Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 12, No. 2, Jun 2021, pp. 1205~1215
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v12.i2.pp1205-1215  1205
Journal homepage: http://ijpeds.iaescore.com
Machine learning based multi class fault diagnosis tool for
voltage source inverter driven induction motor
Jyothi R, Tejas Holla, Uma Rao K, Jayapal R
Department of Electrical and Electronics Engineering, RV College of Engineering, Bengaluru, india
Article Info ABSTRACT
Article history:
Received Dec 19, 2020
Revised Mar 9, 2021
Accepted Apr 23, 2021
AC drives are employed in process industries for varying applications
resulting in a wide range of ratings. The entire process industry has seen a
paradigm shift from manual to automated systems. The major factor
contributing to this is the advanced power electronics technology enabling
power electronic drives for smooth control of electric motors. Induction
motors are most commonly used in industries. Faults in the power electronic
circuits may occur periodically. These faults often go unnoticed as they rarely
cause a complete shutdown and the fault levels may not be large enough to
lead to a breakdown of the drive. An early detection of these faults is required
to prevent their escalation into major faults. The diagnostic tool for detection
of faults requires real time monitoring of the entire drive. In this work, detailed
investigation of different faults that can occur in the power electronic circuit
of an industrial drive is carried out. Analysis and impact of faults on the
performance of the induction motor is presented. A real time monitoring
platform is proposed to detect and classify the fault accurately using machine
learning. A diagnostic tool also is developed to display the severity and
location of the fault to the operator to take corrective measures.
Keywords:
Diagnostic tool
Fault classification
Induction motor
Machine learning
Multiclass SVM
Reliability centered
Voltage source inverter
This is an open access article under the CC BY-SA license.
Corresponding Author:
Jyothi R
Department of Electrical and Electronics Engineering
RV College of Engineering
RV Vidyanikethan Post, Mysuru Road, Bengaluru-560059, India
Email: jyothir@rvce.edu.in
1. INTRODUCTION
Induction motors are rugged in construction and are used in driving lathe machines, crushers, oil
extracting mills, textile industries, etc. For the smooth control of the induction motors, power electronic
converters are employed. In process industries and manufacturing sectors, the monitoring of power electronic
systems is essential as it affects the system's performance and efficiency. The faults in the system are to be
identified on occurrence to avoid complete breakdown of the production process. Most of the organizations
now a days follow preventive maintenance by scheduling periodical maintenance before failure occurs and
causes damage to the entire system under consideration. If the preventive maintenance is scheduled early, it
might lead to wastage of useful life of machine. An alternative approach is reliability centered maintenance.
reliability centered maintenance is chosen to optimize the maintenance program by monitoring the essential
parameters and recognizing the faults that affects the function of an AC drive system. Due to heavy downtime
costs of devices in modern power industries, there is a need for real time monitoring system to detect incipient
faults before they escalate and casue a breakdown.
Much of the research presented in the literature is focused on the detection of faults in the induction
motor such as rotor bar fault, eccentricity fault. Conventional approaches like motor current signature analysis

 ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 12, No. 2, June 2021 : 1205 – 1215
1206
(MCSA) techniques were employed in earlier days for fault diagnosis in three phase induction motors. Fault
feature was extracted through fast fourier transform (FFT) from sampled stator currents to train the model using
multilayer perceptron, support vector machine (SVM) [1], [7], [8]. Rule based approach like fuzzy logic
technique was employed for fault detection in the Induction motors [2]-[6]. Discrete wavelet transform (DWT)
[8], [9] and principal component analysis (PCA) were utilized to identify the discontinuity in the feature caused
by the faults. Relevance vector machine (RVM) with optimization techniques like evolutionary particle swarm
optimization, cuckoo search optimization was employed to improve the fault detection as the DWT involves
huge data and computational time [7], [13]. Clarke transformed two dimensional features along with surface
error were used to detect the fault. Average current of the three stator currents also was used to detect the faults
in the system [11]-[15].
Detailed diagnostic tool for exhaustive faults in the power electronics drive of an induction motor is
not found in the literature. In this work, the various faults that are likely to occur in the power electronics
circuits have been investigated to detect and classify the faults accurately and identify the correct location of
the faults. The number of features has been optimized to improve the efficiency of the diagnostic tool and also
enable its adaptation for a real time monitoring. Two machine learning algorithms namely multilayer
perceptron neural network and support vector machine have been found to give accurate results. 100 percent
accuracy has been achieved for detection, classification and location of the fault. Similar applictaions discussed
in literature use labview and intricate hardware set up. The novelty of this present work is the use of IoT and
minimal sensors for remote monitoring of the devices. Also, a user-friendly GUI has been developed to display
the results as an operating tool. Reliability centered maintenance would go long way with such a diagnostic
tool reducing loss of production time.
The paper is divided into 8 Sections. Section 1 focuses on the different maintenance strategy and the
conventional methods available for fault diagnosis. In Section 2, the structure of the drive and various faults
are presented. Fault detection and classification methodology used is presented in section 3. Section 4 presents
the simulation model and the feature acquisition. Hardware set up and its specifications are mentioned in the
Section 5. Results and discussion are presented in Section 6. Diagnostic tool with case studies is presented in
Section 7 and conclusion is presented in Section 8.
2. FAULTS IN AN AC DRIVE SYSTEM
Figure 1 represents the structure of voltage source inverter driven induction motor. It consists of the
three phase AC supply, controlled or uncontrolled rectifier, DC link and an inverter capable of generating
variable voltage and variable frequency with PWM pulses.different types of PWM techniques and the
modulation index will have impact on inverter line output voltages and stator currents [18], [19]. The three
phase supply is connected to a diode bridge rectifier to produce DC voltage at the front end . The rectified
voltage is fed through DC link to the power inverter circuit consisting of sensitive power electronic switching
devices such as IGBTs or MOSFETs which are driven by gate drive circuits to provide variable voltage and
frequency to the dynamic loads for speed control. The power electronic circuitry employed in AC drives are
liable to numerous types of electrical faults such as faults in the rectifier, inverter , faults at the motor terminals
and faults in the mechanical load as detailed.
Figure 1. Structure of voltage source inverter driven induction motor
2.1. Faults in rectifier
Bridge rectifiers are used in most of the industrial drives. Faults in an uncontrolled bridge rectifier can
be categorized as open diode fault and diode short circuit fault. In case of open diode fault the magnitude of
the DC output voltage of the rectifier reduces thus affecting performance of the inverter circuit. If diodes of
the same leg are shorted, the current through the circuit shoots up swiftly and potential difference across the
diodes reduces to zero. Due to short circuit of DC Link capacitors at the rectifier front end, faults can occur in
the drive system [20].

