Dr. V. Muralidharan | Crescent Education
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Dr. V. Muralidharan

Designation : Associate Professor
Nature of Employment : Regular
Qualification : B.E,.M.Tech.,Ph.D.
Phone : +91 44 2275 1347,48,50 (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 7 yrs
Associate Professor G.K.M College of Engineering & Technology, Perungalathur 02.05.2013 – 07.03.2014 10 Months
Assistant Professor SRM University, Kattankulathur 03.12.2009 – 30.04.2013 3 yrs 5 months
Assistant System Engineer Tata Consultancy Services, Mumbai 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 Journal Publications

  • 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.55, No. 2, pp. 364-371
  • 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
  • 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
  • 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, Vol. 1, pp.37-45
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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

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

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
  • 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
  • Syed Shaul Hameed, Muralidharan V, 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
  • 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
  • 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

International Recognition:

  • Awarded as an Outstanding reviewer for the Journal ISA Transactions, Elsevier, in the year 2017 (Impact factor: 4.305)

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 “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

  • Co – 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 Course Files in the Department of Mechanical Engineering
  • Coordinator for NIRF data submission for the year 2018 (Engineering) and 2020 (Overall)