Qualification: 
Ph.D, M.E, BE
m_saimurugan@cb.amrita.edu

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

Education

  • 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 and Data Analytics : Automated fault diagnosis of dynamic mechanical systems using vibration, acoustic and sound signals.
  • Surface roughness prediction and Tool wear monitoring.
  • Product Design and Development

Sponsored Projects

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2018

Journal Article

S. Saivenkatesh, Ramkumar, A., Blalakumhren, A. P., Dr. Saimurugan M., and K. Marimuthu, P., “Finite element simulation and experimental validation of the effect of tool wear on cutting forces in turning operation”, Mechanics and Mechanical Engineering, 2018.

2018

Journal Article

T. Praveenkumar, Sabhrish, B., Dr. Saimurugan M., and Dr. K. I. Ramachandran, “Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox”, Measurement, vol. 114, pp. 233 - 242, 2018.[Abstract]


Gearbox is an important equipment in an automobile to transfer power from the engine to the wheels with various speed ratios. The maintenance of the gearbox is a top criterion as it is prone to a number of failures like tooth breakage and bearing cracks. Techniques like vibration monitoring have been implemented for the fault diagnosis of the gearbox over the years. But, the experiments are usually conducted in lab environment where the actual conditions are simulated using setup consisting of an electric motor, dynamometer, etc. This work reports the feasibility of performing vibrational monitoring in real world conditions, i.e. by running the vehicle on road and performing the analysis. The data was acquired for the various conditions of the gearbox and features were extracted from the time-domain data and a decision tree was trained for the time-domain analysis. Fast Fourier Transform was performed to obtain the frequency domain which was divided into segments of equal size and the area covered by the data in each segment was calculated for every segment to train decision trees. The classification efficiencies of the decision trees were obtained and in an attempt to improve the classification efficiencies, the time-domain and frequency-domain analysis was also performed on the normalised time-domain data. From, the results obtained, it was found that performing time-domain analysis on normalised data had a higher efficiency when compared with the other methods. Instantaneous processing of the acquired data from the accelerometer enables faster diagnosis. Hence, online condition monitoring has gained importance with the advent of powerful microprocessors. A windows application that has been developed to automate the process was found to be essential and accurate.

More »»

2017

Journal Article

Dr. Saimurugan M. and Ramprasad R, “A dual sensor signal fusion approach for detection of faults in rotating machines”, Journal of Vibration and Control, vol. 24, pp. 2621–2630, 2017.[Abstract]


The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.

More »»

2017

Journal Article

T. Praveenkumar, Dr. Saimurugan M., and Dr. K. I. Ramachandran, “Comparision of Sound, Vibration and motor current signature analysis for detection of gearbox faults”, International Journal of Prognostics and Health Management, vol. 8, no. 2, pp. 1-10, 2017.[Abstract]


Gear box is used in automobiles and industries for power transmission under different working conditions and applications. Failure in a gear box at unexpected time leads to increase in machine downtime and maintenance cost. In order to overcome these losses, the most effective condition monitoring technique has to be used for early detection of faults. Vibration and sound signal analysis have been used for monitoring the condition of rotating machineries. Motor Current Signature Analysis (MCSA) has rarely been used in gearbox condition monitoring. This work presents a methodology based on vibration, sound and motor current signal analysis for diagnosis of gearbox faults under various simulated gear and bearing fault conditions. Statistical features were extracted from the raw data of these three transducer signals and the best features were selected from the extracted features. Then the selected features were given as an input to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers and their performances were compared. In recent years, Hybrid Electric Vehicles (HEV) are gaining more interest for their advances and this work had a scope in monitoring the power loss in hybrid electric vehicle gearbox using MCSA.

More »»

2016

Journal Article

Krishna Pradeep G.V., Dr. Saimurugan M., and Ravikumar S., “Tool Wear Monitoring Using the Fusion of Vibration Signals and Digital Image”, Journal of Chemical and Pharmaceutical Sciences , vol. 9, pp. 537-541, 2016.

