Smart Innovation, Systems and Technologies 239

Veeredhi Vasudeva Rao

Adepu Kumaraswamy
Sahil Kalra
Ambuj Saxena Editors

Computational and
Experimental Methods
in Mechanical
Engineering
Proceedings of ICCEMME 2021
Smart Innovation, Systems and Technologies

Volume 239

Series Editors
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Shoreham-by-sea, UK
Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK
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Veeredhi Vasudeva Rao · Adepu Kumaraswamy ·
Sahil Kalra · Ambuj Saxena
Editors

Computational
and Experimental Methods
in Mechanical Engineering
Proceedings of ICCEMME 2021
Editors
Veeredhi Vasudeva Rao Adepu Kumaraswamy
Department of Mechanical & Industrial Department of Mechanical Engineering
Engineering Defence Institute of Advanced Technology
College of Science, Engineering Pune, India
and Technology University of South Africa
Pretoria, South Africa Ambuj Saxena
Department of Mechanical Engineering
Sahil Kalra G. L. Bajaj Institute of Technology &
Department of Mechanical Engineering Management
Indian Institute of Technology Jammu Greater Noida, India
Jammu, India

ISSN 2190-3018 ISSN 2190-3026 (electronic)

Smart Innovation, Systems and Technologies
ISBN 978-981-16-2856-6 ISBN 978-981-16-2857-3 (eBook)
https://doi.org/10.1007/978-981-16-2857-3

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Preface

International Conference on Computational and Experimental Methods in Mechan-

ical Engineering (ICCEMME-2021) has been the third conference of its series
organized by the Department of Mechanical Engineering of G. L. Bajaj Institute
of Technology and Management, Greater Noida, Uttar Pradesh, India. The institute
is located in the vicinity of industrial hub. Therefore, it was decided to provide a
forum to bring together scientists, speakers from industries, university professors,
graduate students and mechanical engineers, presenting new research in science,
technology, and engineering.
The motive of this conference was to provide an opportunity to share their inno-
vative ideas in the form of a paper presentation. The conference attracted many
participants working in various fields of engineering. Research articles were based
on Design, Mechanics, Materials, Energy, Industrial & Production Engineering
areas such as Sustainable Manufacturing Systems, Rapid Prototyping, Manufac-
turing Process Optimization, Machining, and Machine Tools, Casting, Welding,
Forming, Machining, Machine Tools, Computer-Aided Engineering, Manufacturing,
Management, Automation and Metrology, Industrial, Management and Marketing,
etc. During the conference, about ten delegates were joined from various contraries
and delivered keynote lecture on the theme of the conference. All papers were
critically reviewed by two reviewers from National/International authors.
More than 500 hundred manuscripts were submitted to the conference, topics
ranging from the latest research in the field of aerodynamics and fluid mechanics,
artificial intelligence, IOT, blockchain rapid manufacturing technologies, and proto-
typing, remanufacturing, refrigeration and air conditioning, renewable energies tech-
nology, IC engines, turbo machinery metrology, and computer-aided design and
inspection.
Furthermore, we would like to extend our appreciation to all the authors for
contributing their valuable research to the conference. The committee is also grateful
to all the reviewers who spared their time to carefully review all the assigned research
articles and to all the committee members for their great effort in making their
conference into grant success.

v
vi Preface

We are thankful to all sponsored agencies who gave us their cooperation and
funding support.
We are thankful to our management and the director of G L Bajaj Institute of Tech-
nology and Management, Greater Noida, Uttar Pradesh, India, for their continuous
source of inspiration and valuable support. We are thankful to all the members of the
organizing committee for their contribution in organizing the conference. Last but
not least, we thank Springer for its professional assistance, particularly, Mr. Aninda
Bose who supported this publication.

Pretoria, South Africa Veeredhi Vasudeva Rao

Pune, India Adepu Kumaraswamy
Jammu, India Sahil Kalra
Greater Noida, India Ambuj Saxena
Contents

1 Damage Detection in Eggshell Using Lamb Waves . . . . . . . . . . . . . . . . 1

Sahil Kalra, Maninder Meenu, and Deepak Kumar
2 Study of Steering System for an Electric Trike-Ackerman
Steering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Sachin Patel, Ritwick Rawat, Shantanu, Aditya Kumar,
and Amardeep
3 Experimental Study of Parameter Affecting the Thermal
Conductivity of Composite Materials and Alloy: A Review . . . . . . . . 19
Omprakash Yadav, Ankit Chhonkar, Rahul Sharma,
Sandeep Chauhan, Ashish Bansal, Navneet Singh Baghel,
Himanshu Sharma, and Devesh Sharma
4 An Experimental and Mathematical Analysis for Improvement
of Gas Stove Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Akanksha Mathur, Rohit Singh Lather, Vinit Chauhan,
Rahul Sharma, and Tushar Mehta
5 Theoretical Investigation of Physical, Mechanical
and Thermal Properties of Al–Cu Functionally Graded
Material Through Gradation Laws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Pankaj Kumar Chauhan and Sabah Khan
6 Thermodynamic Analysis of N2 O Transcritical Cycle Using
Dedicated Mechanical Subcooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Pradeep Kumar and Shivam Mishra
7 Effect of PEO Concentration on Electrochemical
and Mechanical Properties of PVDF, PEO and LATP Blended
Solid Polymer Electrolyte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Ponam and Parshuram Singh

vii
viii Contents

8 Applicability of Banana Fiber as Reinforcement in Composites . . . . 77

Sandeep Chauhan, Tarun Kumar Gupta,
and Vishal Shankar Srivastava
9 State of Art on Microstructural and Mechanical
Characterization of Wire and Arc Additive Manufacturing
(WAAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Aman Verma, Himanshu Yadav, Kuldeep Kumar,
Prince Kumar Singh, Mayank Sharma, Vishal Shankar Srivastava,
and Ashish Kumar Srivastava
10 Effect of La3+ Substitution on Structural, Magnetic,
and Multiferroic Properties of Bismuth Ferrite
(Bi1-x Lax FeO3 ) Nanoceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Shama Farozan, Harendra Kumar Satyapal, Om Priya,
Saurabh Sharma, and Singh Sonu Kumar
11 Experimental Analysis of Wear and Mechanical
Characteristics of Aluminium Matrix Composite Fabricated
Through Powder Metallurgy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Harvendra Singh, Harshit Bahri, and Kaushalendra Kumar Singh
12 Recent Advancements on Structural Health Monitoring Using
Lamb Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Deepak Kumar, Sahil Kalra, and Mayank Shekhar Jha
13 Global Overview of Large-Scale Photovoltaic System and Its
Electrical Energy Storage Implementation . . . . . . . . . . . . . . . . . . . . . . . 143
Bajrangi Maurya
14 Consideration Analysis of Stress Distribution Using
Automotive Chassis for Heavy Vehicle Transports . . . . . . . . . . . . . . . . 155
Manish Saraswat, Pradeep Kumar Singh, and Rajat Yadav
15 A Review on Gas Sensor Technology and Its Applications . . . . . . . . . 165
Pooja Saxena and Prashant Shukla
16 CFD Study of Two-Dimensional Profile Geometry of an Airfoil . . . . 177
Harshit Bahri, Kaushalendra Kumar Singh, and Harvendra Singh
17 Tunable Filter at Second Transmission Window Containing
1D Ternary Superconductor/Dielectric Photonic Crystals . . . . . . . . . 189
Vimal, Sanjeev Sharma, Anil Kumar Sharma, and Rajesh Tiwari
18 A Numerical Method for a Problem Occurring in Conduction
of Heat Through a Solid and Other Applications . . . . . . . . . . . . . . . . . 197
Shradha Gupta and Sanjeev Sharma
Contents ix

19 Emotional Intelligence: Assessing Its Impact on Financial

Productivity in an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Shivi Mittal, Prabhat Srivastava, and Deepa Gupta
20 Acquiring FG Homogeneous Composite Shell Structure Using
Finite Element Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Aman Sharma, Rajat Yadav, and Vikas Kumar Sharma
21 An Optimal Control Scheme for Thermal-Hydro System
with Distributive Energy Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Nagendra Kumar, Brijesh Prasad, Kailash Sharma,
Rajat Mehrotra, and Vinamra Kumar Govil
22 Effect of Material Hardness and Operating Conditions
on Wear Rate of Sliding Tribopair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Mohammad Hanief
23 Fluorescence Characteristics of Coumarin Derivatives
with Divalent Metal Ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Pooja Rani
24 A Novel Method for Performance Enhancement of PV Module
Using Graphene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Pragati Tripathi, M. A. Ansari, and Rajat Mehrotra
25 Load Frequency Control in Deregulated-Hybrid Power
System Integrated with Energy Generation/Storage System . . . . . . . 275
Nagendra Kumar, Mohit Bansal, Shivendra Kaura,
and Priyanka Datta
26 Transfer Learning-Based Brain Tumor Detection Using MR
Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
Priyanka Datta and Rajesh Rohilla
27 Recyclability of Tractor’s Engine Component: A Case
Analysis of Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
Bhupendra Prakash Sharma, Rahul Sindhwani,
Priyank Srivastava, Pranav Malhotra, Harkirat Singh,
Shorya Gupta, and Priyanka Singh
28 Feature Extraction of Face Recognition Techniques Utilizing
Neural System as a Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Amrita Rai, C. Shylaja, and Puneet Kumar Mishra
29 Investigating Strategies and Parameters to Predict
Maintenance of an Elevator System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Jasmine Awatramani, Gaayan Verma, Nitasha Hasteer,
and Rahul Sindhwani
x Contents

30 Groundwater Recharge Using Artificial Filter Mechanism . . . . . . . . 333

Deepti Dohare
31 A Computational Technique to Generate Coupler Curve
Equation of 6-bar Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
Khalid Nafees, Mohd. Qamar Tanveer, Ajay Mahendru,
and Anil P. Singh
32 Analysis on Manufacturing Automated Guided Vehicle
for MSME Projects and Its Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . 357
Rishabh Chaturvedi, Anas Islam, and Aman Sharma
33 Design and Fabrication of Self-balanced Electric Two-Wheeler . . . . 367
Karanjot Singh, Jaydeep Singh, Amardeep,
Shailesh Kumar Singh, and Harshit Kumar
34 Use of Response Surface Methodology for Optimization
of Received Signal Strength for GSM Signals in Challenging
Atmospheric Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
Shilpa Choudhary, Abhishek Sharma, Mudita Vats, and Vidit Shukla
35 Structural Analysis and Completion of Fatigue Axial-Flow
Compressor Using Finite Element ANSYS Technology . . . . . . . . . . . . 387
Rishabh Chaturvedi, Vikas Kumar Sharma, and Manoj Kumar
36 Deep Neural Network for Facial Emotion Recognition System . . . . . 397
Vimal Singh, Sonal Gandhi, Rajiv Kumar, Ramashankar Yadav,
and Shivani Joshi
37 An Analytical Study of Partial Replacement of Cement
and Quartz Sand by Feldspar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
Rajat Yadav, Ravindra Pratap Singh, and Kuwar Mausam
38 A Review of Pick and Place Operation Using Computer Vision
and ROS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
Aditya Mathur, Chetan Bansal, Sandeep Chauhan,
and Omprakash Yadav
39 Efficient Activated Metal Inert Gas Welding Procedures
by Various Fluxes for Welding Process . . . . . . . . . . . . . . . . . . . . . . . . . . 419
Aman Sharma, Rishabh Chaturvedi, and Pradeep Kumar Singh
40 Computational Analysis of Heat Transfer Characteristics
of TiO and CuO2 Mixed with Water for Heat Exchanger
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Anuj Gupta, Vinod Kumar Yadav, Rishabh Kumar,
and Nirbhay Singh Parmar
Contents xi

41 Investigation on Automobile Fire and Its Root Causes . . . . . . . . . . . . 445

Shailendra Singh Chauhan, Aditya Kumar Bhati, Mihir Tomar,
Pankaj Kumar Mavi, Siddharth Singh Gurjar, Yash Chauhan,
and S. S. Saxena
42 Examination and Analysis of Thermal Steam Boiler Using
Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
Pradeep Kumar Singh, Rishabh Chaturvedi, and Manoj Kumar
43 Study on the Development and Problems Faced in Electric
Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
Ankit Sharma, Dhruv Sethi, Ishu Kumar, Jatin Yadav,
Siddhant Bhatia, and Amardeep
44 Enhancing Heat Transfer Rate by Optimization
of Commercial Refrigeration Condenser and Its Design
Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
Anas Islam, Vikas Kumar Sharma, and Manish Saraswat
45 FPGA Implementation of Low Power Pre-processor Design
for Biomedical Signal Processing Application . . . . . . . . . . . . . . . . . . . . 489
Kirti, Harsh Sohal, and Shruti Jain
46 Design and Implementation of Smart Energy Meter
with Real-Time Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499
Prem Chand Vashist and Ashish Tripathi
47 Optimization of Glass, Carbon and Graphite Fiber Mono
Composite Leaf Spring Using Genetic Algorithm . . . . . . . . . . . . . . . . . 509
Aatif Ameer, Vikas Tiwari, Vansh Pokhriyal, Alok Saxena,
Divakar Tiwari, and Ranjeet Kumar Singh
48 Prospects of bioCNG in Modified Diesel Engine . . . . . . . . . . . . . . . . . . 519
Rahul Sharma, Amit Pal, and N. A. Ansari

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539

Editors and Contributors

About the Editors

Veeredhi Vasudeva Rao holds Bachelor’s Degree in Mechanical Engineering and

Master’s Degree with specialization in Heat Transfer from Andhra University, India.
He holds a Doctoral Degree from the Indian Institute of Science (IISc) Bangalore,
India, with specialization in Heat Transfer from faculty of engineering. During his
studies at master’s and doctoral level, he was a recipient of (GATE) National Schol-
arship and Council of Scientific and Industrial Research (CSIR) fellowships, respec-
tively. He was Postdoctoral Fellow at Nanyang Technological University, Singa-
pore. Formerly, he was Principal of SreeNidhi Institute of Science and Technology,
Hyderabad, India. He was also Director of Technology Development and Test center
(TDTC) recognized by Government of India. He has published more than 100
research papers in peer-reviewed National and International Journals of repute and
peer-reviewed conference proceedings. He has guided 4 Doctoral Research Scholars
and 25 master’s students for successful completion. He has presented several research
papers in international conference in India and the USA.

Dr. Adepu Kumaraswamy graduated in Mechanical Engineering from Kakatiya

University, Warangal, Telangana, India, in 1992 and received Master’s in Design and
Production Engineering from National Institute of Technology (NIT), Warangal, in
1995. Subsequently, he has pursued doctoral research work in the area of static
and dynamic indentation behavior of materials for defence applications at Osmania
University, Hyderabad, in 2008. He has authored 36 publications in reputed jour-
nals and good number of publications in various conferences. He was Principal
Investigator of three sponsored R&D projects in the area of tribological studies of
hydraulic seals, impact mechanics and high strain rate deformation behavior of mate-
rials for defence applications. He has 13 years of post-M.Tech. and 12 years of post-
Ph.D. teaching experience. He was Professor and Associate Dean (R&D) at SNIST
(autonomous institution), Hyderabad, during 2008–2010. He is currently Professor in
Mechanical Engineering, Head, Department of Technology Management, and Head,
Materials Management Group, Defence Institute of Advanced Technology (DU),

xiii
xiv Editors and Contributors

Pune, funded by Department of Defence R&D, Ministry of Defence, Government of

India. He has guided four Ph.Ds, one MS (R) and over fifty M.Tech. projects and
currently guiding six Ph.D. students.

Sahil Kalra was born in Kaithal, a small city in Haryana. He completed his B.Tech.
in Mechanical Engineering from Kurukshetra University in 2011. During his B.Tech.
degree, he received a full tuition fee waiver from the university. In his first attempt,
he qualified GATE exam with 96 percentile and joined M.Tech. in the Department
of Mechanical Engineering, NIT Jalandhar. Based on the performance, he was given
an opportunity to serve as Assistant Professor in the same institute. However, after
serving for one semester, he moved to IIT Kanpur as a full-time Ph.D. student.
There he worked on a research project sponsored by Space Application Centre,
Ahmedabad, Indian Space Research and Organization (ISRO). He worked jointly
with the ISRO scientists and developed a reconfigurable antenna for space applica-
tions. He defended his Ph.D. degree in May 2, 2019. From his Ph.D. research, he
has published four reputed International Journal articles, two patents and six confer-
ences, and symposiums. He is recipient of two international and two national awards
at different conferences and symposiums. He has been offered a prestigious fellow-
ship, namely Faculty in Science and Engineering, at the University of Groningen
(QS ranking 65) in the Netherlands (Europe). Currently, he is serving as Assistant
Professor in the Department of Mechanical Engineering, IIT Jammu. His research
interests are system dynamics and control, mechatronics, robotics, vibration control,
modal analysis, smart materials and smart structures. He is passionate about the algo-
rithm development for different finite element method-based engineering problems
and its solution using using Abaqus and MATLAB.

Dr. Ambuj Saxena completed his bachelor degree in Mechanical Engineering

Department from Uttar Pradesh Technical University, Lucknow (Uttar Pradesh),
India, in 2008. In his first attempt, he qualified GATE 2008 exam with 85.18 percentile
and completed Master’s degree in Mechanical Engineering from Dr. B R Ambedker
National Institute of Technology (NIT), Jalandhar, in 2011. Further, he was completed
his doctoral degree (Ph.D.) in the area of constitutive modelling and high strain rate
deformation behavior of armor steel weldments for defence applications at Defence
Institute of Advanced Technology (DIAT-DU), Pune, in 2019. He has completed his
doctoral work research experimentation at Defence Metallurgical Research Labora-
tory, Hyderabad. He has authored 21 SCI/SCIE and 5 Scopus publications in reputed
journals. He has published 3 research papers at the reputed international conference.
He is the reviewer of several international journals. He is currently working as an
Associate Professor in the Department of Mechanical Engineering, G. L. Bajaj Insti-
tute of Technology and Management, Greater Noida. He is a lifetime member of the
Indian Structural Integrity Society (InSIS).
Editors and Contributors xv

Contributors

Amardeep Department of Mechanical Engineering, GL Bajaj Institute of Tech-

nology and Management, Greater Noida, India
Aatif Ameer G.L. Bajaj Institute of Technology and Management, Greater Noida,
UP, India
M. A. Ansari Department of Electrical Engineering, Gautam Buddha University,
Greater Noida, India
N. A. Ansari Department of Mechanical Engineering, Delhi Technological Univer-
sity, Delhi, India
Jasmine Awatramani Amity University, Noida, Uttar Pradesh, India
Navneet Singh Baghel Department of Mechanical Engineering, G.L Bajaj Institute
of Technology and Management Greater Noida, Greater Noida, India
Harshit Bahri Department of Mechanical Engineering, G. L. Bajaj Institute of
Technology and Management, Greater Noida, UP, India
Ashish Bansal Department of Mechanical Engineering, G.L Bajaj Institute of
Technology and Management Greater Noida, Greater Noida, India
Chetan Bansal Department of Mechanical Engineering, G L Bajaj Institute of
Technology and Management, Greater Noida, India
Mohit Bansal G. L. Bajaj Institute of Technology & Management, Greater Noida,
U.P., India
Aditya Kumar Bhati Department of Mechanical Engineering, G.L. Bajaj Institute
of Technology & Management, Greater Noida, India
Siddhant Bhatia Department of Mechanical Engineering, GL Bajaj Institute of
Technology and Management, Greater Noida, India
Rishabh Chaturvedi IET Department of Mechanical Engineering, GLA Univer-
sity, Mathura, Uttar Pradesh, India
Pankaj Kumar Chauhan Department of Mechanical Engineering, Jamia Millia
Islamia, New Delhi, India
Sandeep Chauhan Department of Mechanical Engineering, G. L. Bajaj Institute
of Technology & Management, Greater Noida, India
Shailendra Singh Chauhan Department of Mechanical Engineering, G.L. Bajaj
Institute of Technology & Management, Greater Noida, India
Vinit Chauhan Department of Mechanical Engineering, The NorthCap University,
Gurugram, Haryana, India
xvi Editors and Contributors

