Qualification: 
Ph.D
ki_ram@cb.amrita.edu

Dr. K. I. Ramachandran currently serves as Professor at Department of Mechanical Engineering, School of Engineering and Professor at Center for Computational Engineering and Networking (CEN), Coimbatore Campus.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

V. N. Varghees and Dr. K. I. Ramachandran, “Two-channel heart sound segmentation framework using phonocardiogram and pulsatile signals”, in 2016 IEEE Students' Technology Symposium, TechSym 2016, 2017, pp. 305-310.[Abstract]


Phonocardiogram (PCG) segmentation is the crucial first step in automated heart sound analysis and diagnostic systems. Recently, the cardiac signals (including, electrocardiogram, phonocardiogram and photoplethysmogram) are simultaneously recorded for most cardiac signal processing applications such as cardiovascular diagnostic system, biometric authentication, and emotion/stress recognition. In this paper, we present an effective two-channel heart sound segmentation framework using PCG and pulse signals. The proposed framework comprises the steps of: heart sound signal decomposition using stationary wavelet transform, Shannon entropy envelope extraction, heart sound endpoint determination, systolic peak detection, and heart sound discrimination. The proposed framework is tested and validated using the simultaneously recorded heart sound and pulse signals. Performance evaluation results demonstrate that the proposed heart sound endpoint and systolic peak detection methods can achieves an average Se of 98.98%, +P of 96.80% and Se of 99.57%, +P of 99.37%, respectively. The proposed framework achieves an identification accuracy of 100% in distinguishing the first heart sound (S1) and second heart sound (S2) under clean and noisy signal conditions. © 2016 IEEE.

More »»

2017

Conference Paper

K. Sharmila, Sarath, T. V., and Dr. K. I. Ramachandran, “EMG controlled low cost prosthetic arm”, in 2016 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2016 - Proceedings, 2017, pp. 169-172.[Abstract]


Electromyography (EMG) signals have been extensively used as a control signal in robotics, rehabilitation and health care. In this paper, cost effective design of prosthetic hand using EMG control is presented. Signal amplification and filtering is the primary step in surface EMG signal processing and application systems. Quality of the acquired EMG signal depends on the amplifiers and filters employed. Single channel continuous EMG signal has been acquired from the users arm for various hand movements. The acquired signal is passed through various stages of filters and amplifiers for amplification and noise reduction. The conditioned analog signal is converted into digital samples. After the signal acquisition process, features are extracted from the acquired signal and the extracted features are reduced to minimize the number of computations. These reduced feature parameters are used to classify the signal for different hand movements. Once the classifier identifies the intended motion, the control signal will be generated and given to the motors in the prosthetic hand to perform the intended movements. Experiments were done to find the efficiency of the developed system and it is found that this system can give basic movements at a very low cost. © 2016 IEEE.

More »»

2015

Conference Paper

SaSaranya Devi, Dr. K. I. Ramachandran, and Sharma, Ab, “Retinal vasculature segmentation in smartphone ophthalmoscope images”, in IFMBE Proceedings, 2015, vol. 52, pp. 64-67.[Abstract]


Retinal imaging system assists ophthalmologists to diagnose the diseases and to monitor the treatment processes. Conventionally, fundus retinal images are obtained from expensive systems like fluorescein angiography and fundus photography but these systems are large tabletop units and can only be handled by trained technicians. Hence, this study reports a low cost, compact and user friendly smartphone ophthalmoscope to perform indirect ophthalmoscopy. By using this system, initial and periodic screening of retina (both center and periphery regions) becomes easier. Traditionally, retinal diseases are diagnosed by manual observations of fundus images and it is a time consuming process. So, automatic retinal disease diagnosing systems are introduced by extracting the essential features of the fundus retinal images. One of the most essential features of the retina is the blood vessels as its morphological changes helps in diagnosing the retinal diseases. Hence, in this study blood vessels are extracted from smartphone ophthalmoscope (SO) images using level set method to develop an automatic retinal disease diagnosing systems for ophthalmologists. The performance of the retinal vasculature segmentation algorithm is compared and analyzed on DRIVE database of retinal images and on smartphone ophthalmoscope images using the measures like sensitivity, specificity and accuracy level. © Springer International Publishing Switzerland 2015.

