Dr. Saimurugan M. currently serves as Assistant Professor at department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Vibration Analysis, Machine Learning and Machine Condition Monitoring.


  • Ph.D., Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 2013.
  • M. E., Computer Aided Design Government College of Engineering, Periyar University, Salem, May, 2000.                                        
  • B.E., Mechanical Engineering, Kongu Engineering College, Bharathiar University, Coimbatore, May, 1998.                                        

Areas of Research Interest

  •  Machine Condition Monitoring: Automated fault diagnosis of dynamic mechanical systems using vibration, acoustic and sound signals.

Sponsored Projects (Completed)

Fault diagnosis of dynamic mechanical systems (gearbox) based on signal processing using machine learning techniques.

Sponsored Projects (On Going)

Investigation into the surface integrity of the Titanium Alloys during high speed machining


Publication Type: Journal Article
Year of Conference Publication Type Title
2015 Journal Article M. Saimurugan, “Health Monitoring of a Gear Box Using Vibration Signal Analysis”, Applied Mechanics and Materials, vol. 813-814, pp. 1012-1017, 2015.
2015 Journal Article M. Saimurugan, Praveenkumar, T., Krishnakumar, P., and Ramachandran, K. I., “A Study on the Classification Ability of Decision Tree and Support Vector Machine in Gearbox Fault Detection”, Applied Mechanics and Materials, vol. 813-814, pp. 1058-1062, 2015.
2015 Journal Article P. Sundar, KN, V., Saimurugan, M., G. Kumare, P., and Sreenath, P. G., “Automobile Gearbox Fault Diagnosis using Naive Bayes and Decision Tree Algorithm.”, Applied Mechanics & Materials, vol. 813/814, pp. 943-948, 2015.[Abstract]

Gearbox plays a vital role in various fields in the industries. Failure of any component in the gearbox will lead to machine downtime. Vibration monitoring is the technique used for condition based maintenance of gearbox. This paper discusses the use of machine learning techniques for automating the fault diagnosis of automobile gearbox. Our experimental study monitors the vibration signals of actual automobile gearbox with simulated fault conditions in the gear and bearing. Statistical features are extracted and classified for identifying the faults using decision tree and Naïve Bayes technique. Comparison of the techniques for determining the classification accuracy is discussed.

More »»
2015 Journal Article K. Korambeth Rajat, Rajan, M. T. V. Akhil, S.M, P., and Saimurugan, M., “Design and Fabrication of Foldable Bicycle International ”, International Journal of Applied Engineering Research , vol. 10, pp. 32577-32584, 2015.
2014 Journal Article M. Saimurugan and Ramachandran, K. I., “A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis”, International Journal of Data Analysis Techniques and Strategies, vol. 6, pp. 188–204, 2014.[Abstract]

M. Saimurugan obtained his BE in Mechanical Engineering at Kongu Engineering College, Erode under Bharathiar University, Coimbatore in 1998. He completed his ME in Computer Aided Design at Government College of Engineering, Salem under Periyar University, Salem in 2000. Then, he started his career as a Lecturer at Amrita Institute of Technology, Coimbatore. He has published one international journal paper and four international conference papers. Currently, he is working as an Assistant Professor at Amrita Vishwa Vidyapeetham, Coimbatore and is doing his PhD on vibration and sound-based fault diagnosis of rotating machines.

More »»
2014 Journal Article T. Praveenkumar, Saimurugan, M., Krishnakumar, P., and Ramachandran, K. I., “Fault Diagnosis of Automobile Gearbox Based on Machine Learning Techniques”, Procedia Engineering, vol. 97, pp. 2092–2098, 2014.[Abstract]

Gearbox is an essential device employed in industries to vary speed and load conditions according to the requirements. More advancement in its design and operation leads to increase in industrial applications. The failure in any of the components of gearbox can lead to production loss and increase maintenance cost. The component failure has to be detected earlier to avoid unexpected breakdown. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. This paper addresses the use of vibration signal for automated fault diagnosis of gearbox. In the experimental studies, good gears and face wear gears are used to collect vibration signals for good and faulty conditions of the gearbox. Each gear is tested with two different speeds and loading conditions. The statistical features are extracted from the acquired vibration signals. The extracted features are given as an input to the support vector machine (SVM) for fault identification. The Performance of the fault identification system using vibration signals are discussed and compared.

