
transform (AWT) is an elegant algorithm which is capable of
identifying and tracking the frequency of the faulted signal.
The analytical wavelet ridge recognition [90] acquires the
signal of small magnitude at the higher frequencies where
the information of phasor from the AWT assists the tracking
of the faulty signal of stator. Another effective technique
is the combination of both wavelet as well as method of
power-spectral-density, which is competent for recognition of
failures such as rotor broken bar as well as eccentricity, occurs
in an induction machine [91], [92]. The other diagnostic tech-
nique using wavelets, and signature of fault in frequency band
inter relate with the frequency of supply, Time Synchronous
Averaging (TSA) algorithm with DWT can give efficient
result for detection of induction motor rotor faults [93].
V. MOTOR DIAGNOSTIC USING ARTIFICIAL
INTELLIGENCE AND DEEP LEARNING
Induction motor incorporates the advantage of Neural Net-
work, Fuzzy logic, combination of Neuro and Fuzzy and
some optimization techniques like genetic algorithm, which
are model free techniques. Thus, the incorporation of such
algorithms in the system with mapping of input/output with-
out many expressions of dynamics and control of the sys-
tem is to be monitored. In [94] the detection of broken
bar and eccentricity has been implemented by virtue of
Artificial Neural Network (ANN). An ANN comprising of
three layered back propagation architecture, trained by a
Levenberg–Marquardt learning algorithm. The input signal
to this network is filtered vibration signal. The trained neural
network has been validated by using the known set of training
data and residuals are obtained by using ANN output and the
data to be monitored. Depending on set residual thresholds,
triggering the indication of faults by means of these residues
is obtained. The learning algorithms technique based on
clustering with combination of ANN has been implemented.
It has been shown in another technique, which is based on the
behavior implement methods to have the assessment criterion
in a similar manner based on clustering by means of K-means
algorithm [95]. NN structure has been proposed in [96] for
detection of bearing failure for induction motors connected
with the grid. The suggested method examines the state in
real time like voltage unbalance and torque varying condition.
The proposed technique gives the effective result by the use of
high-speed personal computer for experimental test in online
mode. A novel technique has been demonstrated in [97] based
on hybrid feature reduction technique, gives a remarkable
processing of vibration signature which is obtained from the
motor. The proposed method has been used to recognize the
multiple failures in an induction machine by some sequential
tasks i.e. signal decomposition, features estimation based
on statistical time, by using genetic algorithm based feature
optimization, integrating the principal component analysis,
feature selection (by means of Fisher score analysis), fea-
ture extraction. The ANN based classifier has been used
to identify the various types of failures. The effectiveness
has been verified experimentally and the results have been
compared with traditional reduction schemes, making the
suggested methodology worthy for industrial applications.
Authors have suggested ANN [98] based architecture for
recognizing the induction motor failures in early stage. This
proposed system comprises of feature extraction and classi-
fication within a single body system, has an ability to grasp
the most appropriate features by the convenient training. The
proposed method can also detect the electrical as well as
mechanical failures with the aid of electrical quantity (cur-
rent) and mechanical vibration signal. For combining the fea-
ture extraction and classification both, the authors have used
1-D CNN (convolution neural networks) for detecting the
failures in a single learning framework. A deep learning based
scheme have been demonstrated in [99] for online detection
of bearing failure. The authors considered the practical failure
occurrence, using the outer raceway scratch on the bearing.
For characterization of the fault, a Convolution Neural Net-
work (CNN) architecture is applied. Fast Fourier transform
has been applied on the signature of the stator current, fol-
lowed by training of the CNN with the aid of specific fre-
quency component from feature extraction. The effectiveness
of this scheme has been verified by experimental tests corre-
sponding to different fault conditions of the bearing and has
also been tested for detection of multiple failures. Similarly
Self Organizing Map which incorporates radial basis function
is implemented to visualize both electrical and mechanical
faults at different operating speed. In this algorithm the RBF
neuron with varying nature becomes an elegant to implement
in a architecture in accordance with availability of input data
which accomplishes the fault detection objective accurately
along with its severity [100]. Similarly principal component
analysis (PCA) [101] illustrates the feature selection scheme
based on the dimensionality reduction. The efficacy of the
method has been implemented in a test bed for introspection
of bearing fault in induction motor. By virtue of implemen-
tation of PCA relevant features can be extracted for machine
condition monitoring.
On the other hand fuzzy logic is also implemented for
condition monitoring objective of induction motor. Adap-
tive neuro–fuzzy inference systems (ANFIS), wavelet fuzzy
logic, wavelet packet transform (WPT), support vector
machine (SVM) are some important intelligent algorithms
which is more suitable in order to predict the motor failure.
On contrary the growth model associated with the noise
modeling manifests Markov model with higher order terms.
Similarly fuzzy logic concatenated with wavelet techniques
constitutes an important tool for health monitoring of induc-
tion motor. The rule based technique which is an impor-
tant segment of fuzzy logic membership function has been
implemented in order to investigate the condition monitoring.
The dry bearing fault also occurs in induction motor due
to in adequacy of the lubrication. Thus, the vibration signal
randomly increases the probability of dry bearing fault on
contrary to normal motor unless correctly analyzed. In this
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: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
context the analysis of this vibration signal is carried out by
means of wavelet transform [102] in order to augment the
health of the motor in presence of dryness in bearing in the
induction motor. Such analysis is carried out both in offline
by virtue of C
++
as well as online by MATLAB platform
respectively. Similarly support vector machine (SVM) [103]
for fault detection and classification objective for induction
motor is explored by use of kernel function supplemented by
wavelet for addressing multi-class classification. For train-
ing objective feature vectors are obtained from the current
transient signal which is pre-processed with DWT PCA and
kernel PCA. Fault detection objective for induction motor is
analyzed by the initiation of transient signal associated with
current that exploits the advantage of fusion of wavelet and
decision tree in order to improve the accuracy [104]. WPT is
the extended version of wavelet which uses the basis function
and resolution both in time and frequency [105]. This can
extract the signal feature retaining the characteristics of both
stationary and non stationary signal. The obtained output
from the WPT is applied with a combination of fuzzy logic
in order to generate the feature vectors after normalization
and storing the relevant pattern is obtained from the data
from experimentation. The comparative assessment of the
feature vector is carried out with real time data with that
of the pattern being stored in the memory. Since WPT is a
strong feature extraction technique, it has been incorporated
with a combination of SVM in order to interpret the severity
assessment, detection of fault and detection of composite
fault with higher accuracy [106], [107]. Similarly the concept
of WPT is implemented in [108] for stationary stator current
in order to explore the different fault condition which has
been verified in the experimental test bed. Deep belief net-
work (DBN) which incorporates the model based on deep
learning has been presented in [109], the input is data mea-
sured with frequency distribution for induction motor fault
diagnosis in manufacturing. Restricted Boltzmann machine
has been incorporated in the deep architecture, which uses
the training based on greedy layer for the construction of
the model. As a matter of fact, DBN becomes a strong
candidate to model the data of higher dimension in multi
layer learning representation. This approach has reduced the
training error and improved the accuracy of classification.