Yash Chauhan Department of Mechanical Engineering, G.L. Bajaj Institute of

Technology & Management, Greater Noida, India
Ankit Chhonkar Department of Mechanical Engineering, G.L Bajaj Institute of
Technology and Management Greater Noida, Greater Noida, India
Shilpa Choudhary Department of Electronics and Communication Engineering,
GL Bajaj Institute of Technology and Management, Greater Noida, UP, India
Priyanka Datta G. L. Bajaj Institute of Technology & Management, Greater Noida,
U.P., India
Deepti Dohare GL Bajaj Institute of Technology and Management, Greater Noida,
India
Shama Farozan Aryabhatta Center for Nanoscience and Nanotechnology, Aryab-
hatta Knowledge University, Patna, Bihar, India
Sonal Gandhi G L Bajaj Institute of Technology and Management, Greater Noida,
India
Vinamra Kumar Govil EE Department, I.E.T Lucknow, Lucknow, India
Anuj Gupta Department of Mechanical Engineering, Vishveshwarya Group of
Institutions, Dadri, UP, India
Deepa Gupta Department of Management Studies, G.L. Bajaj Institute of Manage-
ment & Research, A.P.J. Abdul Kalam Technical University, Gautam Buddha Nagar,
India
Shorya Gupta Department of Mechanical Engineering, Amity University, Noida,
Uttar Pradesh, India
Shradha Gupta Department of Applied Sciences, GL Bajaj Institute of Technology
and Management, Greater Noida, Uttar Pradesh, India
Tarun Kumar Gupta Department of Mechanical Engineering, G. L. Bajaj Institute
of Technology & Management, Greater Noida, India
Siddharth Singh Gurjar Department of Mechanical Engineering, G.L. Bajaj Insti-
tute of Technology & Management, Greater Noida, India
Mohammad Hanief Mechanical Engineering Department, National Institute of
Technology, Srinagar, India
Nitasha Hasteer Amity University, Noida, Uttar Pradesh, India
Anas Islam IET Department of Mechanical Engineering, GLA University,
Mathura, India
Shruti Jain Department of ECE, Jaypee University of Information Technology,
Solan, HP, India
Editors and Contributors xvii

Mayank Shekhar Jha Centre de Recherche en Automatique de Nancy, Université

de Lorraine, Nancy, France
Shivani Joshi G L Bajaj Institute of Technology and Management, Greater Noida,
India
Sahil Kalra Indian Institute of Technology Jammu, Jammu & Kashmir, India
Shivendra Kaura G. L. Bajaj Institute of Technology & Management, Greater
Noida, U.P., India
Sabah Khan Department of Mechanical Engineering, Jamia Millia Islamia, New
Delhi, India
Aditya Kumar G.L. Bajaj Institute of Technology and Management, Greater
Noida, India
Deepak Kumar Indian Institute of Technology Jammu, Jammu & Kashmir, India
Harshit Kumar Department of Mechanical Engineering, Dronacharya Group of
Institutions, Greater Noida, India
Ishu Kumar Department of Mechanical Engineering, GL Bajaj Institute of Tech-
nology and Management, Greater Noida, India
Kuldeep Kumar GL Bajaj Institute of Technology and Management, Greater
Noida, U.P., India
Manoj Kumar Greater Noida Institute of Technology, Noida, Uttar Pradesh, India
Nagendra Kumar G. L. Bajaj Institute of Technology & Management, Greater
Noida, UP, India
Pradeep Kumar G. L. Bajaj Institute of Technology & Management, Greater
Noida, UP, India
Rajiv Kumar G L Bajaj Institute of Technology and Management, Greater Noida,
India
Rishabh Kumar Department of Mechanical Engineering, G. L. Bajaj Institute of
Technology and Management, Greater Noida, UP, India
Singh Sonu Kumar Aryabhatta Center for Nanoscience and Nanotechnology,
Aryabhatta Knowledge University, Patna, Bihar, India
Rohit Singh Lather Department of Mechanical Engineering, The NorthCap
University, Gurugram, Haryana, India
Ajay Mahendru Department of Mechanical Engineering, Inderprastha Engi-
neering College, Ghaziabad, India
Pranav Malhotra Department of Mechanical Engineering, Amity University,
Noida, Uttar Pradesh, India
xviii Editors and Contributors

Aditya Mathur Department of Mechanical Engineering, G L Bajaj Institute of

Technology and Management, Greater Noida, India
Akanksha Mathur Department of Mechanical Engineering, The NorthCap Univer-
sity, Gurugram, Haryana, India
Bajrangi Maurya G.L. Bajaj Institute of Technology and Management, Greater
Noida, India
Kuwar Mausam IET Department of Mechanical Engineering, GLA University,
Mathura, India
Pankaj Kumar Mavi Department of Mechanical Engineering, G.L. Bajaj Institute
of Technology & Management, Greater Noida, India
Maninder Meenu Centre for Agricultural Research and Innovation, GNDU,
Amritsar, India
Rajat Mehrotra Department of Electrical & Electronics Engineering, GL Bajaj
Institute of Technology & Management, Greater Noida, India
Tushar Mehta Department of Mechanical Engineering, The NorthCap University,
Gurugram, Haryana, India
Puneet Kumar Mishra ECE Department, G.L. Bajaj Institute of Technology and
Management, Greater Noida, UP, India
Shivam Mishra G. L. Bajaj Institute of Technology & Management, Greater Noida,
UP, India
Shivi Mittal Department of Management Studies, G.L. Bajaj Institute of Tech-
nology & Management, A.P.J. Abdul Kalam Technical University, Greater Noida,
India
Khalid Nafees Department of Mechanical Engineering, Inderprastha Engineering
College, Ghaziabad, India
Amit Pal Department of Mechanical Engineering, Delhi Technological University,
Delhi, India
Nirbhay Singh Parmar Department of Mechanical Engineering, Indus Institute of
Technology and Management, Kanpur, UP, India
Sachin Patel G.L. Bajaj Institute of Technology and Management, Greater Noida,
India
Vansh Pokhriyal G.L. Bajaj Institute of Technology and Management, Greater
Noida, UP, India
Ponam Bhagwant University Ajmer, Rajasthan, India
Brijesh Prasad G. L. Bajaj Institute of Technology & Management, Greater Noida,
UP, India
Editors and Contributors xix

Om Priya Aryabhatta Center for Nanoscience and Nanotechnology, Aryabhatta

Knowledge University, Patna, Bihar, India
Amrita Rai ECE Department, G.L. Bajaj Institute of Technology and Management,
Greater Noida, UP, India
Pooja Rani G L Bajaj Institute of Technology and Management, Greater Noida,
UP, India
Ritwick Rawat G.L. Bajaj Institute of Technology and Management, Greater
Noida, India
Rajesh Rohilla Delhi Technological University, Delhi, India
Manish Saraswat Department of Mechanical Engineering, ABES Engineering
College, Ghaziabad, UP, India
Harendra Kumar Satyapal Aryabhatta Center for Nanoscience and Nanotech-
nology, Aryabhatta Knowledge University, Patna, Bihar, India
Alok Saxena G.L. Bajaj Institute of Technology and Management, Greater Noida,
UP, India
Pooja Saxena G. L. Bajaj Institute of Technology and Management, Greater Noida,
UP, India
S. S. Saxena Air Commodore VSM, Auto Wings Training and Consultancy Centre,
Greater Noida, India
Dhruv Sethi Department of Mechanical Engineering, GL Bajaj Institute of Tech-
nology and Management, Greater Noida, India
Shantanu G.L. Bajaj Institute of Technology and Management, Greater Noida,
India
Abhishek Sharma Department of Mechanical Engineering, GL Bajaj Institute of
Technology and Management, Greater Noida, UP, India
Aman Sharma IET Department of Mechanical Engineering, GLA University,
Mathura, India
Anil Kumar Sharma Department of Mathematics, SPC Degree College, Baghpat,
Uttar Pradesh, India
Ankit Sharma Department of Mechanical Engineering, GL Bajaj Institute of
Technology and Management, Greater Noida, India
Bhupendra Prakash Sharma Department of Mechanical Engineering, Amity
University, Noida, Uttar Pradesh, India
Devesh Sharma Department of Mechanical Engineering, G.L Bajaj Institute of
Technology and Management Greater Noida, Greater Noida, India
xx Editors and Contributors