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Machine learning based multi class fault diagnosis tool for voltage source inverter … (Jyothi R)
1207
2.2. Faults in inverter
In general, variable voltage variable frequency inverters are preferred over current source inverters, for
drives. Inverters are also susceptible to open circuit and short circuit IGBT faults. Protection schemes will be
employed for short circuit faults. In case of open IGBT fault, the motor will still be in running condition but at
reduced speed [20]-[22]. The harmonics component in the stator current of corresponding phase increases
resulting in a lower efficiency due to PQ disturbance. Short circuit fault of IGBT results in large amount of
current flow through the inverter circuit.
2.3. Faults at motor terminal
The three-phase variable frequency, variable output voltage from the VSI is fed to the three-phase
motor input terminal. Faults like SLG fault, L-L fault, lines open fault at the AC power line could cause
disturbances at the motor terminal. In case of short circuit of the lines, the induction motor will stop running
and very high current flows through the short-circuited terminals and the reverse current causes damage to the
diodes and IGBTs.
Faults can also occur in the induction motor due to damage in stator windings, bearings, squirrel cage
rotor broken bars, insulation failure [21]-[23]. The incipient faults do not cause the motor to stop unlike short
circuit faults. The induction motors operate even on the occurrence of incipient faults. The reliability of the
entire system tend to reduce and damage the machines over the long run. Hence, they are dangerous as they
may be undetected for a long time, silently causing damage to the motor.
Fault diagnosis plays a crucial role in industrial processes which involves fault detection and
classification (FDC) based on location and its severity. The operator also needs to be informed about the fault
through an interface to take precautionary measures or to take immediate action to avoid the consequences of
failure and damage to the machine. The entire process is termed as reliability centered maintenance. Amongst
numerous approaches followed for the reliability centered maintenance, machine learning appears to be an
encouraging tool.
In the proposed work, two machine learning algorithms namely multi-layer perceptron-neural network
(MLP-NN) and support vector machine (SVM) are employed [22]. Data acquisition is performed through
multiple sensors to monitor the condition of the power electronic system. Often the data collected through
physical sensors may not yield good feature extraction. In such cases, additional features are generated using
the measured data to obtain the hidden features. This data is termed as synthesized data.
In the proposed work, AC drive is simulated using MATLAB/Simulink. Various types of open circuit
faults such as diode open fault in the rectifier, open IGBT fault in the inverter are created and corresponding
electrical parameters like stator currents, inverter line output voltages and their THDs and the mechanical
parameters like speed and torque of the motor are obtained to determine the effect of the electrical faults. Fault's
detection, classification and point of occurrence of fault are identified using machine learning and diagnostic
tool is developed using python to display the information about the severity of fault to the operator to take
corrective measures.
3. FAULT DETECTION AND CLASSIFICATION SYSTEM
The block diagram of fault detection and classification (FDC) system is as shown in Figure 2. Machine
learning based FDC system involves the steps. The data collection from the system, feature engineering which
involves the identification of parameters that are efficient in the detection of faults, data training and choosing
a suitable model, training and evaluating the model and parameter tuning for efficient prediction and diagnosis
of faults in the system. The raw data collected from the system is not often suitable to train the model and
evaluate the parameters for classification of faults. Hence, feature engineering plays a significant role in the
fault detection and classification. For a real time, monitoring tool, the features have to be optimized to improve
computational power, memory usage and result in shorter training time with minimal chance of overfitting.
Classification of faults from the selected features is known as supervised learning approach.
The different machine learning algorithms applicable for classification of the faults in the system are
linear classifiers like logistic regression, decision tree, support vector machine and neural network. Multiclass
support vector machine algorithm has been extensively used for classification and outlier detection in many
applications. Multilayer perceptron neural network (MLP-NN) algorithm consists of set of software neurons
arranged in layers with an organized connection for communication. MLP-NN uses gradient descent algorithm
for learning through loss function. In the current research work, MLP-NN and SVM are preferred for
quantitative multi class electrical fault detection and classification.

 ISSN: 2088-8694
1208
Figure 2. Fault detection and classification system
4. SIMULATION MODEL
Fault detection and classification system developed for voltage source inverter driven induction motor
(VSI-IM) are discussed in this section. The simulation is carried out in MATLAB/Simulink platform, the data
extracted is trained and tested in google colab environment for both the machine learning algorithms namely
MLP-NN and SVM. Two different indicators are discussed for fault detection and classification in the VSI-IM
system.one using instantaneous values of the parameters and another using RMS Values. The Simulink model
used for extracting the data from voltage source driven induction motor is as shown in Figure 3.
Figure 3. Simulink model of VSI driven Induction motor
The severity of open circuit incipient faults is a major concern, Various open circuit faults are diode
open circuit, IGBT open circuit, cross diode and cross IGBT open circuit faults. These are divided into 24
classes. Features are used to distinguish between the various faults. Features such as, i) rectifier output voltage
(Vdc), ii) inverter line output voltages (Vab, Vbc, Vca), iii) induction motor stator currents (Ia, Ib, Ic), iv) the
inverter line output voltages and stator currents THDs, and v) speed are used for fault detection and
classification using machine learning algorithms [15], [16]. The data is obtained from the simulation model as
shown in Figure 3 built using MATLAB 2019a Simulink platform by creating various types of faults at a
sampling frequency of 1e-5 seconds. The nature of the data tried for accurate detection and classification are
of two types. The data is preprocessed by normalisation technique.
a. Instantaneous values
The instantaneous values of inverter output line voltages and induction motor stator currents and the
rectified output voltage and THDs are taken for a window of 1 second with the faults persistent throughout
the window. The limitation of instantaneous values for feature extraction is the voluminous size of the data.
This would be a major disadvantage for real time application. Hence investigation was carried out to reduce
the number of features without sacrificing the classification accuracy. It was observed that three features

1209
namely the three stator currents are sufficient for detection and classification. Other features are found to
be redundant.
b. RMS values
In this approach, RMS values of the stator current phasors were used instead of instantaneous values. Hence
the total number of features were six, as each phasor is represented by magnitude and its angle.
5. HARDWARE
The hardware set up of VSI driven induction motor is as shown in Figure 4. Two switches were
employed to create cross diode open circuit faults and the gating pulses were removed to the IGBTs to create
open circuit IGBT faults. The specifications of the hardware set up for extraction of current features is as
mentioned in the table 1. The stator current RMS data was extracted from the VSI driven system through
ubidots IoT platform. It was observed to be in validation with the simulation data.
Table 1. Specifications of theVSI driven induction motor
Components Details
Diodes 6 Nos. 1200V, 30A (for three phase Diode Bridge rectifier)-RHRG30120
DC Link Capacitor 100uF
IGBT 6 Nos. 1200V, 50A (for Voltage Source Inverter)-KGT25N120KDA
Microcontroller Arduino Uno
sensors 3 Nos. Hall effect Current sensors -ACS716w, Temperature sensor-DTH11
Wi-Fi module ESP8266 Nodemcu
IoT Platform Ubidots
Figure 4. Voltage Source inverter driven induction motor hardware set up
6. RESULTS AND DISCUSSION
The various dimensions of the algorithms for the fault detection and classification are:
a. Type of machine learning
Two types are used namely multi layer perceptron–neural network (MLP-NN) and support vector machine
(SVM).
b. Type of data
Two types of data namely instantaneous and RMS values were employed to train the model.
c. Number of features
14 features namely rectified voltage, inverter line output voltages, stator currents and their corresponding
THDs, speed instantaneous data were used initially. It was observed through investigation that stator current
phasors are sufficient to detect and classify the faults. Hence six parameters namely induction motor stator
currents phasors were chosen to train the model for RMS data.
The data was split in the 70-30 ratio. MLP-NN was trained with parameter. MLP accuracy is tabulated
as mentioned in Table 2 for an optimum hyper parameter tuning of hidden layers, learning rate, solver,
activation function for 14 and 3 features respectively for instantaneous data. SVM accuracy is also tabulated
in Table 2 for an optimal hyper parameter tuning of kernel, C and gamma with 14 and 3 features respectively
for instantaneous data. For RMS data with stator current phasors were selected as 6 features where each stator
current consists of magnitude and phase angle [13], [14]. The accuracy was found to be 100% with hyper
parameter tuning as mentioned in Table 2.
To evaluate the robustness of the model and to convey the balance between the precision and recall,
F1 score was calculated. The F1 score of 0.958 and 0.944 was obtained for 25 classes, 14 features and 3 features
respectively representing the accurate classification of different types of faults for an instantaneous data using