More »»

2015

Conference Paper

R. H. Nair, J. Gini, R., and Dr. K. I. Ramachandran, “A simplified approach to identify the fetal ECG from abdECG and to measure the fHR”, in IFMBE Proceedings, 2015, vol. 52, pp. 23-26.[Abstract]


Fetal ECG (fECG) recording aids physicians to diagnose congenital disorders and other anomalies like asphyxia at the early stages of pregnancy. The fECG extraction has been an area of intensive research. Despite the existence of sophisticated and detailed algorithms – based on adaptive filters, independent component analysis (ICA), &c – filtering out the fECG, buried in the noise and mixed up with the maternal ECG (mECG) remains a challenging task. Some residues of mECG are always present in the fECG extracted with all such techniques. A simple algorithm has been developed here to identify the local maxima in the pre-processed abdominal ECG (abdECG) through thresholding; it locates the mECG peaks explicitly. At the outset, the abdECG has been refined by removing the baseline wander and power line interference at a pre-processing stage. With these as pivots the mECG component is eliminated and the fECG of good quality culled out. The fetal heart rate (fHR) and information required to know the condition of fetal heart can be extracted from this fECG effectively. Extraction of these information helps reducing the rate of fetal mortality, and improving the health condition of fetus as well as mother. Performance of the method is better than the conventional adaptive filtering method and the same is proven quantitatively. A processor based realization of the scheme adds to its credibility substantially to ensure its usability in practice. © Springer International Publishing Switzerland 2015.

More »»

2014

Conference Paper

J. Selvaraj, Dr. K. I. Ramachandran, Venkatesh, D., and Devanathan, S., “Greening the foundry sector by an innovative method of energy conservation and emission reduction”, in 2014 IEEE 8th International Conference on Intelligent Systems and Control: Green Challenges and Smart Solutions, ISCO 2014 - Proceedings, 2014, pp. 60-63.[Abstract]


<p>Foundries are known for their energy intensiveness and environmental pollution. More than 50 % of the energy consumed by the foundries is spent in melting the raw materials and this energy goes waste while molten metal solidifies in sand molds. This paper aims at harvesting that waste heat liberated from molten metal, using the harvested heat to preheat the scraps that are embedded into the sand molds. This preheated scraps when melted for the next batch, the energy consumption in the furnace is reduced by 12 %. This energy conservation is a novel approach and readily gives rise to environmental benefits by reducing energy-related emissions. This method also improves the recyclability of the foundry sand, since the peak sand temperature reduces by more than 100 K, making this method doubly environmental-friendly. © 2014 IEEE.</p>

More »»

2006

Conference Paper

G. R. Sabareesh, Sugumaran, V., and Dr. K. I. Ramachandran, “Fault diagnosis of a taper roller bearing through histogram features and proximal support vector machines”, in IEEE international conference on signal and image processing, December, B V Bhoomaraddi college of Engineering and Technology, Hubli, 2006.

2006

Conference Paper

V. Sugumaran, Sachin, K., and Dr. K. I. Ramachandran, “Fault Diagnostics of Roller Bearing using Single-shot Multi-class Support Vector Machine”, in International Conference on Information Technology (ICIT 2006), Indian Institute of Science, Bangalore, 2006.

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

N. V. Varghees and Dr. K. I. Ramachandran, “Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope”, IEEE Sensors Journal, vol. 17, pp. 3861-3872, 2017.[Abstract]


Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods. © 2017 IEEE.

More »»

2017

Journal Article

J. Selvaraj, P. Marimuthu, Dr. Sriram Devanathan, and Dr. K. I. Ramachandran, “Mathematical modelling of raw material preheating by energy recycling method in metal casting process”, Pollution Research, vol. 36, pp. 217-228, 2017.[Abstract]


Modern economic development programs critically depend on reliable supply of energy. Energy conservation has become the need of the hour. Metal casting industry in one among the many industries which uses a lot of energy for its production. This paper presents a novel method of energy recycling in the sand casting process, which readily translates into substantial energy conservation in foundries. The heat that is being wasted into sand during the solidification process is used to preheat the raw material that is melted, for the subsequent pouring. The influence of the casting parameters such as, offset distance, moisture content in the molding sand, and the insulator thickness on the temperature gain by the raw material, have been analyzed. A mathematical model was developed via statistical analysis of the experimental data, to predict the amount of heat recovered from the solidifying molten metal, for any specific combination of values for the experimental parameters. The predicted values are in good agreement with the experimental values.

More »»

2016

Journal Article

A. K. Sandeep, Nithin, S., and Dr. K. I. Ramachandran, “An image processing based pedestrian detection system for driver assistance”, International Journal of Control Theory and Applications, vol. 9, pp. 7369-7375, 2016.[Abstract]


Advanced Driver Assistance System (ADAS) has developed further from just improving traffic safety to an area to help the driver to anticipate accidents. The Pedestrian Protection System (PPS) is a part of ADAS that alerts the driver once the pedestrian is detected. This paper suggests an approach for detecting pedestrians from a real time video and performing braking action once detected. Real time video is captured and the moving background is first modeled. Foreground segmentation is performed on a modeled background. Blob analysis is performed on the foreground pixels to detect the presence of pedestrian. As a control action an alert is given acoustically and braking is performed by actuating the brake pedals using a controller and actuator as a further measure. © International Science Press.

More »»

2015

Journal Article

K. Ka George, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, and Panda, Ab, “Cosine distance features for improved speaker verification”, Electronics Letters, vol. 51, pp. 939-941, 2015.[Abstract]


Similarities are used with people known already as a means to enhance speaker verification accuracy. Motivated by this, experimental work has been conducted regarding the use of cosine distance (CD) similarity with respect to a set of reference speakers, CD features, with a back-end support vector machine (CDF-SVM) classifier for speaker verification. A state-of-the-art i-vector with CD scoring (i-CDS) is used as the baseline system for the experiments and for the computation of CD similarity. Experimental results on the telephone speech of the core short2-short3 conditions of NIST 2008 speaker recognition evaluation (SRE), for female, male and both-gender trials, show that the proposed CDF-SVM outperforms the baseline i-CDS system. The CDF-SVM achieved an absolute improvement of 1.16% in equal error rate (EER) and 0.38% in minimum DCF over the baseline i-CDS for female trials. Similar performance improvements were also obtained for the male and all-gender trials of the SRE. Finally, fusing the CDF-SVM with i-CDS gave the best overall performance, an absolute improvement of 4.19% in EER and 1.99% in minimum DCF, over the individual CDF-SVM system performance for the all-gender trials. Similar performance improvements were also achieved for male and female trials. © The Institution of Engineering and Technology 2015.

More »»

2013

Journal Article

Va Vaijeyanthi, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, Joy, J. Ka, and Kumar, A. Ab, “Application-Specific fine tuning of multi-Parameter patient monitors”, Electronics Letters, vol. 49, pp. 1432-1433, 2013.[Abstract]


Multi-parameter patient monitors (MPMs) have become increasingly important in providing quality health care to patients. A high alarm accuracy (sensitivity) will need a lower threshold for alarm detection which will lead to lower no-alarm accuracy (specificity) and viceversa. MPMs when used in an intensive care unit (ICU) need to have high sensitivity. However they need to have high specificity when used in in-patient wards for regular health check-ups. Proposed is a novel algorithm to trade-off specificity for sensitivity and viceversa depending on the application. The proposed method is referred as detection error trade-off, trade-off specificity for better sensitivity and vice-versa. The algorithm will help to extend the application of MPMs from ICUs to in-patient wards and thus enhance the quality of health care. Experiments have been conducted with an MPM using the classification and regression tree algorithm. By using the proposed algorithm, an improvement of 10.18% in sensitivity was obtained by trading-off 0.40% in specificity. Furthermore, the overall performance of the refined system is 1.15% better than the baseline system. © The Institution of Engineering and Technology 2013.

More »»

2013

Journal Article

P. K Marimuthu, Krishna Kumar P., Rameshkumar, K., and Dr. K. I. Ramachandran, “Finite element simulation of effect of residual stresses during orthogonal machining using ALE approach”, International Journal of Machining and Machinability of Materials, vol. 14, pp. 213–229, 2013.[Abstract]


This paper presents a finite element model that has been developed to predict the effect of residual stress induced in the work material during multiple pass turning of AISI 4340 steel. Chip morphology and force variation during machining are also quantified using the FE model. Finite element model was developed using arbitrary Lagrangian-Eulerian formulation along with Johnson-Cook material model and Johnson-Cook damage model. The finite element model developed in this study was validated experimentally by studying the chip morphologogy and cutting force variation during the machining. Results indicate that there is good correlation existing between numerical results and experimental results.