More »»
2011 Journal Article M. Saimurugan, R.b Ramachandran, Sugumaran, Vb, and Sakthivel, N. Ra, “Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine”, Expert Systems with Applications, vol. 38, pp. 3819-3826, 2011.[Abstract]

The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared. © 2010 Elsevier Ltd. All rights reserved. More »»
Publication Type: Conference Proceedings
Year of Conference Publication Type Title
2015 Conference Proceedings G. V. Krishna Pradeep, Saimurugan, M., and Ravikumar, S., “Tool Wear Monitoring Using The Fusion of Vibration Signals and Digital Image”, International Conference on Soft Computing in Applied Sciences and Engineering. Noorul Islam University, Kanyakumari, India, 2015.[Abstract]

Tool wear monitoring is an indispensable peremptorily of advanced manufacturing in order to evolve an automated unmanned production. Continuously machining with a worn or impaired tool will result in damage to the work piece. This difficulty becomes more important in subsidiary machining processes like milling which the tool has regularly passed a lot of machining processes and any destruction to work piece at these level consequences in more production losses. In this work, vibration signals in milling process are recorded and examined carefully in order to detect tool wear. The online acquiring of machined surface images has been done at intervals and those captured periodic texture of machined surface images are analysed for detection of tool wear. The vibration signals and the digital images are analysed using data mining techniques, decision tree to classify the tool wear. Further, the effectiveness of fusion of sensory data from the CCD camera (Image analysis) and an accelerometer (Vibration analysis) in tool wear prediction is checked and compared.

More »»
2014 Conference Proceedings G. V. Krishna Pradeep, Saimurugan, M., and Ravikumar, S., “Design and development of Tool Wear Measurement System”, International Conference on Advanceds in Design and Manufacturing(ICAD&M’14). National Institute of Technology Trichy, India, 2014.
2014 Conference Proceedings A. Jasti, .T, P., and Saimurugan, M., “Machine learning based fault diagnosis of gearbox using sound and vibration signal”, 8th International Conference on Science Engineering and technology(SET), . School of Advanced Sciences, VIT University, Vellore, India, 2014.
2013 Conference Proceedings R. Kumar Arun, Saimurugan, M., and Sumesh, A., “Experimental Evaluation of Grinding Wheel Wear Using Vibration Based Technique”, 2nd International Conference on Intelligent Robotics, Automation and Manufacturing. IIT- Indore, 2013.
2012 Conference Proceedings S. Quadir Moinuddin, .K, R., Saimurugan, M., Santhakumari, A., and Rajasekaran, N., “Signature Analysis of welding defects in STB joints using GMAW process”, International symbosium on joining of materials. WRI, Trichy, 2012.
2009 Conference Proceedings M. Saimurugan, Ramachandran, K. I., and Sugumaran, V., “Machine learning approach to Fault Diagnosis of Rotational Mechanical Systems ”, International Conference on Operations Research applications in Engineering and Management (ICOREM). Anna University, Tiruchirappalli, India, 2009.
2008 Conference Proceedings M. Saimurugan, Ramachandran, K. I., and Sugumaran, V., “Support Vector Machine based Fault Diagnosis of Rotational Mechanical Systems”, International Conference on Recent Trends in Materials and Mechanical Engineering. Dr.Mahalingam College of Technalogy, pollachi , India, 2008.
Publication Type: Conference Paper
Year of Conference Publication Type Title
2014 Conference Paper P. T Kumar, Jasti, A., Saimurugan, M., and Ramachandran, K. I., “Vibration Based Fault Diagnosis of Automobile Gearbox Using Soft Computing Techniques”, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, New York, NY, USA, 2014.[Abstract]

Gearbox is the core component in any automotive/industrial application and it consists of gears and gear trains to vary the speed and torque of the machine. In order to reduce the machine breakdown cost and to increase the service life it is vital to know its operating conditions frequently to find the point of defect. The vibration signals are used to extract statistical features for 3 different classes namely Gearbox with Good gear, Gear Tooth breakage and Gear Face wear. The features were collected according to the experimental conditions with 3 fault classes, 3 speeds and 1 load condition with total of 9 testing conditions. The prominent statistical features were selected using decision tree algorithm. The set of IF-Then rule was generated and coded in LabVIEW for automated machine fault diagnosis.

More »»

Workshop – Organized

  1.  M.Saimurugan, P.Krishnakumar,A.Sumesh  Organiser, Recent Trends in Manufacturing, Amrita school of Engineering, Amrita Vishwa Vidyapeetham,Coimbatore, March 2014.
  2.  M.Saimurugan, P.Krishnakumar, A.Sumesh  Organiser, Condition Monitoring Applications in Machining and Welding, Amrita school of Engineering, Amrita Vishwa Vidyapeetham,Coimbatore, February 2015.

Invited Talks

  1.  M.Saimurugan, Invited Speaker,   “Machine Learning based Fault diagnosis of Gearbox using Vibration and Acoustic signals” TEQIP short term course on Advanced Gear Engineering, Organised by IIT Guwahati, November, 2015.
Faculty Details


Faculty Email: