Dr. V. Muralidharan

Designation	:	Associate Professor & Deputy Dean (Research)
Nature of Employment	:	Regular
Date of Joining	:	12-03-2014
Qualification	:	B.E,.M.Tech.,Ph.D.
Phone	:	+91 44 2275 1347, Extn. 277 (Office)
Email ID	:	muralidharan@crescent.education

Teaching

Educational Background

Degree	Dicipline	Year of Passing	University
Ph.D.	Mechanical Engineering	2012	Karpagam Academy of Higher Education, Coimbatore
M.Tech.	Computational Engineering and Networking	2007	Amrita Vishwa Vidyapeetham, Coimbatore
B. E	Mechanical Engineering	2003	Madras University, Chennai.

Work Experience

Designation	Institution	Duration	No. of Years
Associate Professor	B.S.Abdur Rahman Crescent Institute of Science & Technology	12.03.2014 – Till date	8 yrs 10 months
Associate Professor	G.K.M College of Engineering & Technology	02.05.2013 – 07.03.2014	10 Months
Assistant Professor	SRM University	03.12.2009 – 30.04.2013	3 yrs 5 months
Assistant System Engineer	Tata Consultancy Services	24.09.2007 – 26.11.2009	2 yrs 2 months

Lecture Courses

• Engineering Graphics
• Basic Engineering Mechanics
• Kinematics of Machinery
• Dynamics of Machinery
• Mechanical Vibrations
• Design of Transmission Systems

Research

Area of Research Interest

• Fault diagnosis of Mechanical Systems
• Vibration based Condition monitoring
• Wavelets for condition monitoring applications

Publications International Journals : 33 National Journals : 01 International Conference : 11 International Journal Publications