Himanshu Sharma Department of Mechanical Engineering, G.L Bajaj Institute of

Technology and Management Greater Noida, Greater Noida, India
Kailash Sharma G. L. Bajaj Institute of Technology & Management, Greater
Noida, UP, India
Mayank Sharma GL Bajaj Institute of Technology and Management, Greater
Noida, U.P., India
Rahul Sharma Department of Mechanical Engineering, G.L Bajaj Institute of
Technology and Management Greater Noida, Greater Noida, India
Rahul Sharma Department of Mechanical Engineering, The NorthCap University,
Gurugram, Haryana, India
Sanjeev Sharma Department of Physics, GL Bajaj Institute of Technology and
Management, Greater Noida, Uttar Pradesh, India;
Department of Applied Sciences, GL Bajaj Institute of Technology and Management,
Greater Noida, Uttar Pradesh, India
Saurabh Sharma Aryabhatta Center for Nanoscience and Nanotechnology, Aryab-
hatta Knowledge University, Patna, Bihar, India
Vikas Kumar Sharma IET Department of Mechanical Engineering, GLA Univer-
sity, Mathura, India
Prashant Shukla Amity Institute for Advanced Research and Studies (M&D),
Amity University Uttar Pradesh, Sec 125, Noida, UP, India
Vidit Shukla Department of Electronics and Communication Engineering, GL
Bajaj Institute of Technology and Management, Greater Noida, UP, India
C. Shylaja ECE Department, G.L. Bajaj Institute of Technology and Management,
Greater Noida, UP, India
Rahul Sindhwani Department of Mechanical Engineering, Amity University,
Noida, Uttar Pradesh, India
Anil P. Singh Department of Mechanical Engineering, Inderprastha Engineering
College, Ghaziabad, India
Harkirat Singh Department of Mechanical Engineering, Amity University, Noida,
Uttar Pradesh, India
Harvendra Singh Department of Mechanical Engineering, G. L. Bajaj Institute of
Technology and Management, Greater Noida, UP, India
Jaydeep Singh Department of Mechanical Engineering, Dronacharya Group of
Institutions, Greater Noida, India
Karanjot Singh Department of Mechanical Engineering, Dronacharya Group of
Institutions, Greater Noida, India
Editors and Contributors xxi

Kaushalendra Kumar Singh Department of Mechanical Engineering, G. L. Bajaj

Institute of Technology and Management, Greater Noida, UP, India
Parshuram Singh Bhagwant University Ajmer, Rajasthan, India
Pradeep Kumar Singh IET Department of Mechanical Engineering, GLA Univer-
sity, Mathura, Uttar Pradesh, India
Prince Kumar Singh GL Bajaj Institute of Technology and Management, Greater
Noida, U.P., India
Priyanka Singh Department of Mechanical Engineering, Amity University, Noida,
Uttar Pradesh, India
Ranjeet Kumar Singh G.L. Bajaj Institute of Technology and Management,
Greater Noida, UP, India
Ravindra Pratap Singh IET Department of Mechanical Engineering, GLA
University, Mathura, India
Shailesh Kumar Singh Department of Mechanical Engineering, Dronacharya
Group of Institutions, Greater Noida, India
Vimal Singh G L Bajaj Institute of Technology and Management, Greater Noida,
India
Harsh Sohal Department of ECE, Jaypee University of Information Technology,
Solan, HP, India
Ashish Kumar Srivastava GL Bajaj Institute of Technology and Management,
Greater Noida, U.P., India
Prabhat Srivastava Shri Venkateshwara University, Gajraula, Uttar Pradesh, India
Priyank Srivastava Department of Mechanical Engineering, Amity University,
Noida, Uttar Pradesh, India
Vishal Shankar Srivastava Department of Mechanical Engineering, G. L. Bajaj
Institute of Technology & Management, Greater Noida, India
Mohd. Qamar Tanveer Department of Mechanical Engineering, Inderprastha
Engineering College, Ghaziabad, India
Divakar Tiwari G.L. Bajaj Institute of Technology and Management, Greater
Noida, UP, India
Rajesh Tiwari Department of Applied Science (Physics), ABES, Ghaziabad, Uttar
Pradesh, India
Vikas Tiwari G.L. Bajaj Institute of Technology and Management, Greater Noida,
UP, India
Mihir Tomar Department of Mechanical Engineering, G.L. Bajaj Institute of
Technology & Management, Greater Noida, India
xxii Editors and Contributors

Ashish Tripathi Department of Information Technology, G. L. Bajaj Institute of

Technology and Management, Greater Noida, Uttar Pradesh, India
Pragati Tripathi Department of Electrical Engineering, Gautam Buddha Univer-
sity, Greater Noida, India
Prem Chand Vashist Department of Information Technology, G. L. Bajaj Institute
of Technology and Management, Greater Noida, Uttar Pradesh, India;
CST, Lucknow, Uttar Pradesh, India
Mudita Vats Department of Electronics and Communication Engineering, GL
Bajaj Institute of Technology and Management, Greater Noida, UP, India
Aman Verma GL Bajaj Institute of Technology and Management, Greater Noida,
U.P., India
Gaayan Verma Amity University, Noida, Uttar Pradesh, India
Vimal Department of Physics, GL Bajaj Institute of Technology and Management,
Greater Noida, Uttar Pradesh, India
Himanshu Yadav GL Bajaj Institute of Technology and Management, Greater
Noida, U.P., India
Jatin Yadav Department of Mechanical Engineering, GL Bajaj Institute of Tech-
nology and Management, Greater Noida, India
Omprakash Yadav Department of Mechanical Engineering, G L Bajaj Institute of
Technology and Management, Greater Noida, India
Rajat Yadav IET Department of Mechanical Engineering, GLA University,
Mathura, India
Ramashankar Yadav G L Bajaj Institute of Technology and Management, Greater
Noida, India
Vinod Kumar Yadav Department of Mechanical Engineering, G. L. Bajaj Institute
of Technology and Management, Greater Noida, UP, India
Chapter 29
Investigating Strategies and Parameters
to Predict Maintenance of an Elevator
System

Jasmine Awatramani, Gaayan Verma, Nitasha Hasteer,

and Rahul Sindhwani

Abstract In this era of automation, our lives are surrounded by machines, be it

a mobile phone or an elevator. We humans become careless when it comes to the
maintenance of the machine. From the customer’s perspective, until an elevator is not
working, nobody tends to care. This carelessness, in the long run, can result in loss of
human life as well as financial losses. Elevators require maintenance and safety. To
overcome both, the machine requires timely maintenance, and it can be executed with
the precise product vision with the help of predictive maintenance. It not only predicts
future failure but also pinpoints the issues in complex machinery and gives better
results in terms of preventive maintenance. The conventional predictive maintenance
machine learning techniques are established on feature engineering. It is the manual
formation of precise features using domain proficiency and similar methodologies.
Due to this, models are hard to reuse because feature engineering is specific to
the problem structure and the data available, which can vary from one place to the
other. Deep learning methodologies provide better results due to the extraction of
new deep features from the dataset compared with the existing features. This work
reviews the extant literature as well as showcases the implementation of random
forest classifiers on the open-sourced dataset. In our model, an average accuracy of
91.50% was obtained. The dataset consisted of sensor data, which were recorded on
the basis of maintenance actions being taken.