Experimental results interpret the efficacy of the model based
on DBN, which provides the right direction of extraction
of features for fault diagnostic in manufacturing industry.
A novel method for nonlinear model of flux linkage of bear-
ingless induction motors (BIM) have been proposed in [110].
For improving the accuracy of the flux linkage model, least
square support vector machine (LSSVM) has been used
with gray wolf optimization (GWO) technique. The rela-
tion between input and output of the proposed nonlinear
flux linkage model has been studied, and precision model
of GWO-LSSVM flux linkage have been obtained. Simula-
tion results verify that the proposed model have its unique-
ness including high accuracy as well as strong ability of
prediction.
VI. CONCLUSION
The condition monitoring of induction motor as discussed
in the previous sections in an elaborated manner has evoked
the machine diagnostic procedure, analysis of data, commu-
nications, and management of information. Exploration of
these techniques would make the right platform for engineers
and researchers in order to get acquaintance in automated
monitoring systems switched to diverse industry deploy-
ment circumstances which have not been earlier reported.
In addition to the same comprehensive reviews of various
faults, their causes, monitoring techniques, and development
in premises, cloud, and internet of things of this in industrial
automated systems are reported in this paper. The compre-
hensively reviewed methods and techniques would require
further analysis for different technologies associated with the
objective of security. As a matter of fact, the augmentation of
the security would lead to minimum cost and deployment in
large scale for future condition monitoring automation system
of induction motors.
ACKNOWLEDGMENT
The authors are grateful to the Department of Electrical
Engineering, Indian Institute of Technology (Banaras Hindu
University), Varanasi (UP) India for laboratory facilities to
complete this research work. The authors would like to thank
Dr. Soumya Ranjan Mohanty, Associate Professor, Depart-
ment of Electrical Engineering, IIT (BHU), Varanasi (UP)
for his valuable suggestion throughout the preparation of this
manuscript.
REFERENCES
[1]
D. J. T. Siyambalapitiya and P. G. Mclaren, ‘‘Reliability improvement
and economic benefits of online monitoring systems for large induc-
tion machines,’’
IEEE Trans. Ind. Appl.
, vol. 26, no. 6, pp. 1018–1025,
Nov./Dec. 1990.
[2]
G. Prince and M. Shortland, ‘‘Condition monitoring: The automated man-
ufacturing perspective,’’ in
Proc. Int. Conf. Condition Monit.
, Brighton,
U.K., May 1986, pp. 94–95.
[3]
J. C. Steed, ‘‘Condition monitoring applied to power transformers—An
REC experience,’’
IEE Colloq. Condition Monit. Large Mach. Power
Transformers
, vol. 4, no. 1, pp. 411–420, 1997.
[4]
H. W. Whittington and B. W. Flynn, ‘‘High reliability condition monitoring
systems,’’
Brit. J. Non-Destructive Test.
, vol. 35, no. 11, pp. 648–654, 1993.
[5]
Y. Han and Y. H. Song, ‘‘Condition monitoring techniques for electrical
equipment—A literature survey,’’
IEEE Trans. Power Del.
, vol. 18, no. 1,
pp. 4–13, Jan. 2003.
[6]
P. J. Tavner and J. Penman,
Condition Monitoring of Electrical Machines
.
Hertfordshire, U.K.: Research Studies Press, 1987.
[7]
H. T. Grimmelius, P. P. Meiler, H. L. M. M. Maas, B. Bonnier, J. S. Grevink,
and R. F. van Kuilenburg, ‘‘Three state-of-the-art methods for condition
monitoring,’’
IEEE Trans. Ind. Electron.
, vol. 46, no. 2, pp. 407–416,
Apr. 1999.
[8]
P. F. Albrecht, J. C. Appiarius, R. M. McCoy, E. L. Owen, and
D. K. Sharma, ‘‘Assessment of the reliability of motors in utility
applications—Updated,’’
IEEE Trans. Energy Convers.
, vol. EC-1, no. 1,
pp. 39–46, Mar. 1986.
[9]
L. Frosini and E. Bassi, ‘‘Stator current and motor efficiency as indicators
for different types of bearing faults in induction motors,’’
IEEE Trans. Ind.
Electron.
, vol. 57, no. 1, pp. 244–251, Jan. 2010.
[10]
R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartfield, ‘‘Motor
bearing damage detection using stator current monitoring,’’
IEEE Trans.
Ind. Appl.
, vol. 31, no. 6, pp. 1274–1279, Nov. 1995.
VOLUME 7, 2019
90701
Your preview ends here
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S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
[11]
M. Blodt, P. Granjon, B. Raison, and G. Rostaing, ‘‘Models for bearing
damage detection in induction motors using stator current monitoring,’’
IEEE Trans. Ind. Electron.
, vol. 55, no. 4, pp. 1813–1822, Apr. 2008.
[12]
P. Eschmann, L. Hasbargen, and K. Weigand,
Ball and Roller Bearings:
Theory, Design and Application
. London, U.K.: K. G. Heyden, 1958.
[13]
X. Sun, B. Su, L. Chen, Z. Yang, X. Xu, and Z. Shi, ‘‘Precise control of a
four degree-of-freedom permanent magnet biased active magnetic bearing
system in a magnetically suspended direct-driven spindle using neural
network inverse scheme,’’
Mech. Syst. Signal Process.
, vol. 88, pp. 36–48,
May 2017.
[14]
X. Sun, L. Chen, H. Jiang, Z. Yang, J. Chen, and W. Zhang, ‘‘High-
performance control for a bearingless permanent-magnet synchronous
motor using neural network inverse scheme plus internal model con-
trollers,’’
IEEE Trans. Ind. Electron.
, vol. 63, no. 6, pp. 3479–3488,
Jun. 2016.
[15]
X. Sun, B. Su, S. Wang, Z. Yang, G. Lei, J. Zhu, and Y. Guo, ‘‘Performance
analysis of suspension force and torque in an IBPMSM with V-shaped PMs
for flywheel batteries,’’
IEEE Trans. Magn.
, vol. 54, no. 11, Nov. 2018,
Art. no. 8105504.
[16]
X. Sun, Z. Jin, S. Wang, Z. Yang, K. Li, Y. Fan, and L. Chen, ‘‘Perfor-
mance improvement of torque and suspension force for a novel five-phase
BFSPM machine for flywheel energy storage systems,’’
IEEE Trans. Appl.
Supercond.
, vol. 29, no. 2, Mar. 2019, Art. no. 0601505.
[17]
V. V. Thomas, K. Vasudevan, and V. J. Kumar, ‘‘Implementation of
online air-gap torque monitor for detection of squirrel cage rotor faults
using TMS320C31,’’ in
Proc. Int. Conf. Power Electron., Mach. Drives
,
Jun. 2002, pp. 128–132.
[18]
A. Siddique, G. S. Yadava, and B. Singh, ‘‘A review of stator fault monitor-
ing techniques of induction motors,’’
IEEE Trans. Energy Convers.
, vol. 20,
no. 1, pp. 106–114, Mar. 2005.
[19]
S. B. Lee, R. M. Tallam, and fT. G. Habetler, ‘‘A robust, on-line turn-
fault detection technique for induction machines based on monitoring the
sequence component impedance matrix,’’
IEEE Trans. Power Electron.
,
vol. 18, no. 3, pp. 865–872, May 2003.
[20]
J. Aguayo, A. Claudio, L. G. Vela, S. Lesecq, and A. Barraud, ‘‘Sta-
tor winding fault detection for an induction motor drive using actuator
as sensor principle,’’ in
Proc. IEEE 34th Annu. Conf. Power Electron.
Spec. (PESC)
, Acapulco, Mexico, vol. 3, Jun. 2003, pp. 1347–1351.
[21]
T. A. Lipo,
Introduction to AC Machine Design
, 2nd ed. Madison, WI,
USA: Wisconsin Power Electronics Research Center, 2004.
[22]
A. H. Bonnett and G. C. Soukup, ‘‘Cause and analysis of stator and rotor
failures in three-phase squirrel-cage induction motors,’’
IEEE Trans. Ind.
Appl.
, vol. 28, no. 4, pp. 921–937, Jul./Aug. 1992.
[23]
M. A. Cash, ‘‘Detection of turn faults arising from insulation failure in the
stator windings of AC machines,’’ Ph.D. dissertation, Dept. Elect. Comput.
Eng., Georgia Inst. Technol., Atlanta, GA, USA, 1998.
[24]
A. H. Bonnett and G. C. Soukup, ‘‘Rotor failures in squirrel cage induc-
tion motors,’’
IEEE Trans. Ind. Appl.
, vol. IA-22, no. 6, pp. 1165–1173,
Nov. 1986.
[25]
P. Vas,
Parameter Estimation, Condition Monitoring, and Diagnosis of
Electrical Machines
. Oxford, U.K.: Clarendon Press, 1993.
[26]
M. F. Cabanas, J. L. R. Gonzalez, J. L. B. Sampayo, M. G. Melero,
C. H. Rojas, F. Pedrayes, A. Arguelles, and J. Vina, ‘‘Analysis of the
fatigue causes on the rotor bars of squirrel cage asynchronous motors:
Experimental analysis and modelling of medium voltage motors,’’ in
Proc.
4th IEEE Int. SDEMPED
, Aug. 2003, pp. 247–252.
[27]
P. J. Tavner and J. Penman,
Condition Monitoring of Electrical Machines
.
Hertfordshire, U.K.: Research Studies Press, 1987.
[28]
W. Deleroi, ‘‘Broken bars in squirrel cage rotor of an induction motor—
Part I: Description by superimposed fault currents,’’
Arch Elektrotech.
,
vol. 67, no. 2, pp. 91–99, Mar. 1984.
[29]
F. Filipetti, G. Franceschini, C. Tassoni, and P. Vas, ‘‘AI techniques in
induction machines diagnosis including the speed ripple effect,’’
IEEE
Trans. Ind. Appl.
, vol. 34, no. 1, pp. 98–108, Jan./Feb. 1998.
[30]
A. Bellini, C. Concari, G. Franceschini, E. Lorenzani, C. Tassoni, and
A. Toscani, ‘‘Thorough understanding and experimental validation of
current sideband components in induction machines rotor monitoring,’’
in
Proc. 32nd Annu. Conf. IEEE Ind. Electron. (IECON)
, Nov. 2006,
pp. 4957–4962.
[31]
D. G. Dorrell, W. T. Thomson, and S. Roach, ‘‘Analysis of airgap flux,
current, and vibration signals as a function of the combination of static
and dynamic airgap eccentricity in 3-phase induction motors,’’
IEEE Trans.
Ind. Appl.
, vol. 33, no. 1, pp. 24–34, Jan./Feb. 1997.
[32]
S. Nandi, R. M. Bharadwaj, and H. A. Toliyat, ‘‘Performance analysis of
a three-phase induction motor under mixed eccentricity condition,’’
IEEE
Trans. Energy Convers.
, vol. 17, no. 3, pp. 392–399, Sep. 2002.
[33]
A. M. Knight and S. P. Bertani, ‘‘Mechanical fault detection in a medium-
sized induction motor using stator current monitoring,’’
IEEE Trans.
Energy Convers.
, vol. 20, no. 4, pp. 753–760, Dec. 2005.
[34]
X. Li, Q. Wu, and S. Nandi, ‘‘Performance analysis of a three-phase
induction machine with inclined static eccentricity,’’
IEEE Trans. Ind.
Appl.
, vol. 43, no. 2, pp. 531–541, Mar. 2007.
[35]
B. Martin, ‘‘Mechanical signature analysis,’’ Ph.D. dissertation, School
Mech. Eng. Lect. Notes, Univ. Adelaide, Adelaide, Australia, 2003.
[36]
P. J. Tavner, B. G. Gaydon, and D. M. Ward, ‘‘Monitoring generators
and large motors,’’
IEE Proc. B-Electric Power Appl.
, vol. 133, no. 3,
pp. 169–180, May 1986.
[37]
A. Chiba, T. Fukao, and M. A. Rahman, ‘‘Vibration suppression of a
flexible shaft with a simplified bearingless induction motor drive,’’ in
Proc. IEEE Ind. Appl. Conf. 41st IAS Annu. Meeting
, vol. 2, Oct. 2006,
pp. 836–842.
[38]
S. Daley, J. Hätönen, and K. Tammi, ‘‘Instantaneous harmonic vibration
control of a flexible rotor control of sound and vibration,’’ in
Proc. Inter.
Symp.
, 2006, pp. 18–20.
[39]
N. Mehla and R. Dahiya, ‘‘An approach of condition monitoring of induc-
tion motor using MCSA,’’
Int. J. Syst. Appl., Eng. Develop.
, vol. 1, no. 1,
pp. 13–17, 2007.
[40]
G. B. Kliman and J. Stein, ‘‘Methods of motor current signature analysis,’’
Electr. Mach. Power Syst.
, vol. 20, no. 5, pp. 463–474, 1992.
[41]
R. C. Kryter and H. D. Haynes, ‘‘Condition monitoring of machinery using
motor current signature analysis,’’
Sound Vib.
, vol. 23, no. 9, pp. 14–21,
Sep. 1989.
[42]
A. Yazidi, H. Henao, G. A. Capolino, M. Artioli, F. Filippetti, and
D. Casadei, ‘‘Flux signature analysis: An alternative method for the fault
diagnosis of induction machines,’’ in
Proc. IEEE Russia Power Tech
, St.
Petersburg, Russia, Jun. 2005, pp. 1–6.
[43]
J. S. C. Hsu and W. Shepherd, ‘‘Starting and steady-state torques and
currents in a thyristor controlled, two phase induction motor,’’ in
Proc. Int.
Conf. Paper
. Edinburgh, U.K.: Machine Seventies, 1971.
[44]
J. S. Hsu, H. Woodson, and W. F. Weldon, ‘‘Possible errors in measurement
of air-gap torque pulsations of induction motors,’’
IEEE Trans. Energy
Convers.
, vol. 7, no. 1, pp. 202–208, Mar. 1992.
[45]
J. S. Hsu, ‘‘Monitoring of defects in induction motors through air-gap
torque observation,’’
IEEE Trans. Ind. Appl.
, vol. 31, no. 5, pp. 1016–1021,
Sep. 1995.
[46]
Z. Gao, T. G. Habetler, and R. G. Harley, ‘‘An online adaptive stator wind-
ing temperature estimator based on a hybrid thermal model for induction
machines,’’ in
Proc. IEEE IEMDC
. San Antonio, TX, USA, May 2005,
pp. 754–761.
[47]
J. F. Moreno, F. P. Hidalgo, and M. D. Matinez, ‘‘Realisation of tests to
determine the parameters of the thermal model of an induction machine,’’
IEE Proc.-Electr. Power Appl.
, vol. 148, no. 5, pp. 393–397, Sep. 2001.
[48]
P. H. Mellor, D. Roberts, and D. R. Turner, ‘‘Lumped parameter thermal
model for electrical machines of TEFC design,’’
IEE Proc. B-Electr. Power
Appl.
, vol. 138, no. 5, pp. 205–218, Sep. 1991.
[49]
O. I. Okoro, ‘‘Steady and transient states thermal analysis of a 7.5-kW
squirrel-cage induction machine at rated-load operation,’’
IEEE Trans.
Energy Convers.
, vol. 20, no. 4, pp. 730–736, Dec. 2005.
[50]
P. Milanfar and J. H. Lang, ‘‘Monitoring the thermal condition of
permanent-magnet synchronous motors,’’
IEEE Trans. Aerosp. Electron.
Syst.
, vol. 32, no. 4, pp. 1421–1429, Oct. 1996.
[51]
R. K. Singal, K. Williams, and S. P. Verma, ‘‘Vibration behaviour of stators
of electrical machines, part II: Experimental study,’’
J. Sound Vib
, vol. 115,
pp. 13–23, May 1987.
[52]
Y.-S. Lee, J. K. Nelson, H. A. Scarton, D. Teng, and S. Azizi-Ghannad,
‘‘An acoustic diagnostic technique for use with electric machine insula-
tion,’’
IEEE Trans. Dielectr. Electr. Insul.
, vol. 1, no. 6, pp. 1186–1193,
Dec. 1994.
[53]
J. R. Mountain, ‘‘Fuzzy logic motor speed control with real-time interface
using an 8-bit embedded processor,’’ in
Proc. 42nd Southeastern Symp.
Syst. Theory
, Mar. 2010, pp. 307–312.
[54]
F. Rashidi, ‘‘Sensorless speed control of induction motor derives using
a robust and adaptive neuro-fuzzy based intelligent controller,’’ in
Proc.
IEEE Int. Conf. Ind. Technol. (ICIT)
, Dec. 2004, pp. 617–627.
[55]
S. S. Tijare, G. D. Thakare, and R. P. Argelwar, ‘‘Multi-purpose DC high
voltage generator using cockcroft-walton voltage multiplier circuit,’’
Int.
J. Sci. Res. Develop.
, vol. 2, no. 12, pp. 821–823, 2015.
90702
VOLUME 7, 2019
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
[56]
M. Nikhil, R. Waghamare, and P. Argelwar, ‘‘Battery Operated Multistage
High voltage generation by using Cockcroft Walton multiplier,’’
Int. J. Eng.
Sci. Res. Technol.
, vol. 4, no. 3, pp. 370–373, 2015.
[57]
R. Marino, S. Peresada, and P. Valigi, ‘‘Adaptive input-output linearizing
control of induction motors,’’
IEEE Trans. Autom. Control
, vol. 38, no. 2,
pp. 208–221, Feb. 2014.
[58]
O. Thorsen and M. Dalva, ‘‘Condition monitoring methods, failure identi-
fication and analysis for high voltage motors in petrochemical industry,’’
in
Proc. 8th Int. Conf. Elect. Mach. Drives
, Sep. 1997, pp. 109–113.
[59]
Guide for Testing Turn-to-Turn Insulation in Form Ound Stator Coils for
AC Rotating Electric Machines
, IEEE Standard 522, 1992.
[60]
E. P. Dick, B. K. Gupta, P. Pillai, A. Narang, and D. K. Sharma, ‘‘Practical
calculation of switching surges at motor terminals,’’
IEEE Trans. Energy
Convers.
, vol. EC-3, no. 4, pp. 864–872, Dec. 1988.
[61]
D. E. Schump, ‘‘Improved detection method for winding surge tests of
large AC motors,’’ in
Proc. 19th Elect. Electron. Insul. Conf.
, Sep. 1989,
pp. 292–294.
[62]
P. A. Zotos, ‘‘Motor failures due to steep fronted switching surges: The
need for surge protection—User’s experience,’’
IEEE Trans. Ind. Appl.
,
vol. 30, no. 6, pp. 1514–1524, Nov./Dec. 1994.
[63]
L. Frosini, A. Borin, L. Girometta, and G. Venchi, ‘‘A novel approach to
detect short circuits in low voltage induction motor by stray flux mea-
surement,’’ in
Proc. 20th Int. Conf. Elect. Mach. (ICElMach)
, Sep. 2012,
pp. 1538–1544.
[64]
P. C. M. L. Filho, R. Pederiva, and J. N. Brito, ‘‘Detection of stator winding
faults in induction machines using flux and vibration analysis,’’
Mech. Syst.
Signal Process.
, vol. 42, nos. 1–2, pp. 377–387, Jan. 2014.
[65]
R. Romary, R. Pusca, J. P. Lecointe, and J. F. Brudny, ‘‘Electrical machines
fault diagnosis by stray flux analysis,’’ in
Proc. IEEE Workshop Elect.
Mach. Design, Control Diagnosis (WEMDCD)
, Mar. 2013, pp. 247–256.
[66]
O. M. Nassar, ‘‘The use of partial discharge and impulse voltage testing in
the evaluation of interturn insulation failure of large motors,’’
IEEE Trans.
Energy Convers.
, vol. EC-2, no. 4, pp. 615–621, Dec. 1987.
[67]
B. K. Gupta, B. A. Lloyd, G. C. Stone, S. R. Campbell, D. K. Sharma, and
N. E. Nilsson, ‘‘Turn insulation capability of large ac motors part 1—Surge
monitoring,’’
IEEE Trans. Energy Convers.
, vol. EC-2, no. 4, pp. 658–665,
Dec. 1987.
[68]
B. K. Gupta, B. A. Lloyd, G. C. Stone, D. K. Sharma, and J. P. Fitzgerald,
‘‘Turn insulation capability of large ac motors part 2—Impulse strength,’’
IEEE Trans. Energy Convers.
, vol. EC-2, no. 4, pp. 666–673, Dec. 1987.
[69]
B. K. Gupta, B. A. Lloyd, G. C. Stone, D. K. Sharma, N. E. Nilsson, and
J. P. Fitzgerald, ‘‘Turn insulation capability of large AC motors Part 3—
Insulation coordination,’’
IEEE Trans. Energy Convers.
, vol. EC-2, no. 4,
pp. 674–679, Dec. 1987.
[70]
J. C. Botts, ‘‘Corona in high voltage rotating machine windings,’’
IEEE
Elect. Insul. Mag.
, vol. 4, no. 4, pp. 29–34, Jul. 1988.
[71]
A. Boscolo, A. Contin, G. Rabach, and F. Tosato, ‘‘Computer-aided partial
discharge amplitude distribution measurement system for large electrical
machines diagnosis,’’ in
Proc. 2nd Int. Conf. Properties Appl. Dielectric
Mater.
, Sep. 1988, pp. 618–623.
[72]
T. Tanaka, ‘‘Partial discharge pulse distribution pattern analysis,’’
IEE
Proc.-Sci., Meas. Technol.
, vol. 142, no. 1, pp. 46–50, Jan. 1995.
[73]
B. A. Lloyd, S. R. Campbell, and G. C. Stone, ‘‘Continuous on-line partial
discharge monitoring of generator stator windings,’’
IEEE Trans. Energy
Convers.
, vol. 14, no. 4, pp. 1131–1138, Dec. 1999.
[74]
I. J. Kemp, ‘‘Partial discharge plant-monitoring technology: Present and
future developments,’’
IEE Proc.-Sci., Meas. Technol.
, vol. 142, no. 1,
pp. 4–10, 1995.
[75]
G. C. Stone and H. G. Sedding, ‘‘In-service evaluation of motor and
generator stator windings using partial discharge tests,’’
IEEE Trans. Ind.
Appl.
, vol. 31, no. 2, pp. 299–303, Mar./Apr. 1995.
[76]
G. C. Stone, H. G. Sedding, and M. J. Costello, ‘‘Application of partial
discharge testing to motor and generator stator winding maintenance,’’
IEEE Trans. Ind. Appl.
, vol. 32, no. 2, pp. 459–464, Mar. 1996.
[77]
S. M. Tetrault, G. C. Stone, and H. G. Sedding, ‘‘Monitoring partial
discharges on 4-kV motor windings,’’
IEEE Trans. Ind. Appl.
, vol. 35,
no. 3, pp. 682–688, May 1999.
[78]
H. W. Penrose and J. Jette, ‘‘Static motor circuit analysis: An introduc-
tion to theory and application,’’
IEEE Elect. Insul. Mag.
, vol. 16, no. 4,
pp. 6–10, Jul. 2000.
[79]
H. W. Penrose, ‘‘Evaluation of electric motor condition using motor circuit
analysis,’’ in
Proc. Elect. Insul. Conf. Elect. Manuf. Coil Winding Conf.
,
pp. 335–340,2001.
[80]
H. W. Penrose,
Motor Circuit Analysis Standards
. New York, NY, USA:
MCA Newsletter, 2002.
[81]
Rockwell Int. Corp.
Riding the Reflected Wave to Improve Variable-
Speed AC Motor Reliability
. Accessed: Jan. 1, 2019. [Online]. Available:
https://www.reliance.com/pdf_elements/d7722/
[82]
Variable Frequency Drives Optimize Performance and Protection of Off-
shore Oil Electric Submersible Pumps, Allen -Bradley Variable Frequency
Drives Application Notes
. Rockwell Automat., Milwaukee, WI, USA.
[83]
Insulation
for
Inverter
Fed
Motors
.
[Online].
Available:
www.techcentre.tde.alstom.com
[84]
M. Orman, M. Orkisz, and C. T. Pinto, ‘‘Slip estimation of a large induction
machine based on MCSA,’’ in
Proc. 8th IEEE Symp. Diagnostics Elect.
Mach., Power Electron. Drives
, Bologna, Italy, Sep. 2011, pp. 568–572.
[85]
P. Konar and P. Chattopadhyay, ‘‘Bearing fault detection of induction motor
using wavelet and support vector machines (SVMs),’’
Appl. Soft Comput.
,
vol. 11, no. 6, pp. 4203–4211, Sep. 2011.
[86]
B. Yazici and G. B. Kliman, ‘‘An adaptive statistical time-frequency
method for detection of broken bars and bearing faults in motors using
stator current,’’
IEEE Trans. Ind. Appl.
, vol. 35, no. 2, pp. 442–452,
Mar. 1999.
[87]
M. Vetterli and C. Herley, ‘‘Wavelets and filter banks: Theory and design,’’
IEEE Trans. Signal Process.
, vol. 40, no. 9, pp. 2207–2232, Sep. 1992.
[88]
R. Burnett, J. F. Watson, and S. Elder, ‘‘The application of modern
signal processing techniques to rotor fault detection and location within
three phase induction motors,’’ in
Proc. IEEE Instrum. Meas. Technol.
Conf. (IMTC)
, Waltham, MA, USA Apr. 1995, pp. 23–26.
[89]
L. Eren and M. J. Devaney, ‘‘Motor bearing damage detection via wavelet
analysis of the starting current transient,’’ in
Proc. 18th IEEE Instrum.
Meas. Technol. Conf. (IMTC)
, Budapest, Hungary, vol. 3, May 2001,
pp. 1797–1800.
[90]
S. Rajagopalan, J. M. Aller, J. A. Restrepo, T. G. Habetler, and R. G. Harley,
‘‘Analytic-wavelet-ridge-based detection of dynamic eccentricity in brush-
less direct current (BLDC) motors functioning under dynamic operating
conditions,’’
IEEE Trans. Ind. Electron.
, vol. 54, no. 3, pp. 1410–1419,
Jun. 2007.
[91]
J. Cusidó, L. Romeral, J. A. Ortega, J. A. Rosero, and A. G. Espinosa,
‘‘Fault detection in induction machines using power spectral density
in wavelet decomposition,’’
IEEE Trans. Ind. Electron.
, vol. 55, no. 2,
pp. 633–643, Feb. 2008.
[92]
F. Gu, T. Wang, A. Alwodai, X. Tian, Y. Shao, and A. D. Ball, ‘‘A new
method of accurate broken rotor bar diagnosis based on modulation signal
bispectrum analysis of motor current signals,’’
Mech. Syst. Signal Process.
,
vol. 50, pp. 400–413, Jan. 2015.
[93]
N. Ngote, S. Guedira, M. Ouassaid, and M. Cherkaoui, ‘‘Comparison
of wavelet-functions for induction-motor rotor fault detection based on
the hybrid ‘Time synchronous averaging—Discrete wavelet transform’
approach,’’ in
Proc. Int. Conf. Elect. Inf. Technol. (ICEIT)
, Marrakech,
Morocco, Mar. 2015, pp. 94–99.
[94]
H. Su and K. T. Chong, ‘‘Induction machine condition monitoring using
neural network modeling,’’
IEEE Trans. Ind. Electron.
, vol. 54, no. 1,
pp. 241–249, Feb. 2007.
[95]
A. Soualhi, G. Clerc, and H. Razik, ‘‘Detection and diagnosis of faults
in induction motor using an improved artificial ant clustering technique,’’
IEEE Trans. Ind. Electron.
, vol. 60, no. 9, pp. 4053–4062, Sep. 2013.
[96]
W. S. Gongora, ‘‘Neural Approach to Fault Detection in Three-phase IMs,’’
IEEE Latin America Trans.
, vol. 14, no. 3, pp. 1279–1288, Mar. 2016.
[97]
J. J. Saucedo-Dorantes, M. Delgado-Prieto, R. A. Osornio-Rios, and R. de
Jesus Romero-Troncoso, ‘‘Multifault diagnosis method applied to an elec-
tric machine based on high-dimensional feature reduction,’’
IEEE Trans.
Ind. Appl.
, vol. 53, no. 3, pp. 3086–3097, May/Jun. 2017.
[98]
T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, ‘‘Real-time motor
fault detection by 1-D convolutional neural networks,’’
IEEE Trans. Ind.
Electron.
, vol. 63, no. 11, pp. 7067–7075, Nov. 2016.
[99]
S. E. Pandarakone, M. Masuko, Y. Mizuno, and H. Nakamura, ‘‘Deep
neural network based bearing fault diagnosis of induction motor using
fast Fourier transform analysis,’’ in
Proc. IEEE Energy Convers. Congr.
Expo. (ECCE)
, Portland, OR, USA, Sep. 2018, pp. 3214–3221.
[100]
S. Wu and T. W. S. Chow, ‘‘Induction machine fault detection using SOM-
based RBF neural networks,’’
IEEE Trans. Ind. Electron.
, vol. 51, no. 1,
pp. 183–194, Feb. 2004.
[101]
A. Malhi and R. X. Gao, ‘‘PCA-based feature selection scheme for
machine defect classification,’’
IEEE Trans. Instrum. Meas.
, vol. 53, no. 6,
pp. 1517–1525, Dec. 2004.
VOLUME 7, 2019
90703
S. Kumar
et al.
: Comprehensive Review of Condition Based Prognostic Maintenance (CBPM)
[102]
G. K. Singh and S. A. S. A. Kazzaz, ‘‘Isolation and identification of dry
bearing faults in induction machine using wavelet transform,’’
Tribol. Int.
,
vol. 42, no. 6, pp. 849–861, 2009.
[103]
A. Widodo and B.-S. Yang, ‘‘Wavelet support vector machine for induc-
tion machine fault diagnosis based on transient current signal,’’
Expert Syst.
Appl.
, vol. 35, nos. 1–2, pp. 307–316, 2008.
[104]
G. Niu, A. Widodo, J.-D. Son, B.-S. Yang, D.-H. Hwang, and D.-S. Kang,
‘‘Decision-level fusion based on wavelet decomposition for induction
motor fault diagnosis using transient current signal,’’
Expert Syst. Appl.
,
vol. 35, no. 3, pp. 918–928, 2008.
[105]
P.
J.
Escamilla-Ambrosio,
X.
Liu,
N.
A.
J.
Lieven,
and
J. M. Ramírez-Cortés,
‘‘Wavelet-fuzzy
logic
approach
to
structural
health monitoring,’’ in
Proc. Annu. Meeting North Amer. Fuzzy Inf.
Process. Soc.
, El Paso, TX, USA, Mar. 2011, pp. 1–6.
[106]
J. Zhong, Z. Yang, and S. F. Wong, ‘‘Machine condition monitoring and
fault diagnosis based on support vector machine,’’ in
Proc. IEEE Int. Conf.
Ind. Eng. Eng. Manage.
, Macao, China, Dec. 2010, pp. 2228–2233.
[107]
L. Sun, M. Liu, H. Qian, and G. Ye, ‘‘A new method to mechanical fault
classification with support vector machine,’’ in
Proc. 2nd Int. Conf. Digital
Manuf. Automat.
, Hunan, China, Aug. 2011, pp. 68–73.
[108]
J. Zarei and J. Poshtan, ‘‘Bearing fault detection using wavelet packet
transform of induction motor stator current,’’
Tribol. Int.
, vol. 40, no. 5,
pp. 763–769, May 2007.
[109]
S.-Y. Shao, W.-J. Sun, R.-Q. Yan, P. Wang, and R. X. Gao, ‘‘A deep learn-
ing approach for fault diagnosis of induction motors in manufacturing,’’
Chin. J. Mech. Eng.
, vol. 30, no. 6, pp. 1347–1356, 2017.
[110]
K. Li, G. Cheng, X. Sun, and Z. Yang, ‘‘A nonlinear flux linkage model
for bearingless induction motor based on GWO-LSSVM,’’
IEEE Access
,
vol. 7, pp. 36558–36567, 2019.