2016

Journal Article

Dr. Saimurugan M., T. Praveenkumar, B Sabhrish, P. Sachin Menon, and S Sanjiv, “On-Road Testing of a Vehicle for Gearbox Fault Detection using Vibration Signals”, Indian Journal of Science and Technology, vol. 9, no. 34, 2016.[Abstract]


Gearbox is one of the most important components in an automobile, enabling power transmission from the engine to the wheels. Gears and bearings are prone to failure. The impending case of failure can be predicted by performing vibration analysis of a gear box, usually done by acquiring data in lab conditions. Objective: This paper proposes an idea to enable fault detection in the gearbox by acquiring data under on road conditions without having to remove the gearbox, thereby simplifying the condition monitoring of a gearbox. Methodology: The experimental studies were conducted on the gearbox in a test vehicle run in real time conditions and the vibration data from the gearbox was acquired using a piezoelectric accelerometer for different conditions of gearbox. The acquired time domain data was normalized and its statistical features were extracted. The classification of the fault class was done by using decision tree (J48) algorithm. Findings: Classification efficiencies as high as 99% were obtained by using decision tree algorithm. Further, normalization of raw data was found to increase the efficiency of the classifier. This observation can be used to make decision trees more efficient. Improvements: This paper has highlighted the concept of on road testing for two fault conditions. Further research work on other fault conditions can be done.

More »»

2016

Journal Article

Dr. Saimurugan M. and Nithesh, R., “Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals”, International Journal of Prognostics and Health Management, vol. 7, no. 2, pp. 1-10, 2016.[Abstract]


The failure of rotating machine elements causes unnecessary downtime of the machine. Fault in the rotating machinery can be identified from noises, vibration signals obtained from sensors. Bearing and shaft are most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques. Fault diagnosis from sound signals is cost effective than vibration signals.Sound signal analysis was not well explored in the field of automated fault diagnosis. Under various simulated fault conditions, the sound signals are obtained by placing microphone near the bearing for different speeds. The features are extracted by using statistical and histogram methods. The best features of sound signals are obtained by decision tree algorithm. The extracted features are used as inputs to the classifier-Artificial Neural Network. The efficiency results from statistical and histogram features are obtained and compared.

More »»

2015

Journal Article

K. Korambeth Rajat, Rajan, M. T. V. Akhil, S.M, P., and Dr. Saimurugan M., “Design and Fabrication of Foldable Bicycle International ”, International Journal of Applied Engineering Research , vol. 10, pp. 32577-32584, 2015.

2015

Journal Article

P. Sundar, KN, V., Dr. 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

Neethu Mohan, Ambika, P. S., S. Kumar, S., Dr. Saimurugan M., and Soman, K. P., “Multicomponent fault diagnosis using statistical features and regularized least squares”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19074-19080, 2015.[Abstract]


The efficiency and performance of rotating machinery is of major concern in any industrial system. Proper machine condition monitoring is really crucial for identifying the health of machines. The detection and diagnosis of faults in the machinery is important in proper machine condition monitoring. In this paper the multicomponent fault diagnosis in mechanical systems is formulated as machine learning based pattern classification problem. A machine fault simulator setup with different fault conditions induced in its shaft-bearing assembly is utilised for the purpose. The machine is made to run in various good and faulty environments and the vibration signals are extracted from them using an accelerometer. The statistical features extracted from the vibration signals were used for representing the signal in the feature space. The decision tree algorithm is used for selecting the major features that contribute towards classification. Later those features are classified using regularized least squares algorithm for identifying the good and faulty shaft-bearing conditions of the machine. The results were obtained with different kernel functions and accuracies are compared. © Research India Publications. More »»

2015

Journal Article

Dr. Saimurugan M., T. Praveenkumar, Krishna Kumar 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

Dr. Saimurugan M., “Health Monitoring of a Gear Box Using Vibration Signal Analysis”, Applied Mechanics and Materials, vol. 813-814, pp. 1012-1017, 2015.