29.1 Introduction

In recent years, elevators have become a part of commercial as well as residential

complexes. In addition to that, it has emerged as an essential tool in day-to-day life
with the growth of the economy and urbanization holding up to 54% of the world’s
population [1, 2]. Elevator systems tend to lay ease in the lives of people [3, 4].
Recently, the pandemic has impacted maintenance even more severely. It has put
a halt to the installations of new equipment as well as has increased pressure to

J. Awatramani (B) · G. Verma · N. Hasteer · R. Sindhwani

Amity University, Noida, Uttar Pradesh, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 323
V. V. Rao et al. (eds.), Computational and Experimental Methods in Mechanical
Engineering, Smart Innovation, Systems and Technologies 239,
https://doi.org/10.1007/978-981-16-2857-3_30
324 J. Awatramani et al.

decrease the number of yearly visits. The safety issues of elevators have transformed
into a global elevator issue. Therefore, elevator systems require proper maintenance
to ensure safety and reliability. Although, there are various obstacles faced during
the actual maintenance of elevator systems such as inappropriate maintenance tech-
niques, inappropriate upgrading of elevator instruments, high maintenance costs, and
much more. Developing preventive and predictive maintenance arrangements will
be the next phase for revamping the security of elevators, which will further escalate
the life of elevator systems and decrease the cost of service. Preventive mainte-
nance performs systematic inspections of assets and executes routine maintenance
to prevent unexpected equipment downtime or failure. Preventive maintenance can
decrease the frequency of equipment failure. Though this maintenance procedure
needs a definite cost, there is a possibility that the cost of the whole elevator system
might increase if over-maintenance of equipment is being done. If preventive main-
tenance activities are not useful enough, the rate of equipment failure will be a high
rise, and post-maintenance costs of the whole system might also increase.
Predictive maintenance lets you estimate the time-to-failure of a machine. So we
can start preventive maintenance and save time and assets from any big issue. Predic-
tive maintenance strategies are nowadays being implemented by elevator production
service companies. This implementation comes into use by evaluating the remaining
life of the elements that are accountable for faults and remotely tracking faults in
elevator systems. Fault detection and diagnosis are needed by elevators to perform a
healthy operation.
The traditional predictive maintenance machine learning models are based on
feature engineering, which is the manual construction of the right features using
domain expertise and similar methods. This usually makes these models hard to
reuse since feature engineering is specific to the problem scenario and the available
data, which vary from one place to the other.
The most attractive part of applying deep learning in the predictive maintenance
domain is the fact that these networks can automatically extract the right features
from the data, eliminating the need for manual feature engineering. Deep learning
approach provides better results due to the new deep features extracted from the
dataset compared with the existing features.
This paper explores the elevator system’s optimal maintenance policy to reduce
the average maintenance cost. The rest of the work has been structured as follows:
Sect. 29.2 provides the in-depth literature review, Sect. 29.3 showcases gap analysis,
Sect. 29.4 represents the parameter analysis, Sect. 29.5 represents the implementation
of random forest classifier in our model, Sect. 29.6 represents the experimental results
on the same and Sect. 29.7 showcases the conclusion along with the future scope.
29 Investigating Strategies and Parameters to Predict … 325

29.2 Literature Review

Several search engines were used for online library search: IEEE Xplore Digital
Library, Springer Link Online Library, Elsevier ScienceDirect and Google Scholar.
Most of the authors have implemented deep learning strategies in their models in
order to obtain results [1, 2, 5–7]. An effective fault diagnosis and fault prognosis
has been observed. Along with which, implementation of decision trees and random
forest algorithm has also been observed in order to compare the results. The use of
deep learning has helped the authors to extract new deep features from the dataset as
well. These new deep features showcased better accuracy in terms of fault diagnosis.
Mishra et al. [1, 2] designed a generic deep encoder model to automatically calcu-
late highly explanatory deep features from the elevator data. Random forest algorithm
was applied to determine faults based on statistical attributes to compare results.
Strategy with new extracted deep attributes provided great accuracy in determining
the faults. In this paper [5], the authors have proposed a conventional framework
of intelligent predictive maintenance systems for elevator service consisting of fault
prognosis, fault diagnosis, feature extraction, pre-processing signal, IoT and Internet
of Service. Gilabert et al. [6] studied predictive maintenance as a result of uncrit-
ical machinery. Continuous monitoring along with diagnosis has been done using
neural networks, whereas, machine instruments were being observed using vibration
systems and applied using Bayesian networks. It also showed the work carried out
under the MINICON project that tends to develop cost-effective integrated SPUs.
In this paper [7], a consolidated neural-network based on decision support structure
for predictive maintenance of rotating equipment. It comprises a vibration-based
deterioration database by observing rolling element bearings, developing an ANN
model to estimate the life percentile, along with the failure times of roller bearings
and cost matrix designing, and probabilistic substitution model that tends to enhance
the expected cost per time.
Few papers have showcased the implementation of machine learning algorithms
including SVM, genetic algorithms [3, 8, 9]. They focused mainly on reducing the
mean cost of the maintenance of an elevator and finding out the most optimal strategy
to estimate the time of maintenance. These papers consisted of static datasets only
and detection of faults at an early stage.
In this paper [3], the model is combined with a numerical example that is solved
by a genetic algorithm with the goal of reducing the mean cost of maintenance. The
best equipment maintenance plan of action is found by verifying the practicality and
validity of the model. In Langone et al. [8], LS-SVM at an early stage of fault detection
was used. Initially, on sensor data coming from the VFFS machine, an unsupervised
approach, KSC came into play. After that, the NAR model was illustrated, i.e., a
supervised learning methodology, LS-SVM framework. It showed that LS-SVM
can successfully assess, predict mechanical conditions based upon sensor data and
attain higher performance than basic methods. In this paper [9], a discussion on
Multivariate Relevance Vector Machines that examines direct and indirect procedures
326 J. Awatramani et al.

