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MULTIPLE FAULTS DETECTION AND IDENTIFICATION OF
THREE PHASE INDUCTION MOTOR USING ADVANCED
SIGNAL PROCESSING TECHNIQUES
Majid Hussain
PhD Scholar IICT, Mehran University of Engineering and Technology.
Jamshoro, (Pakistan).
E-mail: majidhussain@quest.edu.pk ORCID: https://orcid.org/0000-0002-5581-1260
Rana Rizwan Ahmed
Department of Electronic Engineering, Mehran University of Engineering and Technology.
Jamshoro, (Pakistan).
E-mail: rizwan.ese@gmail.com ORCID: https://orcid.org/0000-0001-7449-3715
Imtiaz Hussain Kalwar
Department of Electrical Engineering, DHA SUFFA University.
Karachi, (Pakistan).
E-mail: imtiaz.hussain@dsu.edu.pk ORCID: https://orcid.org/0000-0002-7947-9178
Tayab Din Memon
NCRA CMS Lab, Mehran University of Engineering and Technology.
Jamshoro, (Pakistan).
E-mail: tayabdin82@gmail.com ORCID: https://orcid.org/0000-0001-8122-5647
Recepción:
07/09/2020
Aceptación:
02/10/2020
Publicación:
13/11/2020
Citación sugerida Suggested citation
Hyder, M., y Ali, S. (2020). Multiple faults detection and identication of three phase induction motor
using advanced signal processing techniques. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición
Especial, Noviembre 2020, 93-117. https://doi.org/10.17993/3ctecno.2020.specialissue6.93-117
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ABSTRACT
In this paper, we have presented the multiple fault detection and identication system
for three-phase induction motor. Fast Fourier Transform (FFT) is the most used signal
processing technique that oers good frequency information but failing in providing time
information and handling multiple faults identication with their occurrence time. FFT also
fails to detect non-stationary condition of the signal and unable to convey sudden changes,
start and end of the events, drifts and trends. To obtain simultaneous time frequency
information and to deal with non-stationary signals Short Time Fourier Transform (STFT)
is considered optimal technique that can clearly provide time and frequency information
both. In this research work, the multiple fault detection and identication system is presented
by employing Short Time Fourier Transform (STFT) signal processing technique. The
proposed model is designed using current signature analysis method (CSAM) for three major
faults including three phase supply imbalance, single phasing condition and breakage of
rotor bars. The system is simulated in MATLAB/SIMULINK and simulation is performed
based on healthy and unhealthy conditions of the motor. Comparative analysis between
FFT and STFT, shows STFT as a promising approach.
KEYWORDS
Induction Motor, STFT, Matlab/Simulink, Current Signature Analysis, Power Supply
Imbalance, Single Phasing, Broken Rotor Bar.
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1. INTRODUCTION
An Induction motor is the main source of mechanical power in almost every industry
including sugar, fertilizer, packing, agriculture lands, domestic and commercial water
supply schemes, water ltration, RO plant, locomotives etc. Apparently, induction motors
are widely accepted in industrial processes as well due to its robustness, cost eectiveness,
capability to operate in rough environment and less error chance (Pandey, Zope, &
Suralkar, 2012; Mortazavizadeh & Mousavi, 2014; Nandi, Toliyat, & Li, 2005; Soother
& Daudpoto, 2019). However, like other motors induction motor also faces several faults
due to its operating environment and usage conditions. Most of the faults are due to load
variations and improper power supply arrangements (Nandi et al., 2005; Soother, Daudpoto,
& Shaikh, 2018).
There are many electrical and mechanical faults related to both stator and rotor. Most
described faults in the literature related to the rotor are bearing faults, broken rotor and end
rings faults, and air gap eccentricity faults (Nandi et al., 2005; Mortazavizadeh & Mousavi,
2014). The faults related to the stator are imbalance in the supply phase voltages, under or
over voltage, single phasing condition, reverse phase sequence and inter turn short circuit
fault etc. (Nandi et al., 2005; Mortazavizadeh & Mousavi, 2014).
Presently much work is reported in this area to nd, isolate and identify dierent types of
the faults and avoid plant shutdown i.e., health of the motor is diagnosed by monitoring
certain parameters. The parameter may be the vibration, torque, ux, temperature, current
etc. (Mortazavizadeh & Mousavi, 2014; El Bouchikhi, Choqueuse, & Benbouzid, 2015).