2014

Journal Article

T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar 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 »»

2014

Journal Article

Dr. Saimurugan M. 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 »»

2011

Journal Article

Dr. Saimurugan M., Dr. K. I. Ramachandran, Sugumaran, V., and Dr. Sakthivel N.R., “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 Paper

Year of Conference Publication Type Title

2016

Conference Paper

K. T. Sreekumar, Gopinath, R., Pushparajan, M., A.S. Raghunath, Dr. Santhosh Kumar C., Ramachandran, K. I., and Dr. Saimurugan M., “Locality constrained linear coding for fault diagnosis of rotating machines using vibration analysis”, in 12th IEEE India International Conference on Electronics, Energy, Environment, Communication, Computer Science, Control (INDICON, 2015), JamiaMilliaIslamia, NewDelhi, 2016.[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.

More »»

2014

Conference Paper

P. T Kumar, Jasti, A., Dr. 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 »»

Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2015

Conference Proceedings

G. V. Krishna Pradeep, Dr. 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

A. Jasti, .T, P., and Dr. 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.

2014

Conference Proceedings

G. V. Krishna Pradeep, Dr. 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.

2013

Conference Proceedings

A. Arun, Kumar, R., Dr. Saimurugan M., and A. Sumesh, “Experimental Evaluation of Grinding Wheel Wear Using Vibration Based Technique”, 2nd International Conference on Intelligent Robotics, Automation and Manufacturing (IRAM 2013). Emerald group, IIT- Indore, p. 364, 2013.

2012

Conference Proceedings

S. Quadir Moinuddin, .K, R., Dr. 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

Dr. Saimurugan M., 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

Dr. Saimurugan M., 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.

Honors & Awards

  1. The paper titled “Locality Constrained Linear Coding for Fault Diagnosis of Rotating Machines using Vibration Analysis” selected as a BEST PAPER in 12th IEEE India International Conference on Electronics, Energy, Environment, Communication, Computer Science, Control (INDICON, 2015), JamiaMilliaIslamia, NewDelhi, 2015.
  2. The paper titled “Tool Wear Monitoring Using The Fusion of Vibration Signals and Digital Image” selected as a BEST PAPER in the International Conference on Soft Computing in Applied Sciences and Engineering, Noorul Islam University, Kanyakumari, India, 2015
  3. Tamilnadu State Council for Science and Technology awarded Rs.10,000/- for final year project under Student Project Scheme in the academic year 2003-2004.

Technical Talk

  1. Delivered a lecture titled “Automated identification of weld defects" in one day workshop on Signature Analysis of Arc Welding, Amrita school of Engineering, Amrita VishwaVidyapeetham,Coimbatore, 6th March 2017
  2. Delivered a lecture titled “Fault diagnosis of gearbox using Machine learning techniques” in KIC-TEQIP Sponsored two days short term course on “ Advanced Gear Engineering” at Indian Institute of Technology, Guwahati during 21st-22nd, November, 2015.
  3. 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.
  4. Delivered a Lecture on “Biodiesel” in AICTE Sponsored nine days short term training programme on “Sustainable development through green manufacturing”, at Amrita school of Engineering, Coimbatore, February 2005.

Workshops Organized

  1. M. Saimurugan, P. Krishnakumar, A. Sumesh - Organiser, Signature Analysis of Arc Welding, Amrita school of Engineering, Amrita VishwaVidyapeetham,Coimbatore, 6th March 2017
  2. M. Saimurugan, P. Krishnakumar, A. Sumesh - Organiser, Condition Monitoring Applications in Machining and Welding, Amrita school of Engineering, Amrita VishwaVidyapeetham,Coimbatore, February 2015.
  3. M. Saimurugan, P. Krishnakumar, A. Sumesh - Organiser, Recent Trends in Manufacturing, Amrita school of Engineering, Amrita VishwaVidyapeetham,Coimbatore, March 2014.

Doctoral Students

  1. “Fault diagnosis of dynamic mechanical systems (gearbox) based on signal processing using machine learning techniques”, T. Praveenkumar, Department of Mechanical Engineering,Amrita Vishwa Vidyapeetham, Coimbatore, India.(Pursuing)
  2. "Estimation of remaining useful life of a rotating component using deep learning", Jithin Jose, Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.(Pursuing)