for forecasting everyday evapotranspiration was being done. The developed models
are based on static datasets.
Few miscellaneous studies were mainly based on the research on the mechanical
strategies, which included examination of hydraulic oil, MCSA and algorithms and
penalty functions to make a degradation and maintenance model [10–13]. These
papers were research-based and showcased the impact of predictive maintenance on
elevator systems.
Xu et al. [10] drew an outline about the role of predictive maintenance in the
epoch of big data and techniques of data-directed fault diagnostics and fault prog-
nostics along with its future extent. In this paper [11], a dynamic predictive mainte-
nance policy for a multi-component approach. It also provides an outline of literature
on predictive and condition-based/vanilla maintenance strategies. Flores et al. [12]
designed Motor Current Signature Analysis (MCSA) is an identification method-
ology in which the mechanical structure acts as a transducer, determining short
torque variations produced within the mechanical structure. MCSA has been used
for determining the condition of various mechanical components such as bearings,
motor fan, rotor unbalance and gearboxes, providing information in regards with fault
centralization. In this paper [13], various methods have been performed to inspect
the percentage of metals and corrosion; sulfur levels in the hydraulic fluid depict the
maintenance intervals of an elevator system. The study of zinc, phosphorus, chlorine
and calcium verifies that the oil that was used in one elevator is different from the oil
used in the other two. Additionally, it releases significant wear to all the elevators as
a result of the working environment conditions. This study of the hydraulic fluid can
be included in the testing process, which inspects the safety of elevators (Fig. 29.1).

Fig. 29.1 Timeline evolution of techniques to predict the health of elevator systems
29 Investigating Strategies and Parameters to Predict … 327

29.3 Gap Analysis

In [3], the mathematical explanation was presented, which reduced the mean main-
tenance cost, but the discussion about the relationship between the two equipment
was not discussed. Although the analytical model used in [5] was pretty advanced,
still, there is a need for an efficient data-mining approach and analysis model. In this
study, the main focus of this work was on diagnostics and prognostics techniques,
managing the tasks that are scheduled and with the restricted data, predicting the
machine RUL. But, challenges faced by the authors are to improve the efficacy of
the model [10]. In [11], to improve the efficacy of maintenance. The work focused
more on the component dependencies rather than system dependencies. To look for
various techniques in order to model stochastic dependence. The work in [7] show-
cased the optimization of maintenance of task scheduling. Challenges were faced
while recording the readings of bearing when replaced in group. Cost matrix, along
with margin life distribution, should also be expanded.

29.4 Parameter Analysis

Elevators work on the principle of the see-saw. The central module of an elevator
is the elevator car, in which the commuter or load is carried. The elevator car has a
supporting assembly attached to it, that assembly is attached to a traction rope. The
vertical motion of the elevator is guided by a pair of rails. The elevator is raised and
lowered with the help of traction steel rope and counterweight.
The rope is looped around a pulley, which is known as a driving pulley [14]. The
driving pulley is attached to an electric machine. They together form a system known
as a traction system. Traction system runs by the elevator’s control system present
in the elevator, which allows its motion.
There are many mechanical components present in an elevator system, these
components are in direct relation with the performance and fidelity of the elevator.
The traction system is the driving force of an elevator. The pulley present in the
traction system consists of ball bearing and this wears off due to fretting friction
caused between the pulley and the steel ropes [15]. Counter-weight balances the
elevator car, which puts constant stress on the traction cable, the stress increases
at the time of vertical movement of the elevator system. The motor present in the
traction system produces short torque vibration, which helps determine the longevity
of the elevator system. Distance traveled by the elevator is in direct relation with the
elevator as the travels more distance the components wear off. Other components
such as motor fans, gearboxes, humidity also affect the health of the elevator systems
[12].
Some parameters have more impact on the health of the elevator than others.
Ball-bearing present in the driving pulley is responsible for friction-free movement
of the elevator system. This component of the traction system wears off as there is
328 J. Awatramani et al.

constant stress present from the traction steel ropes. Fitting friction among the ropes
causes curtailment of the cross-sectional area of the metal, which naturally leads to
resistance on the pulley. As a result, the bearing wears off. Distance traveled by an
elevator is a clear indicator as to how much an elevator is used. As a consequence, it
helps determine the health, wear and condition of the elevator across a timeline [16].
Torque vibration refers to the vibration produced by the mechanical motor present
in the traction system, this attribute determines the health of the motor.

29.5 Methodology Used

29.5.1 Dataset and Attributes

The dataset used [4] has been recorded from a variety of IoT sensors used in the
elevator industry. It contains the evening usage of an elevator between 16:30 and
23:30. The dataset consists of mainly three attributes: ‘Ball-Bearing’, ‘Vibration’
and ‘Humidity’. Dataset provides operational data in the form of time series. It
showcases the functional life of an elevator system, which is observed to be roughly
27 years. On analysis, it has been observed in Fig. 29.2 that there is a constant
decline in revolutions (rpm). A perfectly functional ball bearing has 93 rpm, and it
deteriorates as low as 13 rpm in a span of 27 years. The average life of an elevator is
20–25 years. Hence, by observing the graph, it is determined that at a value between
35 and 45 rpm, the elevator is most likely to cause a failure.
In Fig. 29.3, it is observed from the available statistics that in the initial 5–6 years
of the elevator system, minor changes occur. The elevator system starts experiencing
fluctuation in vibration after 7–10 years of use through the occurrence is less. The
major increase in the occurrence of vibration is observed after 15–20 years of use.
As traction motors are the source of short torque vibration, the health of the motor
can be determined by the intensity of the vibrations.

Fig. 29.2 Analysis of revolutions of ball-bearing parameter of an elevator system

29 Investigating Strategies and Parameters to Predict … 329

Fig. 29.3 Analysis of vibration parameter of an elevator system

29.5.2 Random Forest

Classification is one of the biggest parts of machine learning and random forest
classifier holds the top of the classifier hierarchy [1, 2]. It is basically a bagging
technique in which there is a collection of a large number of decision trees. Decision
trees have two important properties of low bias and high variance. Random forest
classifier creates an array of decision trees that are created from randomly generated
subsets of training data. Then, votes from the different decision trees are combined
to determine the final category of the test object. Random forests can also apply the
weight concept, which takes into account the magnitude of influence of decision tree
outcome. Trees with low error values have high weight values and vice versa. This
increases the impact of trees that have low error rates. The following parameters have
been used in the study:
• Gini Impurity: calculates the purity of the split and it is better than entropy, as it
is computationally efficient. The range of gini impurity lies between 0 and 1.
• N_estimators: 20 (total number of trees)
• max_depth: 3 (depth of an individual tree in forest)
• min_samples_split: 2 (minimum samples required to split branch of the tree)
• min_sampels_leaf: 1 (minimum samples required to be at leaf node)
Random forest classifier assigns a random value to the root node sample and
classification condition which in our case is “samples = 44408” and “ball-bearing <=
40.045”. According to the classification condition, the range of samples is classified
into three distinct states: Good (0), Fair (1) and Poor (2). This process continues until
all the samples are classified successfully.
In Fig. 29.4, a random forest classifier selects a randomly selected condition value,
which is close to the midpoint which in our observation is “ball-bearing <= 40.045”.
So, all the sample points with ball bearing less or equal to 40.045 are categorized as
class 2. In the second level of the tree, the leaf node signifies the number of samples
that are categorized as class 2. The non-leaf node on the second level of the tree
represents the remaining samples, which are further divided into two subtrees based
330 J. Awatramani et al.

Fig. 29.4 Random forest tree visualization

on the randomly selected condition value selected by the random forest classifier
“ball-bearing <= 60.661”. This process is repeated until all the samples are classified.

29.6 Experimental Results

An average accuracy of about 91.50% (ranging 91% ± 2%) was achieved using a
random forest classifier (Fig. 29.5).

Fig. 29.5 Feature

preference score
29 Investigating Strategies and Parameters to Predict … 331

After the implementation of the random forest classifier, it was observed that the
ball-bearing feature came out to be of more significance than the vibration feature.
This was due to the frequent fluctuations seen in the readings of vibration in our
dataset.

29.7 Conclusion

In this study, we investigated strategies and parameters to predict the maintenance

of an elevator system and showcased the importance of predictive maintenance, and
how it can contribute to better preventive maintenance strategies. With the help of
predictive maintenance, it can prevent future accidents, save lives and can be cost-
effective. Data analysis being the most crucial part of predictive maintenance is
complex too. But, the implementation of deep learning algorithms has contributed
in the extraction of new deep features. Thus, this has resulted in better efficacy and
analysis of the model used.
In the future, the usage of a predictive maintenance strategy on an elevator
system will surely result in optimized maintenance for all elevator components, thus
prolonging elevator’s overall lifetime. Future issues will be predicted even before
the actual customer sees it. The implementation of improvised models along with
deep learning in this process will contribute to the generation of new and useful deep
features, which will result in more precise results, as well as, proper management of
schedules.

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Author Index

A G
Amardeep, 9, 367, 469 Gandhi, Sonal, 397
Ameer, Aatif, 509 Govil, Vinamra Kumar, 227
Ansari, M. A., 259 Gupta, Anuj, 429
Ansari, N. A., 519 Gupta, Deepa, 207
Awatramani, Jasmine, 323 Gupta, Shorya, 299
Gupta, Shradha, 197
Gupta, Tarun Kumar, 77
B Gurjar, Siddharth Singh, 445
Baghel, Navneet Singh, 19
Bahri, Harshit, 115, 177
Bansal, Ashish, 19 H
Bansal, Chetan, 411 Hanief, Mohammad, 241
Bansal, Mohit, 275 Hasteer, Nitasha, 323
Bhati, Aditya Kumar, 445
Bhatia, Siddhant, 469
I
Islam, Anas, 357, 481
C
Chaturvedi, Rishabh, 357, 387, 419, 459
Chauhan, Pankaj Kumar, 43
J
Chauhan, Sandeep, 19, 77, 411
Jain, Shruti, 489
Chauhan, Shailendra Singh, 445
Jha, Mayank Shekhar, 131
Chauhan, Vinit, 33
Joshi, Shivani, 397
Chauhan, Yash, 445
Chhonkar, Ankit, 19
Choudhary, Shilpa, 377
K
Kalra, Sahil, 1, 131
D Kaura, Shivendra, 275
Datta, Priyanka, 275, 287 Khan, Sabah, 43
Dohare, Deepti, 333 Kirti, 489
Kumar, Aditya, 9
Kumar, Deepak, 1, 131
F Kumar, Harshit, 367
Farozan, Shama, 105 Kumar, Ishu, 469
© The Editor(s) (if applicable) and The Author(s), under exclusive license 539
to Springer Nature Singapore Pte Ltd. 2022
V. V. Rao et al. (eds.), Computational and Experimental Methods in Mechanical
Engineering, Smart Innovation, Systems and Technologies 239,
https://doi.org/10.1007/978-981-16-2857-3
540 Author Index

Kumar, Kuldeep, 93 Saxena, Pooja, 165

Kumar, Manoj, 387, 459 Saxena, S. S., 445
Kumar, Nagendra, 227, 275 Sethi, Dhruv, 469
Kumar, Pradeep, 55 Shantanu, 9
Kumar, Rajiv, 397 Sharma, Abhishek, 377
Kumar, Rishabh, 429 Sharma, Aman, 215, 357, 419
Kumar, Singh Sonu, 105 Sharma, Anil Kumar, 189
Sharma, Ankit, 469
Sharma, Bhupendra Prakash, 299
L Sharma, Devesh, 19
Lather, Rohit Singh, 33 Sharma, Himanshu, 19
Sharma, Kailash, 227
Sharma, Mayank, 93
M Sharma, Rahul, 19, 33, 519
Mahendru, Ajay, 349 Sharma, Sanjeev, 189, 197
Malhotra, Pranav, 299 Sharma, Saurabh, 105
Mathur, Aditya, 411 Sharma, Vikas Kumar, 215, 387, 481
Mathur, Akanksha, 33 Shukla, Prashant, 165
Maurya, Bajrangi, 143 Shukla, Vidit, 377
Mausam, Kuwar, 403 Shylaja, C., 313
Mavi, Pankaj Kumar, 445 Sindhwani, Rahul, 299, 323
Meenu, Maninder, 1 Singh, Anil P., 349
Mehrotra, Rajat, 227, 259 Singh, Harkirat, 299
Mehta, Tushar, 33 Singh, Harvendra, 115, 177
Mishra, Puneet Kumar, 313 Singh, Jaydeep, 367
Mishra, Shivam, 55 Singh, Karanjot, 367
Mittal, Shivi, 207 Singh, Kaushalendra Kumar, 115, 177
Singh, Parshuram, 67
Singh, Pradeep Kumar, 155, 419, 459
N Singh, Prince Kumar, 93
Nafees, Khalid, 349 Singh, Priyanka, 299
Singh, Ranjeet Kumar, 509
Singh, Ravindra Pratap, 403
P Singh, Shailesh Kumar, 367
Pal, Amit, 519 Singh, Vimal, 397
Parmar, Nirbhay Singh, 429 Sohal, Harsh, 489
Patel, Sachin, 9 Srivastava, Ashish Kumar, 93
Pokhriyal, Vansh, 509 Srivastava, Prabhat, 207
Ponam, 67 Srivastava, Priyank, 299
Prasad, Brijesh, 227 Srivastava, Vishal Shankar, 77, 93
Priya, Om, 105

T
R Tanveer, Mohd. Qamar, 349
Rai, Amrita, 313 Tiwari, Divakar, 509
Rani, Pooja, 1, 245 Tiwari, Rajesh, 189
Rawat, Ritwick, 9 Tiwari, Vikas, 509
Rohilla, Rajesh, 287 Tomar, Mihir, 445
Tripathi, Ashish, 499
Tripathi, Pragati, 259
S
Saraswat, Manish, 155, 481
Satyapal, Harendra Kumar, 105 V
Saxena, Alok, 509 Vashist, Prem Chand, 499
Author Index 541

Vats, Mudita, 377 Y

Yadav, Himanshu, 93
Verma, Aman, 93 Yadav, Jatin, 469
Yadav, Omprakash, 19, 411
Verma, Gaayan, 323 Yadav, Rajat, 155, 215, 403
Yadav, Ramashankar, 397
Vimal, 189 Yadav, Vinod Kumar, 429