• D. Pradeep Kumar, V. Muralidharan, S Ravikumar, 2022, Histogram as features for fault detection of multi point cutting tool–A data driven approach, Applied Acoustics, 186, 108456. https://www.sciencedirect.com/science/article/abs/pii/S0003682X21005508
• Syedshaulhameed, V. Muralidharan, 2022, Feature extraction using Discrete Wavelet Transform for fault classification of planetary gearbox–A comparative study, Applied Acoustics, 188, 108572. https://www.sciencedirect.com/science/article/abs/pii/S0003682X21006666
• Syedshaulhameed, D. Pradeep Kumar, V. Muralidharan, 2022Multi-Point Tool Condition Monitoring System – A Comparative Study, FME Transactions, 50 (1), pp. 193-201. https://www.mas.bg.ac.rs/_media/istrazivanje/fme/vol50/1/20_v._muralidharan_et_al.pdf
• Syedshaulhameed, V. Muralidharan, BK Ane, 2021,Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox,Applied Soft Computing, 106, 107306 https://www.sciencedirect.com/science/article/abs/pii/S1568494621002295
• Syedshaulhameed, V. Muralidharan, D. Pradeep Kumar, Fault Classification using Fuzzy Logic in an Epicyclic Gearbox with Statistical Features, SAE Technical Paper, 2021-28-0220, 2021 https://www.sae.org/publications/technical-papers/content/2021-28-0220
• C Sivakumar, V. Muralidharan, R Natarajan, D. Murali Manohar, 2021,Numerical Analysis on Mechanical Behavior of Viscoelastic Nano Composite,SAE Technical Paper, 2021-28-0240 https://www.sae.org/publications/technical-papers/content/2021-28-0240/
• K Radhakrishnan, V. Muralidharan, A Study on the Welding and Heat Treatment of 0.3% C-Cr-Mo-v Steel, Lecture Notes in Mechanical Engineering, Advances in Design and Thermal Systems, 475-492, 2021 https://link.springer.com/chapter/10.1007/978-981-33-6428-8_39
• K.Radhakrishnan, V. Muralidharan, 2021, The Effect of Quenching Mediums on Heat Treatment Properties of Multi-pass Welding of 0.3% C-Cr Mo V Steel, Lecture Notes in Mechanical Engineering, Advances in Design and Thermal Systems, 463-474 https://link.springer.com/chapter/10.1007/978-981-33-6428-8_38
• Syedshaulhameed, V.Muralidharan, 2021, Fault Detection in Single-Stage Helical Planetary Gearbox Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) with Statistical Features, Lecture Notes in Mechanical EngineeringAdvances in Design and Thermal Systems, 119-132, 2021 https://link.springer.com/chapter/10.1007/978-981-33-6428-8_8
• S. Ravikumar, V. Muralidharan, P. Ramesh, C. Pandian, 2021,Fault diagnosis of self-aligning conveyor idler in coal handling belt conveyor system by statistical features using random forest algorithm, Lecture Notes in Mechanical Engineering, Advances in Smart Grid Technology, 207-219 https://link.springer.com/chapter/10.1007/978-981-15-7241-8_16
• S Ravikumar, H Kanagasabapathy, V. Muralidharan, 2020, Multi-component fault diagnosis of Self Aligning Troughing Roller (SATR) in belt conveyor system using decision tree: A statistical approach, FME Transactions, Vol.48, No. 2, pp. 364-371 https://www.mas.bg.ac.rs/_media/istrazivanje/fme/vol48/2/14_v._muralidharan_et_al..pdf
• S Ravikumar, H Kanagasabapathy, V. Muralidharan, 2019, Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm, Measurement, Vol. 133, pp. 341-349 https://www.sciencedirect.com/science/article/abs/pii/S0263224118309175
• Pradeep Kumar Durairaj, V. Muralidharan, 2019, Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal, SAE Technical Paper, 2019-28-0142 https://www.sae.org/publications/technical-papers/content/2019-28-0142/
• Syed ShaulHameed, V. Muralidharan, Mahendran K, 2019, Fault Detection in Single Stage Helical Planetary Gearbox Using Artificial Neural Networks (ANN) and Decision Tree with Histogram Features, SAE Technical Paper, 2019-28-0151 https://www.sae.org/publications/technical-papers/content/2019-28-0151/
• S Ravikumar, V. Muralidharan, H.Kanagasabapathy, 2016, condition monitoring of induced draft fan (IDF) in coal based thermal power generation system using statistical features and decision tree algorithm, International Journal of Scientific Research and Innovations, Vol.1. pp.37-45
• V Muralidharan, V. Sugumaran, 2016, SVM-based wavelet selection for fault diagnosis of monoblock centrifugal pump, International Journal of Data Analysis Techniques and Strategies, vol.8,No.4, pp.357-369 https://www.inderscience.com/info/inarticle.php?artid=81364
• V. Muralidharan, S. Ravikumar, H. Kangasabapathy, 2014, Condition monitoring of Self aligning carrying idler (SAI) in belt-conveyor system using statistical features and decision tree algorithm, Measurement, vol.58, pp. 274-279 sciencedirect.com/science/article/abs/pii/S0263224114003765
• V. Muralidharan, V. Sugumaran, V Indira, 2014, Fault diagnosis of monoblock centrifugal pump using SVM, Engineering Science and Technology, an International Journal, vol.17, No.3, pp.152-157 https://www.sciencedirect.com/science/article/pii/S2215098614000275
• V Muralidharan, V Sugumaran, NR Sakthivel, 2014, Fault diagnosis of monoblock centrifugal pump using stationary wavelet features and Bayes algorithm, Asian Journal of Science and Applied Technology, Vol.3. No.2.pp. 1-4 https://www.trp.org.in/wp-content/uploads/2014/07/AJSAT-Vol.3-No.2-July-Dec-2014pp.1-4.pdf
• V Muralidharan, V Sugumaran, 2013, Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump, Measurement, Vol.46, No.9, pp.3057-3063 https://www.sciencedirect.com/science/article/pii/S0263224113002352
• V Muralidharan, V Sugumaran, 2013, Fault diagnosis of centrifugal pump using wavelet features–fuzzy-based approach, International Journal of Computational Systems Engineering, Vol.1. No. 3, pp. 175-183 https://www.inderscience.com/info/inarticle.php?artid=52587
• V Muralidharan, V Sugumaran, 2013,Selection of discrete wavelets for fault diagnosis of monoblock centrifugal pump using the J48 algorithm, Applied Artificial Intelligence,Vol.27, No.1, pp.1-19 https://www.tandfonline.com/doi/full/10.1080/08839514.2012.721694
  23. V Muralidharan, V Sugumaran, 2013, Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump, Measurement, Vol.46, No.1, pp.353-359 https://www.sciencedirect.com/science/article/abs/pii/S026322411200276X
  24. V Muralidharan, V Sugumaran,2012, A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis, Applied Soft Computing, Vol.12, No.8, pp. 2023-2029 https://www.sciencedirect.com/science/article/abs/pii/S1568494612001135
  25. V. Sugumaran V. Muralidharan, 2012, Effect of Cascaded Classifiers and Wavelet Features for Fault Diagnosis of Monoblock Centrifugal Pump, International journal of decision making in supply chain and logistics
  26. V. Sugumaran V. Muralidharan, Hemantha Kumar,2012, Fault Diagnosis of Monoblock Centrifugal Pump Using Discrete Wavelet Features and J48 Algorithm, International Journal of Mechanical Engineering and Technology, Vol.3, No.1, pp.120-126 https://iaeme.com/Home/article_id/IJMET_03_01_012
  27. V Muralidharan, V Sugumaran, Gaurav Pandey, 2011, SVM based fault diagnosis of monoblock centrifugal pump using stationary wavelet features, International Journal of Design and Manufacturing Technology (IJDMT), Vo.2,No.1, pp.1-6. https://iaeme.com/Home/article_id/IJDMT_02_01_001
  28. Gaurav Pandey, V Muralidharan, V Sugumaran, 2011, Fault Diagnosis Of Monoblock Centrifugal Pump Using Stationary Wavelet Features And J48 Algorithm, International Journal of Production Technology and Management, Vol.1, No.1,pp.65-70 https://iaeme.com/Home/article_id/IJPTM_01_01_006
  29. V Muralidharan, V Sugumaran, NR Sakthivel, 2011, Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump, International Journal of Data Analysis Techniques and Strategies, Vol.3, No.2, pp.159-177 https://www.inderscienceonline.com/doi/abs/10.1504/IJDATS.2011.039849?journalCode=ijdats
  30. V Muralidharan, V Sugumaran, Bharathkumar Hegde, 2010, Fault Diagnosis of Centrifugal Pump Using Haar Wavelet Transform,Int. J of Applied Engineering Research, Vol.5, No.13, pp.2307-2316.
  31. V Muralidharan, V Sugumaran, P. Shanmugam, K. Sivanathan, 2010, Artificial Neural Network based Classification for Monoblock Centrifugal Pump using Wavelet Analysis, International Journal of Mechanical engineering, Vol.1, pp.28-37 https://www.ripublication.com/Volume/ijaerv5n13.htm
  32. Ravi Teja C, V Sugumaran, V Muralidharan, Bharath Kumar Hegde, 2010, Intelligent process selection for NTM—a neural network approach, Int J IndEng Res Dev, Vol.1, pp.87-96 https://iaeme.com/Home/article_id/IJIERD_01_01_006
  33. V Sugumaran, V Muralidharan, KI Ramachandran, 2007, Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing, Mechanical systems and signal processing, Vol.21, No.2, pp.930-942 https://www.sciencedirect.com/science/article/abs/pii/S0888327006001142

