Dr. V. Muralidharan

Teaching

Educational Background

Work Experience

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 : 08

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, 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, 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, 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, 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, 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, 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, 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
1. 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
2. S Ravikumar, H Kanagasabapathy, 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
3. S Ravikumar, H Kanagasabapathy, 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
4. Pradeep Kumar Durairaj, 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/
5. 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/
6. S Ravikumar, 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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),2,No.1, pp.1-6.
    https://iaeme.com/Home/article_id/IJDMT_02_01_001
19. 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
20. 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
21. V Muralidharan, V Sugumaran, Bharathkumar Hegde, 2010, Fault Diagnosis of Centrifugal Pump Using Haar Wavelet Transform, J of Applied Engineering Research, Vol.5, No.13, pp.2307-2316.
22. 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
23. 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
24. 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

• 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
• 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
• 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
• 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
• 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
• Sugumaran S. Siddarth, V. Muralidharan, 2011, Condition Assessment of Robots in Flexible Assembly System, COSMA 2011.
• Sugumaran V. Muralidharan, 2011, Selection of Wavelets for Fault Diagnosis of Monoblock Centrifugal pump using decision tree Algorithm, COSMA 2011

Ph.D. Guidance

• No. of Ph.D. Scholars : 05 (On going)

International Recognition

• Awarded as an Outstanding reviewer for the Journal ISA Transactions, Elsevier, in the year 2017 (Impact factor: 5.468)

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 on “Wavelets and its Applications – An Introduction” in a 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)