 ISSN: 2088-8694
1210
MLP. The F1 score of 0.96 and 0.956 was obtained for 25 classes, 14 features and 3 features respectively
representing the accurate classification of different types of faults for an instantaneous data using SVM.
Table 2. Optimum hyper parameters for instantaneous and RMS values
Instantaneous Values MLP-NN:
Features
Optimum Hyper parameters
Accuracy F1-score
Hidden layers Learning rate Solver Alpha Activation Function
14 (50,100, 50) Constant Adam 0.05 tanh 95.6 % 0.958
3 (50,100, 50) Constant Adam 0.0001 relu 95.4% 0.944
Instantaneous values SVM
Features
Accuracy F1-score
Kernel C Gamma
14 rbf 1000 0.001 95.4% 0.96
3 Rbf 1000 1 95.08% 0.956
RMS values MLP-NN
Features
Accuracy F1-score
Hidden layers Learning rate Solver Alpha Activation Function
6 (50,50,50) Constant Adam 0.0001 tanh 100% 1
RMS values SVM
Features Kernel C Gamma Accuracy F1-score
6 rbf 1 1 100% 1
The F1 Score of 1 was obtained for all 25 classes with accurate classification of different fault types,
location and severity for RMS stator current phasors using MLP and SVM in comparison with instantaneous
data the limitation of the instantaneous value method is the voluminous data and the computation time. Though
the accuracy was found to be greater than 95%, it was a pattern-based approach. Hence, another approach
which was used for training and testing the model was by using RMS values of the magnitude of stator currents
only. But the accuracy was limited to only 51% in both MLP and SVM algorithms. Hence, stator current
phasors were considered for training and testing the model. The model was simulated for all 25 classes of faults
with voltage ranging from 390V to 426 V for 0.5HP, squirrel cage 3 phase induction motor rated for 415V,
50Hz.
From the number of case studies carried out, it was observed that minor faults are detected as distortion
in the concordia patterns of stator currents. The performance of the AC drive system is significantly captured
by the phasor stator currents flowing in the circuit as observed by the two-dimensional stator currents obtained
by Clarke’s transformation. A plot of current concordia patterns for different types of electrical faults in voltage
source inverter are as shown in Figure 5 (a) to Figure 5 (e). It can be observed that concordia patterns are
distinct for each type of fault. Hence, the phasor stator currents carry the signature of the faults and can be
effectively used for detection and classification of faults [4], [25][26].
Figure 5. Current concordia patterns for different types of electrical faults in voltage source inverter
The computational training time for the instantaneous values was more than one hour using MLP
algorithm to obtain accurate classification of faults as more number of hyper parameters are to be tuned due to
voluminous data. The computational training time for the RMS values was 12.38 minutes for MLP and 18.52
seconds for SVM. The stator current phasor approach was found to be more beneficial even for practical

1211
implementation as it involves minimal number of sensor data. Both MLP and multiclass SVM gave accurate
results of 100% with exact detection, classification and location of the faults. Since the input is taken over a
wide range and number of case studies have been conducted with different parameter sets for SVM and MLP,
the 100% accuracy cannot be attributed to overfitting. Rather it indicates the efficacy of the method and the
nature of data collection. Multi class SVM is more preferable than MLP for fault detection and classification
as the computational time is less.
7. DEVELOPMENT OF DIAGNOSTIC TOOL
After an accurate detection and classification of faults, it is essential to take corrective measure to
avoid catastrophic effects to the system. Hence a diagnostic tool was developed using TKinter package in
python to visually display the information to the operator. In Diagnostic Tool, the RMS values of stator
currents, inverter line output voltages along with the waveforms, Concordia current pattern and the speed plot
are displayed.
7.1. Case studies
7.1.1.Healthy condition
The diagnostic tool for the healthy condition of the VSI driven induction motor is as shown in the
Figure 6 (a) and Figure 6 (b). The RMS values of the currents and voltages are balanced. The current concordia
pattern for healthy system with PWM-VSI driven induction motor is found to be regular symmetrical pattern
along iBeta axis and the speed is also found to be constant. Prediction fault type and location are displayed
null.
(a)
(b)
Figure 6. Healthy condition of VSI driven induction motor
7.1.2.Single IGBT open fault
The diagnostic tool for the single IGBT open fault condition of the VSI driven induction motor is as
shown in the Figure 7(a) and Figure 7(b). The RMS values of the currents and voltages are unbalanced. The
current Concordia pattern for IGBT open fault with PWM-VSI driven induction motor is found to be

 ISSN: 2088-8694
1212
asymmetrical and the speed is also found to be fluctuating affecting the load. Prediction fault type and location
are displayed exactly. IGBT5 is the third leg upper switch which conducts during positive half cycle. In case
of IGBT5 open fault, it can be clearly visualized that the positive half cycle of phase c stator current is missing
in the waveforms.
(a)
(b)
Figure 7. Unhealthy condition fault prediction for IGBT5 open fault
7.1.3.Cross IGBTs open fault
The diagnostic tool for the cross IGBT open circuit fault condition of the VSI driven induction motor
is as shown in the Figure 8 (a) and Figure 8 (b). The RMS values of the currents are unbalanced and the inverter
line output voltages are also unbalanced which causes increased vibration in the induction motor leading to
severe damage if existed for a long run and also increased THDs leading to power quality issues. The current
concordia pattern for IGBT open fault with PWM VSI driven induction motor is also found to be asymmetrical
and the speed is also found to be fluctuating affecting the load. Prediction fault type and location are displayed
in the tool. IGBT3 is the second leg upper switch which conducts during positive half cycle. In case of IGBT2
and IGBT3 open fault, it can be clearly visualized that the positive half cycle of phase b and negative half cycle
of phase c inverter output currents are is missing in the waveforms.
(a)

1213
(b)
Figure 8. Unhealthy condition fault prediction for IGBT3 and IGBT2 open fault
8. CONCLUSION
Robust and accurate fault diagnostic tools have been developed for faults in the induction motor.
However, in modern power electronic drives, there is a need to develop a diagnostic tool for detection of faults
in the Power electronics circuit. These faults are not easily predictable, as the induction motor often continues
to run. They may lead to catastrophic effects. Hence, an early detection is necessary. In this work, details of
Detection and classification of faults in inverter driven induction motor are presented using MLP-NN and
multiclass SVM. Different sets of features were investigated using both instantaneous values and RMS values
of inverter output line voltages and induction motor stator currents. When instantaneous values were used, the
three stator currents carry the signature of various faults and are sufficient to correctly classify them. However,
the data is voluminous and the computation time is also high. An alternative to use of instantaneous values is
use of RMS values of stator current phasors. The accuracy is also found to be 100 percent with minimal features
and a diagnostic tool is developed as an indicator to the operator. The laboratory prototype developed for
monitoring the power electronics circuitry with minimal sensors has also been presented. The work would be
effective in realising the concept of reliability centred maintenance. Further extension of the work is being
carried out using PCA for high dimensionality reduction.
REFERENCES
[1] Shu-Ying Li, and Lei Xue , “Motor's early fault diagnosis based on support vector machine,” AMIMA 2018 IOP
Publishing, IOP Conf. Series: Materials Science and Engineering 382 (2018) 032047, pp 1-4, 2018
DOI:10.1088/1757-899X/382/3/032047
[2] Khireddine, M.S. Slimane, N. Abdessemed and Yassine Makhloufi, M.T, “Fault detection and diagnosis in induction
motor using artificial intelligence technique,” MATEC Web of Conferences, 2014, vol 16, pp. 1-5, DOI:
10.1051/matecconf/20141610004.
[3] Furqan Asghar, Muhammad Talha, and Sung Ho Kim, “Comparative study of three fault diagnostic methods for three
phase inverter with induction motor,” International Journal of Fuzzy Logic and Intelligent Systems , vol. 17, no. 4,
pp. 245-256, December 2017, DOI: 10.5391/IJFIS.2017.17.4.245.
[4] Faeka Khater, Sebah M. I., and Osama M, “Fault diagnostics in an inverter feeding an induction motor using fuzzy
logic,” Journal of Electrical Systems and Information Technology, vol. 4, no. 1, pp. 10-17, May 2017, DOI:
10.1016/j.jesit.2016.10.005.
[5] Shorouk Ossam aIbrahim, khaled Nagdy Faris, and Esam Abo Elzahab, “Implementation of fuzzy modeling system
for faults detection and diagnosis in three phase induction motor drive system,” Journal of Electrical Systems and
Information Technology , vol. 2, no. 1, pp. 27-46, 2015, DOI: 10.1016/j.jesit.2015.03.004.
[6] V. Gomathy and Selvaperumal S, “Fault detection and classification with optimization techniques for a three-phase
single-inverter circuit,” Journal of Power Electronics, vol. 16, pp. 1097-1109, May 2016, DOI:
10.6113/JPE.2016.16.3.1097.
[7] H. Yang, J. Zhao and F. Wu, “Current similarity based fault diagnosis for induction motor drives with discrete wavelet
transform,” 2016 Prognostics and System Health Management Conference (PHM-Chengdu), Chengdu, 2016, pp. 1-
6, DOI: 10.1109/PHM.2016.7819841.
[8] Furqan Asghar, Muhammad Talha, Sung Ho Kim, ”Neural network based fault detection and diagnosis system for
three-phase inverter in variable speed drive with induction motor”, Journal of Control Science and
Engineering, vol. 2016, pp. 1-12, 2016. DOI: 10.1155/2016/1286318.
[9] Dong-Eok Kim and Dong-Choon Lee, “Fault diagnosis of three-phase PWM inverters using wavelet and SVM,”
IEEE International Symposium on Industrial Electronics, 2008, pp. 329-334, DOI: 10.1109/ISIE.2008.4676998.
[10] Wei, Li, Xu, and Huang, “A review of early fault diagnosis approaches and their applications in rotating
machinery,” Entropy, vol. 21, no. 4, p. 409, Apr. 2019, DOI: 10.3390/e21040409.

 ISSN: 2088-8694
1214
[11] Ubale, M.R., Dhumale, R.B., and Lokhande, S, “Open switch fault diagnosis in three phase inverter using diagnostic
variable method,” International Journal of Research in Engineering and Technology, vol. 02, no. 12, pp. 636-640,
2013.
[12] H. Zhao, W. Deng, R. Yao, M. Sun, Y. Luo, and C. Dong, “Study on a novel fault diagnosis method based on
integrating EMD, fuzzy entropy, improved PSO and SVM,” Journal of Vibroengineering, vol. 19, no. 4, pp. 2562-
2577, Jun. 2017, DOI: 10.21595/jve.2017.18052.
[13] Jlassi, J. O. Estima, S. K. El Khil, N. M. Bellaaj and A. J. M. Cardoso, “A robust observer-based method for IGBTs
and current sensors fault diagnosis in voltage-source inverters of PMSM drives,” in IEEE Transactions on Industry
Applications, vol. 53, no. 3, pp. 2894-2905, May-June 2017, DOI: 10.1109/TIA.2016.2616398.
[14] Jian-jian, Z., Yong, C., Zhang-Yong, C., and Anjian, Z., “Open-switch fault diagnosis method in voltage-source
inverters based on phase currents,” IEEE Access, vol. 7, pp. 63619-63625, 2019, DOI:
10.1109/ACCESS.2019.2913164.
[15] R. Jyothi, R. Jayapal and K. U. Rao, “Severity and impact of faults on current harmonics in inverter-fed AC drives,”
2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Melbourne, VIC, 2016, pp. 401-405, DOI:
10.1109/ISGT-Asia.2016.7796419.
[16] R. Jyothi, Shaik Mohammed Zahid, Mohammed Sufyan Zain, Reshma Begum; Syed Afzal and K. Uma Rao, et al.,
“Automatic fault diagnosis system for voltage source inverter driven induction motor,” 2019 3rd International
Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), NOIDA, India, 2019,
pp. 477-481, DOI: 10.1109/RDCAPE47089.2019.8979041.
[17] Shalini Vasishta, and KR Rekha, “A Survey: space vector PWM (SVPWM) in 3φ voltage source inverter (VSI),”
International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no.1, pp. 11-18, February 2018, DOI:
10.11591/ijece.v8i1.pp11-18.
[18] Akhilesh Sharma, Anandh N., and Sarsing Gao, “Modulation index effect on inverter based induction motor drive,”
International Journal of Power Electronics and Drive System (IJPEDS), vol. 11, no. 4, pp. 1785-1798, Dec 2020,
DOI: 10.11591/ijpeds.v11.i4.pp1785-1798.
[19] Amine Mohamed Khelif, Azeddine Bendiabdellah, and Bilal Djamal Eddine Cherif, “Short-circuit fault diagnosis of
the DC-Link capacitor and its impact on an electrical drive system,” International Journal of Electrical and Computer
Engineering (IJECE), vol. 10, no. 3, pp. 2807-2814, June 2020, DOI: 10.11591/ijece.v10i3.pp2807-2814.
[20] Rekha SN, P Aruna Jeyanthy, and D Devaraj, “Relevance vector machine based fault classification in wind energy
conversion system,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 3, pp. 1506-
1513, June 2019, DOI: 10.11591/ijece.v9i3.pp1506-1513.
[21] Khadidja El Merraoui, Abdellaziz Ferdjouni, and M’hamed Bounekhla, “Real time observer based stator fault
diagnosis for IM,” International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 1, pp. 210-
222, Feb 2020, DOI: 10.11591/ijece.v10i1.pp210-222.
[22] Lachtar Salah, Ghoggal Adel, Koussa Khaled, Bouraiou Ahmed, and Attoui Issam, “Broken rotor bar fault diagnostic
for DTC Fed induction motor using stator instantaneous complex apparent power envelope signature analysis,”
International Journal of Power Electronics and Drive System (IJPEDS), vol. 10, no. 1, pp. 1187-1196, Sep 2019,
10.11591/ijpeds.v10.i3.pp1187-1196.
[23] N. F. Fadzail, S. Mat Zali, “Fault detection and classification in wind turbine by using artificial neural network,”
International Journal of Power Electronics and Drive System (IJPEDS), vol. 10, no. 3, pp. 1687-1693, Sep 2019,
DOI: 10.11591/ijpeds.v10.i3.pp1687-1693.
[24] A. O. Di Tommaso, F. Genduso, R. Miceli and G. R. Galluzzo, “Current fault signatures of Voltage Source Inverters
in different reference frames,” 2016 International Symposium on Power Electronics, Electrical Drives, Automation
and Motion (SPEEDAM), Anacapri, 2016, pp. 599-604, DOI: 10.1109/SPEEDAM.2016.7525977.
[25] Yong C, Zhang JJ, Chen ZY, “Current observer-based online open-switch fault diagnosis for voltage-source inverter”,
ISA Transactions, pp. 445-453, Apr 2020, DOI: 10.1016/j.isatra.2019.09.019.
[26] Chouhan Abhisar, Gangsar Purushottam, Porwal Rajkumar, Mechefske Christopher K, “Artificial neural network
based fault diagnostics for three phase induction motors under similar operating conditions”, Vibroengineering
PROCEDIA 2020,Vol. 30, pp.55-60,Apr 2020,DOI: 10.21595/vp.2020.21334.
BIOGRAPHIES OF AUTHORS
Jyothi R received B.E in Electrical and Electronics Engineering and M.Tech in Power
Electronics From Visvesvaraya Technological University in the year 2009 and 2013
respectively.She is currently pursuing Ph.D in Visvesvaraya Technological University. She has
8 years of teaching experience and currently working as Assistant Professor in Department of
Electrical and Electronics Engineering, RV College of Engineering, Bangalore. Her research
interests include Power Electronics, Drives and Fault diagnosis using Artificial Intelligence.
She has authored 5 articles in the area of Power Electronics and Drives. E-mail:
jyothir@rvce.edu.in

1215
Tejas Holla is pursuing final year B.E in Electrical and Electronics Engineering at RV College
of Engineering, Bangalore. His areas of interest include Power Electronics and Drives, Machine
Learning, Internet of Things and Embedded Systems.He has also participated in National Level
Formula Bharat Electric vehicle engineering design Competition. E-mail:
tejasholla.ee17@rvce.edu.in
K Uma Rao obtained her B.E in Electrical Engineering and M.E in Power systems from
University Visvesvaraya College of Engineering, Bangalore in 1984 and 1987 respectively. She
received her Ph.D from Indian Institute of Science, Bangalore in 1997.She has 32 years of
teaching experience and currently working as Professor in department of Electrical and
Electronics Engineering, R.V. College of Engineering, Bangalore. Her research interests
include FACTS, Custom Power, Power Quality, renewable energy and technical education. Dr.
K Uma Rao has published 12 technical books and more than 170 technical publications in
national and international journals and conferences. She had also received 11 awards of
excellence from various technical societies. E-mail: umaraok@rvce.edu.in
Jayapal R obtained his B.E in Electrical Engineering and M.E in Power systems from
University Visvesvaraya College of Engineering, Bangalore respectively. He received his Ph.D
from Vivesvaraya Technological University in 2012. He has 29 years of teaching experience
and currently working as Principal at RV Institute of Technology and Management, Bangalore.
His areas of interest include Robust control sysytems, loop shaping techniques, real time
simulator and robust power system stabilizer. He has authored 20 articles in the areas of Power
systems and Power Electronics. E-mail: jayapalr@rvce.edu.in

Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

More Related Content

What's hot (20)

Similar to Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor (20)

More from International Journal of Power Electronics and Drive Systems (20)

Recently uploaded (20)

Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor