Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Design and Automation

Health diagnosis of bearing is essential reduce the breakdowns of rotating machinery. An intelligent method to diagnose the bearing fault using vibration signal is proposed. This paper proposes a binary genetic algorithm (BGA) in feature selection process and discuss about the role of fitness functions in feature selection process by application of different fitness functions in GA process. A vibration signal from various conditions of bearing is extracted from a test rig and statistical features extracted using wavelet coefficients by continuous wavelet transform (CWT). A new heuristic classifier artificial bee colony (ABC) algorithm is applied and fault diagnosis results are compared with learning vector quantization (LVQ) classifier and their relative efficiency were compared based on their classification accuracy. To select the predominant features a famous feature selection approach a binary genetic algorithm (BGA) were used. © June 2015 IJENS.

School of Mechanical Engineering

Vellore Campus

World University root node

Department of Software and Systems Engineering

School of Information Technology and Engineering

International Journal of Mechanical and Mechatronics Engineering

Bearing fault diagnosis using CWT, BGA and artificial bee colony algorithm

The reduction of corrosion and hydrogen permeation through mild steel in 0. 5M H2SO4 and 1M HCl has been studied using weight loss measurements and various electrochemical techniques in the presence and absence of a green inhibitor viz., Carbenicillin(CBN). The inhibitor is found to be more inhibitive in H2SO4 than in HCl. Potentiodynamic polarization studies clearly justify the fact that this compound acted as a mixed inhibitor; but predominantly as a cathodic inhibitor. Hydrogen permeation studies and AC impedance measurements also confirm the improved performance of the compound in H2SO4. The adsorption of this compound on the mild steel surface follows Temkin’s adsorption isotherm. © 2015 University of Manchester and the authors.

Department of Chemistry

School of Advanced Sciences

Department of Manufacturing Engineering

Journal of Corrosion Science and Engineering

Studies on the reduction of mild steel corrosion and hydrogen permeation in acidic media

A high speed auto catalytic copper plating bath has been developed with Naficillin and Oxacillinantibiotics as accelerators for auto catalytic copper plating process. The acceleration effect of the compounds was determined by weight gain and electrochemical method. Both compounds improved the rate of deposition to considerable extent by adsorbing strongly on the steel surface. The adsorption of the accelerators was found to obey Langmuir adsorption isotherm. The quantum mechanical parameters further justified the impressive performance of accelerators through their effective adsorption on metal surface. © 2015, Sphinx Knowledge House. All Rights Reserved.

International Journal of ChemTech Research

Quantum mechanical investigations for auto catalytic copper plating process

The inhibitive action of lansoprazole (LPZ) on stainless steel 304 corrosion and permeation of hydrogen gas in 2N H3PO4 has been analyzed using weight loss, gasometric and electrochemical studies. The antibiotic seems to be more effective in reducing the dissolution of steel in 2N H3PO4. Potential-Current curves clearly indicated that the compound observes mixed mode of inhibition in 2N H3PO4. Hydrogen permeation and EIS measurements have confirmed that LPZ retards the corrosion of SS 304 effectively. The quantum mechanical studies validated the performance of the inhibitor molecule through its effective adsorption on steel surfaces using theoretical values of EHOMO, ELUMO, ΔE and dipole moment of inhibitor.

Der Pharmacia Lettre

Quantum mechanical and hydrogen permeation studies on the corrosion of stainless steel in acid medium using a novel inhibitor

In this paper an attempt has been made to fabricate hybrid aluminum metal matrix composite with a composition of 7wt% Silicon carbide and 3wt% Alumina using stir casting method. Morphology of the composite and reinforced particle distribution were studied by optical microscopy. The fabricated cast composites were carefully machined. The influence of machining parameters like cutting speed (s), feed rate (f) and depth of cut (d) on surface roughness (Ra) and cutting force (F) was investigated during experimentation. The optimal machining parameters have been found by Analysis of Variation (ANOVA). Results shows that when cutting speed increases surface roughness value decreases. Also it shows cutting force is directly proportional to cutting speed. Tool wear study has been made to find out tool wear during machining. It is concluded that tool wear is directly proportional to depth of cut. © Research India Publications.

International Journal of Applied Engineering Research

Fabrication of hybrid aluminium metal matrix composite using stir casting and analysis of machining parameters

Reduction of emission from the engine is a very difficult task these days since emission norms are becoming very strict on NOx, HC, CO etc. Special methods should be incorporated into the engines to have such emission standards. Here dual fuel diesel engine which use liquefied petroleum gas (LPG) and diesel fuel is suggested for better HC values in the emission. Proposed system uses a Fuzzy logic based controller to control the LPG flow into the engine and diesel as a pilot fuel which is only used to ignite the fuel. Also, EGR system will reduce the NOx content in the exhaust. Both EGR and LPG are controlled by a fuzzy closed loop control system.

Department of Automotive Engineering

Journal of Chemical and Pharmaceutical Sciences

Variation of gaseous fuel flow rate in a dual-fuel diesel engine using fuzzy logic

Composite journal bearings are becoming more popular now a day because they eliminate the possibility of seizure failure to the bearings. The major drawback of the metal bearings is the seizure failure. This failure is depends upon friction and lubricant used. To overcome this problem, the composite journal bearings are widely used by the industries. Hence, this paper presents the wear behaviour of Biaxial glass fiber reinforced with silicon carbide (SiC) and aluminium oxide (Al2O3) composite using sea water as lubricant. The test specimens are prepared and tested as per ASTM standard. The experiments are conducted by using a pin on disc wear tester. The results indicated that the wear resistance of the hybrid composite is better than that of the glass fiber. The fracture surface of composites shows the ductile tear ridges and cracked SiC and Al2O3 particles indicating both ductile and brittle fracture mechanism.In this study, different combinations based composite materials were comparatively investigated under actual loading, sliding distances and time frame to configure best suitable bearing materials. The hybrid composite are proven for clean functioning and shows remarkable life. © 2015 Sphinx Knowledge House. All rights reserved.

Sliding wear characteristics of Biaxial glass fiber with Epoxy/Al2O3/Sic hybrid composites for journal bearing liner using sea water lubricant

This paper presents annew approach to intelligent gear fault diagnosis in the field of rotating machinery condition monitoring.The prediction process is focused on the vibration signals collected from the accelerometer sensor mounted on the test rig for critical components monitoring. The pre-processed signals were decomposed into several signals containing one approximation and some details using Wavelet Packet Decomposition and thestatistical features were extracted from 4th level WP decomposition. The J48 algorithm which uses gain ratio to determine the splits of decision tree and to select the most important features were used for selecting the predominant features and selected features were fed as input for training and testing of Multi class Least Squares Support Vector Machines (MCLS-SVM) together with different kernel functions such as radial basis function kernel, linear-kernel, Polynomial-kernel functions for classification and predict the fault condition of the components and machines.Analysis is done for various LS-SVM models such as One against One and One against All. The proposed approach is applied to fault diagnosis of gear, and testing resultsshows the proposed approach can reliably recognise different fault categories and severities. © Research India Publications.

An Intelligent Gear Fault Diagnosis Based On Wavelet Packet Transform, Information Gain And Multiclass Least Squares Support Vector Machines

Fault diagnosis in bearings has been the subject of intensive research as bearings are critical component in many-rotating machinery applications and an unexpected failure of the bearing may cause substantial economic losses. Vibration analy-sis has been used as a predictive maintenance procedure and as a support for machinery maintenance decisions. In this study, we used Multiclass Support Vector Machines (MSVM) for classifica-tion task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. In data pre-processing step advance signal processing methods such as wavelet transform is used to extract the features which reveals the characteristics of the actual conditions of the bearing. To reduce the dimensionality of the feature SVM-Recursive Feature Elimination (SVM-RFE),Wrapper subset method, ReliefF meth-od and Principle component analysis (PCA). Moreover, investiga-tion is done performance of MSVM with mentioned different feature selection strategies as well as MSVM alone on basis of classification accuracy. To validate the proposed methodology, four kinds of running states are simulated with artificially creat-ed faults in bearing that is accommodated in the test rig. By com-paring classification accuracy along with computation timing the best scheme is selected for diagnostic prediction. © February 2015 IJENS.

Bearing fault diagnosis using multiclass support vector machine with efficient feature selection methods

Nanocatalysts based on Ag and AuNPs stabilized over chitosan functionalized GO exhibited excellent catalytic activity towards reduction of nitroarenes and dye degradation.