The condition monitoring makes it possible to detect any abnormal behavior in the motor
at an early stage so that any big loss can be avoided (Gao, Cecati, & Ding, 2015). After
observing any abnormal condition, the necessary preventive maintenance strategies can
be applied for the removal of faults Unlike corrective maintenance strategy in which
correction applied after fault has gone through motor and motor operation is disturbed
(Mal et al., 2020; Ujjan et al., 2020). In this case, motor may be seriously damaged and can
cause unrecoverable loss to the plant.
Many researchers have been working in the eld of condition monitoring for fault detection
and identication using dierent fault diagnoses schemes including vibration, thermal,
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chemical and electrical (Nandi et al., 2005; Mortazavizadeh & Mousavi, 2014; Gao et al., 2015).
In vibration monitoring, faults are identied based on intensity of vibrations in healthy and
unhealthy conditions. Vibration monitoring sometimes gives ambiguous result when there
are uctuations in the load so thermal monitoring is employed, in which temperature of
the dierent sections of the machine is monitored and faults identied based on the sensors
located at dierent sections on the motors. Thermal technique does not give good results
when there are multiple faults in the motor and multiple temperature sensors requirement
make it costly (Siddiqui et al., 2014).
Another technique previously used for fault detection is Air-Gap Torque monitored. In this
technique motor torque is measured and non-zero frequency of the torque describes the
faulty situation of the machine. Its main drawback is that there is no specic mathematical
model available for fault signature (Gao et al., 2015). Stator power analyses is another
useful technique used previously for unbalance fault detection in which spectral and AC
components of the power signal are measured in all three phases. This technique fails to
produce good results for low intensity faults (Sharma et al., 2015).
Nowadays, most used technique for condition monitoring of the motor is Motor Current
Signature Analyses (MCSA). In MCSA stator current is continuously acquired and after
applying a signal processing technique at current signal the frequency spectrum gives the
knowledge about the health of the motor (Benbouzid, 2000; Zhongming & Bin, 2000; Gao
et al., 2015). The signal processing technique to be applied depends upon the type of the
fault to be detected and nature of the fault. Some types of faults are low intensity in nature.
Sometimes only information about frequency component of the signal is desired and, in
some cases, both time and frequency information are required. So, it depends upon the fault
which signal processing technique will be suitable for it (Nandi et al., 2005; Mortazavizadeh
& Mousavi, 2014). The most common signal processing techniques employed are FFT,
Short time Fourier Transform (STFT), wavelet transform (WT), Hilbert-Huang transform
(HHT) and Wigner-Ville Distribution (WVD) (Gao et al., 2015).
In Mehala and Dahiya (2008), Cusidó et al. (2008), and El Bouchikhi et al. (2015), authors
have reported comparative study on dierent signal processing techniques and compared
the results for broken rotor bar fault using FFT, STFT and wavelet transform. With variable
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FFT does not provides better results and misses to provide useful information like start and
end of the event, changes in load etc. So, a new algorithm is proposed by using STFT and
WT to achieve better results. It is further reported that each technique has advantages
and disadvantages that depends upon the application and constraint for example –
performance, complexity, and desired results. It is not advisable to use STFT and WT for
ordinary fault where only frequency information is needed. No doubt WT provides better
results as compared to STFT but wavelet transform is complex in nature, as signal is divided
into high and low frequency parts, therefore requires more calculations and computation
time. It always remains an issue of selecting the basis for wavelets which matches with type
of information is required. Interpreting the results of the wavelets also requires skill. STFT
is the trade-o technique between FFT and WT. STFT provides better results for ordinary
faults both in time and frequency domain with less eort. In Mirabbasi, Seifossadat, and
Heidari (2009), authors have detected unbalance in supply voltages using FFT. In Liang et
al. (2002), and Messaoudi and Sbita (2010), authors have detected unbalance supply fault
and broken rotor bar fault using FFT. In Mehrjou et al. (2010), Shi et al. (2014), and Siddiqui
and Giri (2012), wavelet transform is employed for broken rotor bar fault detection. In Da
Silva, Povinelli, and Demerdash (2008), authors have used stator current envelope analyses
for broken rotor bar fault and stator short circuit fault detection. In Çalış and Çakır (2008)
zero crossing time technique for rotor bar fault is employed. In Haggag and Mageed (2013)
authors have developed unity relation in which instantaneous voltage and its complement
are squared in order to detect Single Phasing condition.
This research work presents the detection and identication of three types of motor faults
using short time Fourier transform (STFT) namely a) Imbalance of supply Voltage b) Single
Phasing of supply and c) broken rotor bars. It also presents the simultaneous detection of
multiple faults.
Further this paper proceeds as follows. Section 2 describes possible reasons and impacts
of faults in induction motor, Section 3 presents the simulation and mathematical model of
induction motor with description of STFT, Section 4 presents comparative results of the
proposed system in terms of FFT and STFT analysis, and Section 5 concludes the research.
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2. CAUSES AND EFFECTS OF FAULTS OVER INDUCTION MOTOR
In subsequent sections causes and eects of the faults over induction motor performance and
stator current are discussed in detail that is followed by system modeling and simulations.
2.1. IMBALANCE SUPPLY VOLTAGE
There are several reasons for the imbalance in the power supply voltages. In Pakistan
imbalance voltage condition is frequently faced in domestic, industrial and especially
agriculture sector. The induction motors in the agriculture lands are located very far from
the electrical substation. So, a very long distribution line and poor arrangement of electrical
equipment causes too much voltage uctuations and sometimes single phasing condition
occurs, which can destroy the motor permanently. So, motor with applied imbalance
voltage or in single phasing condition must not run for longer time to avoid any damage.
Single phasing is most serious condition that induction motor faces and it can permanently
damage the motor. The reasons for single phasing may include blowing of Line fuse, supply
terminal loosing, connection of motor from a distribution transformer located very far,
distribution transformer phase opening, Power supply wiring conductors may face unequal
impedance (Mirabbasi et al., 2009; Lee, 1999).
The imbalance and single phasing condition cause several adverse eects on the performance
of the induction motor. According to National Electrical Manufacturers Association
(NEMA), for a better life of induction motor it should not be operated with more than 5%
unbalance in the supply (Quispe, Gonzalez, & Aguado, 2004). Due to unbalance supply
motor may experience negative and pulsating torque which may produce excessive noise.
The imbalance will also increase the current imbalance in windings and temperature of the
motor; this can reduce the life and eciency of the motor.
2.2. EFFECT OF SUPPLY VOLTAGES ON STATOR CURRENT
The induction motor is operated at 3 phase supply with 50 Hz frequency. When motor is
operating in normal condition the stator current spectrum will show only 50 Hz frequency.
If any type of the fault occurs, it causes sidebands near the main frequency. The frequency
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of the generated sidebands depends upon the type of the fault. Every specic fault will have
its own current signature.
Figure 1. Increase in motor losses and heating due to unbalance voltage.
Figure 1 shows the impact of unbalance supply voltage on motor heating. The power
supply unbalance fault will also introduce the sidebands frequencies upon occurrence of
fault (Messaoudi & Sbita, 2010).
(1)
where, k=1, 2, 3, ……… N, f
unv
= unbalanced supply voltage, fs= frequency of supply
voltage
2.3. BROKEN ROTOR BAR FAULT
The rotor of Squirrel cage induction motor as shown in Figure 2 is constructed of usually
copper bars instead of windings and permanently short circuited with end rings. So, under
certain load conditions these bars and end rings are cracked.
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Figure 2. Rotor of induction motor.
Due to no. of reasons the cracks appear in the bars as well as at end rings. This may be due
to thermal stress that causes overloading, magnetic stress caused by electromagnetic forces,
due to electromagnetic force imbalance, vibration and noise cause stress on the Bars. Defect
at manufacturing time causes residual stress. Dynamic stress as a result of shaft torque,
centrifugal forces and cyclic stress has a negative impact on the rotor (Szabó, Dobai, & Biró,
2004).
Under normal condition, current distribution in the rotor bars is uniform according to the
load applied. Upon breakage of the bars, the resistance of the bars is increased and causes
uneven distribution in current loops made by end rings and bars. So, if load is changed
during induction motor operation the current distribution is greatly aected (Liang et al.,
2014). This type of fault is load dependent.
2.4. EFFECT OF FAULTY BROKEN BARS
The change caused by broken rotor bars and end rings in the stator current will introduce
new frequency components at the following frequencies (Messaoudi and Sbita, 2010):
(2)
where, k = 1, 2, 3…, N, f
bb
: broken rotor bar frequency, fs: electrical supply frequency, p:
number of pole pairs, s: slip.
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3. SYSTEM DESIGN AND IMPLEMENTATION
The scope of the research work is limited to the fault detection and identication. This is
achieved by continuously monitoring three phase stator current as shown in Figure 3, which
depicts the implementation of fault diagnoses system for three phase induction motor.
Figure 3. Multiple faults detection and identication system
Three phase supply is fed to the induction motor. Speed, Torque, and three phase stator
current are the motor’s outputs. As in MCSA, the individual phase stator current is taken
as the monitoring parameter in order to detect the fault with its occurrence time. After
acquiring the stator current STFT analysis is conducted on current signals and resulting
spectrogram will convey the information about the condition of the motor. After analysis of
the spectrogram the type and time of the fault can be detected and can be further decided
whether motor should continue running or it may be stopped for necessary maintenance.
This section presents the Mathematical model of the induction motor including its
simulation and describes STFT analysis in the following sub-sections.
3.1. MATHEMATICAL MODEL OF THE INDUCTION MOTOR
The three-phase induction motor model is realized in Simulink using famous dq model that
is explained in Simion, Livadaru, and Munteanu (2012), Robyns et al. (2012), and Batool
and Ahmad (2013). According to this model the three phase quantities are converted to
two phase dq model. The three phases, 120
0
electrically apart are converted into two phase
voltage i.e. “d” and “q” as shown in Figure 4. The following assumptions are made while
considering the two phase dq model:
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• Uniform air gap with no saturation
• Stator windings are distributed sinusoidal.
• Inter bar current is zero
Figure 4. ‘abc’ to ‘dq’ conversion.
The equivalent circuit of three phase induction motor is given in Figure 5 with two phases
d and q. All the related parameters are shown with labeling.
Figure 5. Induction Motor Equivalent circuit.
The following equations can be drawn from the equivalent circuit in order to develop a
mathematical model in the Simulink.
(3)
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(4)
(5)
(6)
(7)
(8)
where, d : direct axis, q : quadrature axis, s : stator variable, r : rotor variable, Fij is the
ux linkage (i=q or d and j=s or r), vqs, vds : q and d–axis stator voltages, vqr, vdr : q and d–
axis rotor voltages, Fmq, Fmd : q and d axis magnetizing ux linkages, Rr : rotor resistance,
Rs : stator resistance, Xls : stator leakage reactance (w
e
Lls), Xlr : rotor leakage reactance
(w
e
Llr)
iqs, ids : q and d–axis stator currents,
iqr, idr : q and d–axis rotor currents,
P : number of poles,
J : moment of inertia,
Te : electrical output torque,
T
L
: load torque,
we : stator angular electrical frequency,
wb : motor angular electrical base frequency,
wr : rotor angular electrical speed.
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Figure 6 represents Motor model developed by exploring Simulink Library using equations
03 to 08 (Ozpineci & Tolbert, 2003; Leedy, 2013). Three phases to two phase voltages are
converted by abc-sync block and syn-abc block performs vice versa function. There are two
inputs to the motor and three outputs. The inputs are three phase supply and the applied
load. The outputs are three phase current, rotor speed and motor output torque.
Figure 6. System Model in Matlab.
3.2. SHORT TIME FOURIER TRANSFORM
The STFT overcomes the drawback associated with the Fast Fourier Transform. The
FFT performs the function of transformation from time to frequency domain. Usually the
transformation is performed to extract the additional information from the signal. FFT is
converting the signal from time to frequency completely misses out the time information
(Polikar, 1994). In case of induction motor faults, the time information is important because
some faults are severe and are not required to persist for long time. While there is some
irregular behavior or any incipient fault which usually can be tolerated for certain time and
the motor does not require the urgent maintenance. FFT fails to provide time information
along with frequency information. By using STFT the simultaneous time and frequency
information can be obtained. So, with time-frequency results both type and time of the
fault can be identied. Mathematically STFT is expressed below:
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(9)
where, X(τ,ω) is STFT output, x(t) is input signal ,w(τ) is the window function
The window function localizes the frequency contents in time. A window function “w
n
”
has a tapering at its end to avoid unnatural irregularities present in the signal frequency
contents. The window function is the trade-o between time and frequency. The time and
frequency information depend upon the size and type of the window. Larger time window
will result poor time resolution and vice versa.
Although there are dierent types of window that are used to localize time frequency
representation, but Hamming, Hann and rectangular window are the most popular. In this
research work Hamming window is used which resulted the useful information.
4. RESULTS AND DISCUSSIONS
This section presents the results obtained from mathematical model of the induction motor.
Moreover, it describes each fault introduced in the model and their comparative analysis
in terms of FFT and STFT spectrum. The system design, development and simulations
are performed in MATALB/Simulink. The motor dq-model is implemented in Simulink
and STFT is applied by exporting the parameters in command window. The supply to the
motor is 220 V, 3-Phase, 50 Hz. The step function is used as load at dierent instants. The
motor outputs include three phase current, Torque and speed of the rotor.
4.1. HEALTHY MOTOR CONDITION
Figures 7 to 9 give motor output parameters in healthy condition. The motor takes high
starting current and then have normal current but when a load of 25 N/m is applied at 1
second the current is increased while rotor speed is decreased.
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Figure 7. Motor output Torque.
Figure 8. Three phase stator current.
Figure 9. Motor RPM. Figure 10. FFT Spectrum.
Time (secs)
Freq (Hz)
Spectrogram
0.4 0.6 0.8
1 1.2 1.4 1.6 1.8 2
0
50
100
150
200
250
300
350
400
450
500
Figure 11. STFT showing abnormality at 1 second.
By taking FFT of stator current, the spectral contents can be seen at 50 Hz fundamental
component which is the indication of healthy motor in Figure 10, FFT can compute
perfect spectral contents but it fails to provide time information in addition to frequency
information. To observe frequency and time simultaneously the STFT is computed as
shown in Figure 11. Figure 11 shows changes at 1 second which is due to the change in
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magnitude (load change) from lower to higher level which we are unable to identify using
FFT. By having time domain signal, we can identify the time of any irregularity or event
and in order to check that irregularity FFT can be computed but to explore simultaneous
Time-Frequency information STFT can be the optimal choice.
4.2. FAULT 01 SIMULATION: POWER SUPPLY IMBALANCE
The 20 volts drop is simulated in Red Phase as power supply imbalance fault after 1 second
and motor is simulated for 2 seconds which can be seen in time domain in Figure 12, having
two irregularities, rst at 0.5 second and second at 1 second. In order to check whether it
is due to change in load or it is because of any fault, FFT is computed. The FFT shows a
sideband is generated at 150 Hz in Figure 13, which indicates power supply imbalance fault
as equation 1 but unable to determine when it occurred. In other words FFT fails to detect
nonstationary condition of the signal. While computing STFT Figure 14, show a change
in the load at 0.5 sec and a sideband is generated along with main frequency component
at 1 sec. The color of the sideband generated is light because of the low magnitude of 150
Hz component.
Figure 12. Sideband generated at 150 Hz. Figure 13. 20 Volt drop introduced after 1 second.
Figure 14. Power supply imbalance spectrogram. Figure 15. 20V drop between 1.5 to 4 seconds.
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Again, the same 20 Volt drop in Red Phase is simulated between 1.5 to 4 seconds and can
be seen in Figure 15, that time domain representation is unable to convey the information
about irregularities at 1 sec, 1.5 sec and at 4 second. Computing FFT of red phase for same
20 V drop shown in Fig. 16 that a sideband is generated at 150 Hz frequency which is the
indication of power supply imbalance fault but again we are unaware what happened at
1sec, 1.5 sec and at 4 sec instants. By analyzing the same fault by STFT Fig. 17, gives some
idea about the time of load change and type of fault by looking 150 Hz sideband between
1.5 to 4 seconds.
Figure 16. FFT for 20V drop between 1,5 to 4 seconds.
Figure 17. STFT for 20V drop between 1.5 to 4
seconds.
Figure 18 shows the 3-D spectrogram of the single fault i.e. imbalance in the supply phase
voltages. It is showing all three parameters time frequency and magnitude of the signal,
describing health and faulty portion of the signal. Similarly in Figure 19, power supply
imbalance fault is detected between 01 to 03 seconds at no load condition.
Figure 18. 3D spectrogram for imbalance of supply
voltage.
Figure 19. STFT for power imbalance fault between 1
to 3 seconds.
4.3. FAULT 02 SIMULATION: SINGLE PHASING CONDITION
Single phasing occurs if any phase among three phases of the supply is missing. Due to
this condition current among the two phases increses drastically and can cause permenant
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damage to induction motor. As in Figure 20, load is applied after 1sec and single phasing
is introduced at 2 sec that causes huge increase in current. If in this condition motor is
allowed to run it may be permenantly damaged. By looking at Time domain reprsentation
we can not estimate what happened at ‘01’ and ‘02’ second. By computing FFT of the
current signal in Figure 21 sidebands at 150,250 and 350 Hz are generated that is the clear
indicatoin of single phasing condition according to relation (1). For having simultaneous
time frequency information STFT is computed as shown in Figure 22, showing three
side bands after 2 seconds. It is clear from Figure 22 that 50 Hz fundamental is dominant
while 150,250 and 350 Hz has respectively decreasing contribution. Figure 23 shows 3D
spectrogram for single phasing condition.
Figure 20. Irregularity at 1 and 2 seconds. Figure 21. FFT for irregualrity at 1 and 2 sec.
Figure 22. 3D Spectrogram for Figure 20. Figure 23. Irregualrity identied by STFT.
4.4. FAULT 03: BROKEN ROTOR BAR DETECTION
Figures 24-26 showing the diagrams for broken rotor bar fault in time, frequency and time-
frequency domain respectively. The broken rotor bar fault is introduced by changing the
resistance of the rotor circuit. Due to broken rotor bar two sidebands are generated at 36
Hz and 64 Hz according to equation (2). Figure 25 shows both sidebands are present for all
times along with main frequency component. So broken rotor bar fault is present since start
and remains all the time.
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Figure 24. Broken rotor bar fault in time domain. Figure 25. Broken rotor bar fault in frequency domain.
Figure 26. Broken rotor bar fault in time and frequency domsain.
4.5. MULTIPLE FAULT INDUCTION
When multiple faults are present in the motor then it is necessary to nd out their occurrence
time in order to know the nature of the fault so that it can be decided whether motor should
continue to run, or it may be stopped. Figure 27 shows FFT spectrum for broken rotor bars
and power supply imbalance faults together. Three side bands along with 50 Hz frequency
component are generated. Two sidebands at 36 Hz and 64 Hz are due to the result of two
broken bars and one sideband at 150 Hz frequency is the result of power supply imbalance
fault but the time information of these faults is missing. Figure 28 shows the computation
of STFT for these two faults. It is clear from the spectrogram that broken rotor bar fault is
present all the time while power supply imbalance fault has occurred at 1.5 second instant.
Similarly Figures 29 and 30 shows the six broken bars and power supply imbalance faults.
Broken bar fault is present all the time and power supply imbalance fault exist between 1
to 3 seconds.
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Figure 27. Two Broken Bars and Power Supply
imbalance faults together.
Time (secs)
Freq (Hz)
Spectrogram
0.5 1 1.5 2 2.5
0
50
100
150
200
250
300
350
400
450
500
Figure 28. Spectrogram for Broken Bar and Power
Supply imbalance faults together.
Figure 29. Six broken bars and 20 V drop fault.
Figure 30. STFT for Six broken bars and 20 V drop
fault.
5. CONCLUSION
Previously, a lot of research has been conducted on Induction motor fault diagnoses
for single fault detection using multiple signal processing techniques. Very less work is
reported on simultaneous multiple faults detection. In this paper, multiple faults with time
information are successfully diagnosed in induction motor. Monitoring system eciently
detected imbalance of supply, single phasing and broken rotor bars occurring at dierent
instants of time. This study can help to overcome the drawback of techniques which are
unable to provide simultaneous time-frequency information of multiple faults. The short
time Fourier transforms has accurately provided the desired results for multiple faults with
their occurrence time. For more detailed information of the faults WT is better choice
but for ordinary faults STFT provides optimal results and conveys enough time-frequency
information with lesser eorts. In case of wavelet transform more calculations are required
because of complex nature. By incorporating this research in the industries and agriculture
elds, online health of the machine can be monitored. Upon having the knowledge of time
and type of faults desired maintenance strategy can be applied thus saving enough time by
avoiding costly downtime.
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254 – 4143 Edición Especial Special Issue Noviembre 2020
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ACKNOWLEDGMENT
The authors express their gratitude to Higher Education Commission (HEC), Pakistan and
Ministry of planning and development Pakistan for providing funds for this research work
as national laboratory on ‘Haptics and Human-Robotic, Condition Monitoring Systems
Lab. This lab is part of the National Center of Robotics and Automation (NCRA). We are
also thankful to MUET Jamshoro for providing lab facility to perform research work.
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