National Journal

• V Muralidharan, V Sugumaran, 2016, A comparative study between Support Vector Machine (SVM) and Extreme Learning Machine (ELM) for fault detection in pumps, Indian Journal of Science and Technology, Vol.9, No. 48, pp. 0974-564 https://indjst.org/articles/a-comparative-study-between-support-vector-machine-svm-and-extreme-learning-machine-elm-for-fault-detection-in-pumps

International Conference Publications

• SH Syed, V Muralidharan, P Kumar, 2022, Effect of k-Nearest Neighbours (k-NN) in Classifying Planetary Gearbox Faults Using Statistical Features, ADMMS 2022, SAE Technical Paper.
• Pradeep Kumar, V. Muralidharan, Syed Shaul Hameed, 2022, Fault Classification of Face Milling Tool Using Vibration Signals and Histogram Features–A Machine Learning Approach, ADMMS 2022, SAE Technical Paper
• Ravikumar Santhanam, Syed Shaul Hameed, V Muralidharan, Pradeep Kumar Real-Time Condition Monitoring of Multi-Component High Torque Helical Gearbox in Coal Handling Belt Conveyor System Using Machine Learning–A Statistical Approach,ADMMS 2022, SAE Technical Paper
• V Gunasegaran, V Muralidharan, 2020,Fault Diagnosis of Spur Gear System through Decision Tree Algorithm Using Vibration Signal, Materials Today: Proceedings, Vol.22, pp. 3232-3239
• S Ravikumar, S Kanagasabapathy, V Muralidharan, R Srijith, M Bimalkumar, 2018, Fault diagnosis of self-aligning troughing rollers in a belt conveyor system using an artificial neural network and naive bayes algorithm, Emerging Trends EngSciTechnolSoc Energy Environ Proceed IntConfEmerg Trends EngSciTechnol ICETEST, Vol. 2018, pp.401-408
• S. Giftlin S. Ravikumar, H.Kanagasabapathy, V. Muralidharan, 2016, Vibration based fault detection and condition monitoring using Decision Tree Algorithm for Training carrying Idlers (TCI) in Belt-Conveyor System, ICMED – 2016
• S. Giftlin S. Ravikumar, H.Kanagasabapathy, V. Muralidharan, 2016, Application of Decision Tree algorithm for the classification and identification of faults in self aligning Training idler in Bulk material handling belt conveyor systems, International Conference on Futuristic Engineering, Science and Technology,  pp.37-45
• K.Radhakrishnan, V.Muralidharan, 2016, Optimization of Weld Parameters and Weld Bead Dimensions in welding of 0.3%C-Cr-Mo-V Steel by Gas Tungsten Arc Welding process, ICMED – 2016
• H.Kanagasabapathy  S. Ravikumar, V. Muralidharan, 2014, Condition Monitoring of Self Aligning Carrying Self aligning carrying idler (SAI) in Belt-Conveyor System using Statistical Features and support vector machine, ICCIAMR-2014
• V. Sugumaran S. Siddarth, V. Muralidharan, 2011, Condition Assessment of Robots in Flexible Assembly System, COSMA 2011.
• V. Sugumaran V. Muralidharan, 2011, Selection of Wavelets for Fault Diagnosis of Monoblock Centrifugal pump using decision tree Algorithm, COSMA 2011

Ph.D. Guidance

S.No.	Name of the Scholar	Topic of Research	Status
1.	Mr. S. Karthikeyan	Experimental Investigations of Microstructure, Mechanical Properties and Electrochemical Behavior of A390 alloy based Metal Matrix Composites Material	Completed (2022)
2.	Mr. D. Pradeep Kumar	Fault Diagnosis Of Multi Point Cutter Using Vibration Signals And Machine Learning Algorithms	Completed (2023)
3.	Mr. Syed Shaul Hameed	A Machine Learning Approach To Fault Diagnosis Of Planetary Gearbox	Completed (2023)
4.	Mr. C. Sivakumar	Numerical Analysis on Mechanical Behavior of Viscoelastic Nano Composite	Comprehensive Exam completed
5.	Mr. K. Radhakrishnan	A Study on the Welding and Heat Treatment of 0.3% C-Cr-Mo-v Steel	Comprehensive Exam completed

International Recognition:

• Awarded as an Outstanding reviewer for the Journal ISA Transactions, Elsevier, in the year 2017 (Impact factor: 5.911)

Reviewer for the Following Refreed International Journals

• ISA Transactions, Elsevier
• Measurement – Journal of the International Measurement Confederation, Elsevier
• Artificial Intelligence Review, Springer Publications
• International Journal of Engineering, Science and Technology, Elsevier.
• Computers and Mathematics with Applications, Elsevier.
• Neural Computing and Applications, Springer Publications.
• International Journal of Computer Aided Engineering and Technology, Inderscience.

Books Edited / Authored

• Edited a conference proceedings (ETDMMT 2020) volume titled “Advances in Design and Thermal Systems”, Springer Publications, 2021 (ISBN :978-981-336-427-1)
• Co-authored a book on “Instrumentation and Control Systems” University Science Press, An Imprint of Laxmi Publications Pvt. Ltd, Ed.1, 2015, (ISBN: 978-93-83828-50-0)

Resource Person

• Delivered a guest on “Machine Learning Applications in Mechanical Engineering” in AICTE sponsored (ATAL) FDP conducted at Acropolis Institute of Technology & Research, Indore, on Dec 17, 2021
• Delivered a guest lecture on “Wavelets and its Applications – An Introduction” in AICTE sponsored (ATAL) FDP conducted at K.Ramakrishna College of Engineering, Trichy, on Sep 22,2020
• Delivered a lecture on ‘Equilibrium of Rigid Bodies‘in a FDP on Engineering Mechanics at B.S. Abdur Rahman University, Chennai on 24-12-2014.
• Delivered a guest lecture entitled ‘Balancing of Reciprocating Masses‘ at Velammal Engineering College, Chennai on 19-09-2014.

Extension Additional Responsibilities

• Coordinator for an International virtual conference on “Emerging Trends in Design, Manufacturing, Materials and Thermal Systems” conducted during Sep. 24-25, 2020
• Member in Board of Studies in the Department of Mechanical Engineering
• Member in team for Curriculum Design / Revision in the Department of Mechanical Engineering
• Coordinator for Ph.D Admissions in the Department of Mechanical Engineering
• Coordinator for Research Publication in the Department of Mechanical Engineering
• Supporting member for NAAC / NBA visits at department level
• Coordinator for NIRF data submission for the year 2018 (Engineering) and 2020 (Overall)