SANJAY KUMAR
(S’15) received the B.E. degree
in electrical engineering from Government Engi-
neering College, Rewa, India, in 1997, and the
M.Tech. degree with specialization in electrical
machines and drives from IIT (BHU) Varanasi,
Varanasi, India, in 2010, where he is currently a
Research Scholar with the Department of Elec-
trical Engineering. His research interests include
electrical machines and drives and power system
protection.
DEBOTTAM MUKHERJEE
received the B.Tech.
degree in electrical engineering from the West
Bengal University of Technology, in 2014, and
the M.Tech. degree in power system from the
University of Calcutta, India, in 2017. He is cur-
rently pursuing the Ph.D. degree with IIT (BHU)
Varanasi, Varanasi, India. His research interests
include cyber security, state estimations, small sig-
nal stability of power systems, optimal reactive
and active power flow in the grid with the help of
STATCOM, FACTS devices, and also static VAr compensator.
PABITRA KUMAR GUCHHAIT
received the
B.Sc. degree (Hons.) in physics from the R. K.
Mission Vivekananda Centenary College (under
W.B.S.U), in 2011, and the B.Tech. degree in elec-
trical engineering and the M.Tech. degree in power
systems from the University of Calcutta, India,
in 2014 and 2016, respectively. He is currently
pursuing the Ph.D. degree with the National Insti-
tute of Technology, Arunachal Pradesh, India. His
research interests include optimal reactive power
flow with the help of facts devices, static VAr compensators, and STATCOM.
RAMASHIS BANERJEE
received the B.Tech.
degree in electrical engineering from the West
Bengal University of Technology, in 2013, and
the M.Tech. degree in instrumentation and con-
trol engineering from the University of Calcutta,
in 2017. He is currently pursuing the Ph.D. degree
with the National Institute of Technology, Silchar.
His research interests include adaptive control,
optimal control, robust control, fractional order
control, and the condition monitoring of induction
machine.
ANKIT
KUMAR
SRIVASTAVA
received
the
B.Tech. degree in electrical engineering from VBS
Purvanchal University, Jaunpur, India, in 2008,
and the M.Tech. degree in power electronics and
drives from the Kamla Nehru Institute of Tech-
nology, Sultanpur, India, affiliated to G.B. Tech-
nical University Lucknow, India, in 2011. He is
currently pursuing the Ph.D. degree with Dr. APJ
Abdul Kalam Technical University, Lucknow.
He is also an Assistant Professor with the Depart-
ment of Electrical Engineering, IET, Dr. Rammanohar Lohia Avadh Univer-
sity, Ayodhya, India. His research interests include short-term load and price
forecasting, and AI applications in power system.
D. N. VISHWAKARMA
(M’01–SM’02) received
the
B.Sc.
(Engg.),
M.Sc.
(Engg.),
and
Ph.D.
degrees from Patna University, Patna, India. He is
currently a Senior Professor with the Department
of Electrical Engineering, IIT (BHU) Varanasi,
Varanasi, India. He has a teaching and research
experience of more than 40 years. He has con-
tributed about 75 research papers in various inter-
national and national journals and conferences.
He has coauthored a book
Power System Protec-
tion and Switchgear
(McGraw Hill Education Private Ltd). His current
research interests include numerical protection of power systems, wide area
measurements; smart grid and AI applications to power systems. He is a
Fellow of the Institution of Engineers, India, of the Institution of Electronics
and Telecommunication Engineers, and a Life Member of the Systems
Society of India, and of the Indian Society for Technical Education, India.
R. K. SAKET
(M’16–SM’19) is currently a Pro-
fessor with the Department of Electrical Engi-
neering, IIT (BHU) Varanasi, Varanasi, India.
He has contributed more than 85 scientific articles
including book chapters in prestigious handbooks,
research papers in indexed international journals,
and peer-reviewed conference proceedings. His
research interests include reliability engineering,
electrical machines and drives, power system reli-
ability, and renewable energy systems. He is a
Fellow of the Institution of Engineers, India, SMIEEE, USA, MIET, U.K.,
and a Life Member of the Indian Society for Technical Education, New Delhi,
India. He is an Editorial Board Member of the Engineering, Technology and
Applied Science Research, Greece. He has received many awards, honors,
and recognitions for his academic and research contributions including the
prestigious GYTI Award–2018 by the Hon’ble President of India at New
Delhi, India, Design Impact Award–2018 by Padma Vibhushan Ratan Tata
at Mumbai, India, and Nehru Encouragement Award by M.P. State Govern-
ment, Bhopal, India.
90704
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been selected, including 422SS (stainless steel), IN718 (nickel alloy), and Ti64 (titanium alloy) with their
measured tensile properties and equation of true stress-true strain relationship used listed below. Tref25°C.
Specifically, three factors will need to be evaluated, including different materials, temperature, and size
effect. Please calculate true stress values for true strain ranging between 0-3 for each case listed below.
Material
A (MPa)
& (S-¹)
Tm (°C)
870
0.01
1520
422SS (Peyre et al., 2007)
IN718 (Kobayashi et al., 2008)
Ti64 (Umbrello, 2008)
980
1
1300
782.7
1E-5
1660
Material
422SS (CINDAS, 2011)
IN718 (Davis, 1997)
Ti64 (Fukuhara and Sanpei, 1993)
0 =
X
G (GPa)
1+
B…
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Could you please explain where and what is tight side and also how to find 5-9
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(b) A special sprinkler system is comprised of three identical humidity sensors,
a digital controller, and a pump, of which the reliability is 0.916, 0.965, and
0.983 respectively. The system configuration is shown in the figure below.
Sensor
Controller
Pump
Reliability block diagram of a sprinkler system.
(b) Calculate the reliability of the sprinkler system.
(c) Discuss the importance of safety in an engineering maintenance.
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Vibrations
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Which of these statements are correct?
arrow_forward
otoring:
MITO
R
(Motor)
=
VAPPL
Torque [N*m]
60
T 0₁ V
200 rad/s
W₁
Viscous Friction
b₁
N₁
J₁
2
N₂
b₂
Viscous Friction
J₂
VMOTOR K, W₁
T=K₁ i
0₂
ta from the steady state dynamometer test and the given values: J₁
², b₁
2N, b2 = 12N, N₁ = 50 teeth, N₂ = 100 teeth (R should
rad'
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) Explain the procedure adopted to arrive at the specification of piezo electric sensor charge amplifiercrank angle encoder and AD convener with data storage for heat release analysis of a given IC engine.
(b) Discuss the method of obtaining pressure crank angle diagram. List down the parameters that can bestudied from the pressure crank angle diagram.
arrow_forward
Dynamics of a current controller DC motor is given as follows:
KimJm+bB
where, ẞ denotes the shaft angle (output), i, is the motor current (input), K, is the torque constant, im is rotor inertia along the rotation axis, b, is the coefficient of viscous friction for the rotor.
im= 0.48gm, bm = 0.2Nms/rad, K, = 0.96 Nm/A
Initial conditions are zero.
Obtain the transfer function of the system, Design a lead controller such that the static velocity error constant is improved by a factor of 13 when compared to the case of unit feedback. Use unit ramp for both cases. Moreover, gain margin
should be at least 10 dB and the phase margin should be at least 40 degrees. Clearly show all the necessary steps. (When computing max. phase lead, consider additional 34.33 degrees for safety)
Using MATLAB's margin function, indicate the phase margin and the gain margin of the controlled system. Show this Bode plot, steps used.
Using MATLAB/Simulink, simulate the controller. Compare it with respect to unit…
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40. Marbles on a Xylophone. A xylophone is made of several cuboidal keys of the same material
Z, each having its own resonant frequency to play the different notes on a scale. Through various sound
experiments, it was found that the resonant frequency of a key was given by
w lY
where
• w is the thickness of the key;
• A is the area of the key;
• Y is the Young's modulus of Z;
• p is the density of Z; and
• K is a dimensionless constant.
(a) The key which plays A4 (f = 440 Hz) has length, width, and thickness 28 cm, 6 cm, and 3cm,
respectively. If the density and Young's modulus of Z are p = 800 kg/m³ and Y = 96 GPa, find x.
A 2g marble is dropped on this key from a height of 1.8 m, and the key acquires an instantaneous initial
velocity.
(b) Assuming a perfectly elastic collision, find the magnitude of this velocity. I
Due to this impact, the key oscillates. We can treat the key as if it were attached to a spring with
damping. This damping causes the key to experience oscillations which…
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drawing is attached for reference
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Please show step by step work
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Dynamics of a current controller DC motor is given as follows:
Ktim = Jmẞ +bmẞ
where, ẞ denotes the shaft angle (output), i, is the motor current (input), K, is the torque constant, im is
rotor inertia along the rotation axis, bm is the coefficient of viscous friction for the rotor.
im=0.4kgm, bm = 0.1Nms/rad, K, = 0.48 Nm/ A
Initial conditions are zero.
Obtain the transfer function of the system, Design a lead controller such that the static velocity error
constant is improved by a factor of 13 when compared to the case of unit feedback. Use unit ramp for
both cases. Moreover, gain margin should be at least 10 dB and the phase margin should be at least 40
degrees. Clearly show all the necessary steps. (When computing max. phase lead, consider additional
34.33 degrees for safety)
Using MATLAB's margin function, indicate the phase margin and the gain margin of the controlled
system. Show this Bode plot, steps used.
Using MATLAB/Simulink, simulate the controller. Compare it with respect…
arrow_forward
Consider the basic EOQ model. We want to know the sensitivity of (1) the optimal order quantity, (2) the sum of the annual order cost and the annual holding cost (not including the annual purchase cost cD), and(3) the time between orders to various parameters of the problem. a. How do (1), (2), and (3) change if the setup cost K decreases by 10%? b. How do (1), (2), and (3) change if the annual demand doubles? c. How do (1), (2), and (3) change if the cost of capital increases by 10%? (For this part, assume that the storage cost s is zero.) d. How do (1), (2), and (3) change if the changes in parts a, b, and c all occur simultaneously?
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Steps-Part aStep 1) In order to calculate the spring constant, the spring is positioned vertically, and a small mass is attached to its lower hook.Step 2) Visit the site:https://phet.colorado.edu/sims/html/masses-and-springs/latest/masses-and-springs_en.htmlStep 3) Select the Lab icon.Step 4) Make sure the damping is set to none. Using the ruler, measure the displacement of the spring and calculate the spring constant.Step 5) The mass is removed and securely attached to an inelastic cord. On other end of the cord is attached to the spring. Another segment of cord is attached to the other side of the spring. The opposite end of this cord is left free. This is the end in which the system will be rotated. The setup is shown in Fig. 1. The ball is rotated in a horizontal circle.Step 6) The radius of the system is measured before the spring is displaced.Step 7) The system is now spun around until uniform circular motion is achieved and the spring displacement is measured.
Steps-Part bStep…
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The vibration of a dynamic system was monitored in 4 different scenarios, obtaining the following displacement recordings over time. Match each case with its corresponding frequency spectrum.
Case 1, Case 2, Case 3, Case 4: (Displacement vs Time graphs shown for each case)
Options:(Frequency vs Amplitude graphs labeled from Option A to Option P)
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- 3. Microfluidic channels will need to be fabricated on a key micro-scale sensor used by aerospace industries. Before running machining tests and analyzing machined quality, preliminary efforts are needed to evaluate selected materials and factors affecting machining process¹. Three material candidates have been selected, including 422SS (stainless steel), IN718 (nickel alloy), and Ti64 (titanium alloy) with their measured tensile properties and equation of true stress-true strain relationship used listed below. Tref25°C. Specifically, three factors will need to be evaluated, including different materials, temperature, and size effect. Please calculate true stress values for true strain ranging between 0-3 for each case listed below. Material A (MPa) & (S-¹) Tm (°C) 870 0.01 1520 422SS (Peyre et al., 2007) IN718 (Kobayashi et al., 2008) Ti64 (Umbrello, 2008) 980 1 1300 782.7 1E-5 1660 Material 422SS (CINDAS, 2011) IN718 (Davis, 1997) Ti64 (Fukuhara and Sanpei, 1993) 0 = X G (GPa) 1+ B…arrow_forwardCould you please explain where and what is tight side and also how to find 5-9arrow_forward(b) A special sprinkler system is comprised of three identical humidity sensors, a digital controller, and a pump, of which the reliability is 0.916, 0.965, and 0.983 respectively. The system configuration is shown in the figure below. Sensor Controller Pump Reliability block diagram of a sprinkler system. (b) Calculate the reliability of the sprinkler system. (c) Discuss the importance of safety in an engineering maintenance.arrow_forward
- Vibrationsarrow_forwardWhich of these statements are correct?arrow_forwardotoring: MITO R (Motor) = VAPPL Torque [N*m] 60 T 0₁ V 200 rad/s W₁ Viscous Friction b₁ N₁ J₁ 2 N₂ b₂ Viscous Friction J₂ VMOTOR K, W₁ T=K₁ i 0₂ ta from the steady state dynamometer test and the given values: J₁ ², b₁ 2N, b2 = 12N, N₁ = 50 teeth, N₂ = 100 teeth (R should rad'arrow_forward
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