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 »»

2010

Journal Article

B. S Devasenapati, Sugumaran, V., and Dr. K. I. Ramachandran, “Misfire Identification in A Four-Stroke Four-Cylinder Petrol Engine Using Decision Tree”, Expert Systems with Applications, vol. 37, pp. 2150–2160, 2010.[Abstract]


Misfire detection in an internal combustion engine is very crucial to maintain optimum performance throughout its service life and to reduce emissions. The vibration of the engine block contains indirect information regarding the condition of the engine. Misfire detection can be achieved by processing the vibration signals acquired from the engine using a piezoelectric accelerometer. This hidden information can be decoded using statistical parameters like kurtosis, standard deviation, mean, median, etc. This paper illustrates the use of decision tree as a tool for feature selection and feature classification. The effect of dimension, minimum number of objects and confidence factor on classification accuracy are studied and reported in this work. More »»

2010

Journal Article

N. Saravanan, Siddabattuni, V. N. S., and Dr. K. I. Ramachandran, “Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)”, Applied Soft Computing, vol. 10, pp. 344–360, 2010.[Abstract]


Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or assembly under study. This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gear box using artificial neural network (ANN) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared. More »»

2009

Journal Article

N. Saravanan, Cholairajan, S., and Dr. K. I. Ramachandran, “Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique”, Expert Systems with Applications, vol. 36, no. 2, pp. 3119–3135, 2009.[Abstract]


To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. The vibration signal acquired from the operating machine has been used to effectively diagnose the condition of inaccessible moving components inside the machine. Suitable sensors are kept at various locations to pick up the signals produced by machinery and these signals are very meaningful in condition diagnosis surveillance. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing is required. This paper presents the use of decision tree for selecting best statistical features that will discriminate the fault conditions of the gear box from the signals extracted. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions – good bevel gear, bevel gear with tooth breakage (GTB), bevel gear with crack at root of the tooth (GTC), and bevel gear with face wear of the teeth (TFW) for various loading and lubrication conditions. The statistical features were extracted and good features that discriminate the different fault conditions of the gearbox were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging. More »»

2009

Journal Article

N. Saravanan, Siddabattuni, V. N. S., and Dr. K. I. Ramachandran, “Static and Dynamic Analysis of Asymmetric Bevel Gears using Finite Element Method.”, International Journal of Applied Engineering Research, vol. 4, pp. 645-664, 2009.[Abstract]


The aim of asymmetric tooth is to improve the performance of gears such as increasing the load capacity or reducing noise and vibration. Application of asymmetric tooth side surfaces is able to increase the load capacity and durability for the drive tooth side. The tooth form has left-right symmetry in the involute cylindrical gear, and the same performance can be obtained at forward and backward rotation. However, both the forward and backward rotations are not always expected in the practically used gear units for power transmission. Therefore, two sides of the gear tooth are functionally different for most gears. Even if one side (drive side) is significantly loaded for longer periods, the opposite side (coast side) is unloaded or slightly loaded for short duration only. In several papers , the higher pressure angle profile for the drive side and lower pressure angle profile for the coast side have been considered. This kind of application makes it possible for the gear to reduce the bending stress. The asymmetric involute tooth can be manufactured by the same process as in generating the symmetric involute tooth. Asymmetric profile is achievable by adopting the different pressure angle values of coast side and drive side of the bevel gear for the two sides of the rack. Depending on the special tooling, production cost of these gears increases. Therefore, the gears with asymmetric teeth should be considered for gear systems that require extreme performance like aerospace applications and for mass production, where the share of the tooling cost per one gear is insignificant. The most promising application of asymmetric profiles seems to be in molded gears and powder gears. In this study, asymmetric spur bevel gear with higher drive side pressure angle than coast side pressure angle has been considered. The purpose of this study is to determine bending load carrying capacity and the dynamic characteristics of asymmetric bevel gear. More »»

2009

Journal Article

V. Sugumaran and Dr. K. I. Ramachandran, “Wavelet Selection Using Decision Tree for Fault Diagnosis of Roller Bearings”, International Journal of Applied Engineering Research, vol. 4, pp. 201-225, 2009.[Abstract]


Fault diagnosis of the roller bearings as pattern classification problem has three main steps: feature extraction, feature selection and classification. Wavelets have been widely used for feature extraction from vibration signals. Identifying a suitable wavelet for a given application is a challenging task in the whole process. This paper investigates the use of decision tree for selecting apt wavelet for fault diagnosis of roller bearings with discrete wavelet transform features. The study is done on vibration signals of roller bearings from different fault conditions. The faults considered in this study are bearings with inner race fault, bearings with outer race fault and bearings with both of them. The decision tree has been used for feature selection as well as for classification. Many commonly used wavelets families have been considered in this study and their classification accuracies were compared. More »»

2009

Journal Article

N. Saravanan and Dr. K. I. Ramachandran, “A case study on classification of features by fast single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box”, Expert Systems with Applications, vol. 36, pp. 10854–10862, 2009.[Abstract]


This paper deals with the application of fast single-shot multiclass proximal support vector machine for fault diagnosis of a gear box consisting of twenty four classes. The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing multiclass proximal support vector machine. The efficiency and time consumption in classifying the twenty four classes all-at-once is reported. More »»

2009

Journal Article

N. Saravanan and Dr. K. I. Ramachandran, “Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification”, Expert Systems with Applications, vol. 36, pp. 9564–9573, 2009.[Abstract]


The wavelet transform (WT) is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In present work, a discrete wavelet, Daubechies wavelets (db1–db15) is used for feature extraction and their relative effectiveness in feature extraction is compared. The major steps in pattern classification are feature extraction and classification. This paper investigates the use of discrete wavelets for feature extraction and a Decision Tree for classification. J48 Decision Tree algorithm has been used for feature selection as well as for classification. This paper illustrates the powerfulness and flexibility of the discrete wavelet transform to decompose linear and non-linear processing of vibration signal. More »»

2008

Journal Article

N. Saravanan, Siddabattuni, V. N. S. Kumar, and Dr. K. I. Ramachandran, “A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box”, Expert systems with applications, vol. 35, pp. 1351–1366, 2008.[Abstract]


The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared. More »»

2008

Journal Article

V. Sugumaran, Sabareesh, G. R., and Dr. K. I. Ramachandran, “Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine”, Expert Systems with Applications, vol. 34, pp. 3090–3098, 2008.[Abstract]


Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing’s nature of vibration reveals its condition and the features that show the nature are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Finding out good features that discriminate the different fault conditions of the bearing is often a problem. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper addresses the feature selection process using decision tree and uses kernel based neighborhood score multi-class support vector machine (MSVM) for classification. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race faults. The statistical features are extracted therefrom and classified successfully using MSVM. The results of MSVM are compared with and binary support vector machine (SVM). More »»

2007

Journal Article

V. Sugumaran, Muralidharan, V., and Dr. K. I. Ramachandran, “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, pp. 930–942, 2007.[Abstract]


Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing's nature of vibration reveals its condition and the features that show the nature, are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Often the problem is, finding out good features that discriminate the different fault conditions of the bearing. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper illustrates the use of a Decision Tree that identifies the best features from a given set of samples for the purpose of classification. It uses Proximal Support Vector Machine (PSVM), which has the capability to efficiently classify the faults using statistical features. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted therefrom and classified successfully using PSVM and SVM. The results of PSVM and SVM are compared. More »»

2007

Journal Article

V. Sugumaran and Dr. K. I. Ramachandran, “Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing”, Mechanical Systems and Signal Processing, vol. 21, pp. 2237–2247, 2007.[Abstract]


Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has two main phases: feature extraction and feature classification. Statistical features like minimum value, standard error and kurtosis, etc. are widely used as features in fault diagnostics. These features are extracted from vibration signals. A rule set is formed from the extracted features and input to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper presents the use of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions—good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted and good features that discriminate the different fault conditions of the bearing are selected using decision tree. The rule set for fuzzy classifier is obtained once again by using the decision tree. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging. More »»

Publication Type: Book

Year of Publication Publication Type Title

2010

Book

Dr. Soman K. P. and Dr. K. I. Ramachandran, Insight into wavelets: From theory to practice. PHI Learning Pvt. Ltd., 2010.[Abstract]


Wavelet theory has matured and has entered into its second phase of development and evolution in which practitioners are finding newer applications in ever-widening scientific domains such as bio-informatics, computational drug discovery and nano-material simulation. Parallelly, the theory of wavelets got more and more demystified and has become an everyday tool for signal and image processing. Postgraduate courses in mathematics and physics now include a subject on wavelet theory either as a separate... More »»

2008

Book

Dr. K. I. Ramachandran, Deepa, G., and P. K. Krishnan Namboori, Computational Chemistry and Molecular Modeling. Springer, 2008.[Abstract]


Computational chemistry and molecular modeling is a fast emerging area which is used for the modeling and simulation of small chemical and biological systems in order to understand and predict their behavior at the molecular level. It has a wide range of applications in various disciplines of engineering sciences, such as materials science, chemical engineering, biomedical engineering, etc. Knowledge of computational chemistry is essential to understand the behavior of nanosystems; it is probably the easiest route or ...

More »»

207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS