Qualification: 
M.Tech
m_pushparajan@cb.amrita.edu

Pushparajan M. currently serves as Assistant Professor at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Operations Research.

Education

  • 2010 : M.Tech. Engineering Design
    Amrita Vishwa Vidyapeetham, Coimbatore
  • 1997 : B.Tech. Mechanical Engineering
    Govt. College of Engg., Kannur

Experience

  • August 11, 2000 - Present:

Assistant Professor
School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore

  • June 26, 1999 - July 31, 2000:

Lecturer
Model Poly Technic, Kalliasseri

  • August 12, 1998 - May 31, 1999:

Lecturer
Govt. College of Engineering, Kannur

  • May 25, 1997 - August 6, 1998:

Industrial Trainee
The Western India Plywoods Ltd., Baliapattam, Kannur

Courses Handled

  • Engineering Mechanics
  • Engineering Drawing
  • Mechanical Vibrations
  • Operations Research
  • Fluid Mechanics
  • Solid Mechanics
  • Statistical Quality Control
  • Basic Mechanical Engineering

Publications

Publication Type: Conference Proceedings

Year of Publication Title

2015

K. T. Sreekumar, Gopinath, R., Pushparajan M., A.S. Raghunath, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, and Dr. Saimurugan M., “Locality Constrained Linear Coding for Fault Diagnosis of Rotating Machines using Vibration Analysis”, 12th IEEE India International Conference on Electronics, Energy, Environment, Communication, Computer Science, Control (INDICON, 2015). Institute of Electrical and Electronics Engineers Inc., JamiaMilliaIslamia, NewDelhi, pp. 1-6, 2015.[Abstract]


Support Vector Machine (SVM) is an important machine learning algorithm widely used for the development of machine fault diagnosis systems. In this work, we use an SVM back-end classifier, with statistical features in time and frequency domain as its input, for the development of a fault diagnosis system for a rotating machine. Our baseline system is evaluated for its speed dependent and speed independent performances. In this paper, we use locality constrained linear coding (LLC) to map the input feature vectors to a higher dimensional linear space, and remove some of the speed specific dimensions to improve the speed independent performance of the fault diagnosis system. We use LLC to do the feature mapping to the higher dimensional space, and select only the k nearest neighbour basis vectors to represent the input feature vector and thus reduce/minimize the effect of speed specific factors from the input feature vector, and thus improve the speed independent performance of the fault diagnosis. We compare the performance of the LLC-SVM system for the time and frequency domain statistical features. The proposed approach has improved the overall classification accuracy by 11.81% absolute for time domain features and 10.53% absolute for frequency domain features compared to the baseline speed independent system. © 2015 IEEE.

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2013

R. Gopinath, Nambiar, T. N. P., Abhishek S., Pramodh, S. M., Pushparajan M., Dr. K. I. Ramachandran, Dr. Santhosh Kumar C., and Thirugnanam, R., “Fault Injection Capable Synchronous Generator for Condition based Maintenance”, 7th International Conference on Intelligent Systems and Control, ISCO 2013. Coimbatore, Tamilnadu, pp. 60-64, 2013.[Abstract]


This paper presents the design specifications of an experimental setup capable of injecting faults in a synchronous generator to develop and test algorithms for condition based maintenance of aerospace applications. A 3 kVA alternator is designed to inject faults in the stator winding and field windings. The system is capable of injecting open and short circuit faults in the stator and rotor windings using a fault injection unit and record current, voltage and vibration signals from the respective sensors, with programming capability. The response of the synchronous generator during normal condition and faulty condition are discussed. The work reported in this paper will help other researchers develop low cost experimental facility to pursue research in machine condition monitoring. © 2013 IEEE.

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Faculty Research Interest: