Dissertation / Project / Project Work Title:
Predictive Maintenance of a Medium Voltage Motor
Using its Digital Twin.
Course No.: DMZG628T
Course Title: Dissertation / Project / Project Work
Dissertation / Project /Project Work Done by: GIREESH.N
Student Name: GIREESH.N
BITS ID: 2021HT56048
Degree Program: M. Tech in Digital Manufacturing
Research Area: IIoT
Dissertation / Project Work carried out at:
RAK Company for White Cement and Construction Materials, United Arab
Emirates (UAE)
BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE,
PILANI
VIDYA VIHAR, PILANI, RAJASTHAN -
333031.
January 2023
Contents:
1. Broad Area of Work 3
2. Background 3
3. Objectives 3
4. Scope of Work 4
5. Plan of Work 4
6. Literature References 5
7. Particulars of the Supervisor and Additional Examiner 6
8. Remarks of the Supervisor 6
1. Broad Area of Work:
Digital Twin (DT) is an innovative new concept and technology that evolved with the
advancement of Industry 4.0. DT is one of the enabling tools of Industry 4.0 that
integrates the actual physical system with its virtual replica with the help of models,
sensors, data, and software to monitor and analyze future performance. The role of
Digital Twin in the advancement of industrial operations particularly in predictive
maintenance is highly significant.
The current research project focus in the following areas:
● Advanced physical modelling of the machines with dynamic modelling and virtual
sensors.
● Simulation tuning of the physics-based models during operation of the machines.
● Realization of 5-dimension D.T model and usage of its outputs in Predictive
Maintenance.
● Usage of Machine Learning and Artificial Intelligence in DT.
2. Background:
Predictive maintenance is a technique for creating a more sustainable, safe, and profitable
industry. One of the key challenges for creating predictive maintenance systems is the lack
of failure data, as the machine is frequently repaired before failure. Digital Twins provide a
real-time representation of the physical machine and generate data that the predictive
maintenance algorithm can use.
DT technologies can be deployed to predict the failure of components based on multiple data
sources. The related knowledge-base and DT systems need to be devised to collect such data,
including a method that will amalgamate this data to produce an accurate forecast.
3. Objectives:
The objectives of my project are as follows:
● Development of physical modelling of a medium voltage induction motor.
● Simulation and tuning of the physical modelling with input received during
operation of the motor.
● Operation of the Digital Twin of the motor.
● Utilization of the output of the D.T for finding out the remaining useful life of
major components of the motor and thereby develop a sustainable maintenance
plan.
4. Scope of Work:
Scope of this dissertation is to develop a Digital Twin of a medium voltage(6.6KV) slip ring
induction motor and utilize the D.T for assessing the remaining useful life of major
components of the motor for an effective predictive maintenance plan.
5. Plan of Work:
Phases Start Date-End Date Work to be done
Dissertation 11 Jan 2023 – 22 Jan 2023 Literature Review and prepare
Outline Dissertation Outline
Design & 23 Jan 2023 – 10 March 2023 Design & Development Activity
Development of
Digital Twin
Operation of the 11March 2023 – 7 April 2023 Testing, Evaluation and Conclusion
D.T and
utilization of its
output
Dissertation 08 April 2023-15 April 2023 Submit Dissertation to
Review Supervisor & Additional
Examiner for review and
feedback
Submission 16April 2023 -23 April 2023 Final Review and submission of
Dissertation
6. Literature References:
The state art is the base for any successful research project. In current project, the
literatures on Digital Twin and Predictive Maintenance are considered. The following are
referred journals taken from Google Scholar for the preliminary literature review.
[1] P. Aivaliotis, K. Georgoulias & G. Chryssolouris (2019) The use of Digital Twin for
predictive maintenance in manufacturing, International Journal of Computer Integrated
Manufacturing, 32:11, 1067-1080, DOI: 10.1080/0951192X.2019.1686173.
[2] Falekas, G.; Karlis, A. Digital Twin in Electrical Machine Control and Predictive
Maintenance: State-of-the-Art and Future Prospects. Energies 2021, 14, 5933. https://
doi.org/10.3390/en14185933
[3] Enabling technologies and tools for digital twin Qinglin Qi a , Fei Tao a, *, Tianliang
Hu b , Nabil Anwer c , Ang Liu d , Yongli Wei b , Lihui Wang e , A. Y.C. Nee f
[4] Predictive maintenance using digital twins: A systematic literature review Raymon van
Dinter a,b , Bedir Tekinerdogan b,* , Cagatay Catal c a Sioux Technologies, Apeldoorn, The
Netherlands b Information Technology Group Wageningen University & Research,
Wageningen, The Netherlands c Department of Computer Science and Engineering, Qatar
University, Doha, Qatar.
[5] Extending the Multiphysics Modelling of Electric Machines in a Digital Twin Concept
Gabriel Mendes1 , Ângela Ferreira1 1 Research Centre in Digitalization and Intelligent
Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-
253 Bragança, Portugal, www.cedri.ipb.pt
[6] Real-Time Digital Twin of a Wound Rotor Induction Machine Based on Finite
Element Method Sami Bouzid ∗ , Philippe Viarouge and Jérôme Cros Department of
Electrical and Computer Engineering, Laval University, Quebec, QC G1V 0A6, Canada;
philippe.viarouge@gel.ulaval.ca (P.V.); jerome.cros@gel.ulaval.ca (J.C.) *
Correspondence: sami.bouzid.1@outlook.com; Tel.: +1-8197639153
[7] Digital Twin Design for Maintenance Optimization Oliver Daviesa , Abhishek
Makkattila , Ce Jianga , Maryam Farsia * School of Aerospace, Transport and
Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK *
[8] Methodology for enabling Digital Twin using advanced physics-based modelling in
predictive maintenance P. Aivaliotisa , K. Georgouliasa , Z. Arkoulia , S. Makrisa * a
Laboratory for Manufacturing Systems & Automation, University of Patras, Rion Patras,
26504, Greece
7. Particulars Of the Supervisor and Additional Examiner:
Supervisor Additional Examiner
Name Venugopalan Ganapathy Arun Kumar
Qualification Bachelor’s in mechanical engineering B.E in Mechanical Engineering
Designation Technical Director Mechanical Engineer
Employing RAK Company for White Cement RAK Company for White
Organization and Construction Materials, UAE Cement and Construction
and Location Materials, UAE
Phone No.
(with STD 00971501860136 00971553517705
Code)
g.venugopalan@rakwhitecement.ae
Email Address a.kumar@rakwhitecement.ae
8. Remarks of the Supervisor:
This project seems to very promising and can have appreciable impact on the maintenance
aspects of the cement plant. The student has covered all the relevant literature review. The
project is holding the current domain of research and highly justifiable the level of
dissertation for master studies. The outcome covered by the project seems to be very
interesting and include all the state of arts in today’s era of IIoT4.0. The project covers the
development of a Digital Twin of a motor and utilize it for assessing the life of various
components of the motor. This project also contains the prospect of publication in a technical
journal in future. After the initial meeting with the student, I have got an impression that he
is very serious on the project and have the skills to develop the D.T model. I do approve me
as the Supervisor of this Project.
Information about the Supervisor: A highly qualified cement professional with 40 plus
years of multinational experience in cement manufacturing, business process set up,
projects and engineering with proven capabilities of setting and achieving inspirational
goals by collaborating with senior board management to fulfill organizational objectives.
