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Volume-4 Issue-4

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Volume-4 Issue-4, September 2015, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



Madhuri Pradhan, R. K. Bhoi, S. Rana

Paper Title:

Optimization of Operating Parameters for EDM Process based on the Taguchi Method

Abstract:   In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining, particularly for operating unconventional machining procedure like EDM. A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of their numerous and diverse range. Machining parameters tables provided by the machine tool builder cannot meet the operators’ requirements, since for an arbitrary desired machining time for a particular job, they do not provide the optimal machining conditions. An approach to determine parameters setting is proposed. Based on the Taguchi parameter design method and the analysis of variance, the significant factors affecting the machining performance such as crater diameter,recsat layer,grain size,haz for a hole machined by EDM process, are determined.

   complexity, Optimization, EDM, significant factors.


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19.        Chang,S.H,Hwang,J.R,Doong,J.L,(2010) ‘Optimization of the injection moulding process of short glass fiber reinforced polycarbonate composites using grey relational analysis, ” Journal of Material Processing and Technology, Vol. 132, pp 340-344.

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B. Rajagopal, S. Singaravelu

Paper Title:

Detection of Air gap Eccentricity Fault of Three Phase Induction Motor by Fast Fourier Transform Using ARM Microcontroller

Abstract:    Induction machines are the backbones of many industrial processes due to its robustness and reliability. Online fault diagnostics of induction motor is important, and its real function is to attempt to recognize the development of faults at an early stage, which are highly useful preventive rescue especially in high power applications. Among various faults occurred in induction motors, eccentricity faults are of significant importance as they produce secondary effects that can lead to a major fault to a motor. Using different signal processing and mathematics techniques, the stator current signals of a motor can be analyzed, interpreted and faults inside the motor can be identified. It is observed that the fault frequencies for different faults of induction motor are unique. This paper investigates on detection of air gap eccentricity fault in three phase cage induction using modulated motor stator current.. MCSA (Motor Current Signature Analysis) technique using FFT (Fast Fourier Transform) approach is utilized in this research work to identify air gap eccentricity fault of induction motor under different loading conditions. Hence, in this paper RISC (Reduced Instruction Set Computing) based ARM (Advanced RISC Machine) architecture controller (LPC2148 from NXP) for current signature analysis is developed to analyze the air gap eccentricity fault. An experimental setup, using the ARM based data acquisition board and PC based analysis software is also developed and results are given for air gap eccentricity fault.

 Induction motor, MCSA, FFT, Air gap eccentricity fault, ARM controller


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13.    Neelam Mehla, and Ratan Dahiya,2007, “An approach of condition monitoring of  induction motor using MCSA,” International Journal of systems applications Engineering & development, volume 1, issue 1, pp. 13-17.
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15.    Nandi. S.,Bharadwaj, R.M., and Toliyat. H. A., “Performance analysis of a three phase  induction motor under incipient mixed eccentricity condition,” IEEE, Transactions on Energy conversion, vol. 17, no.3.,2002, pp. 392- 399.

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17.    Mohamed EI Hachemi Benbouzid, Michelle Vieira and Celine Theys, “Induction Motors Faults Detection and Localization Using stator current Advanced signal processing Techniques,” IEEE, Transactions on Power Electronics, Vol.14, No.1, January 1999, pp.14- 21.G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),”    in Plastics, 2nd ed. vol. 3, J. Peters, Ed.  New York: McGraw-Hill, 1964, pp. 15–64.






Swapnil B. Mohod, Vilas N. Ghate

Paper Title:

Automated Classification and Detection of Power Quality Disturbances Using RBF Fault Classifier

Abstract:     the proliferation of power electronic devices in a modern industrial control pronounced more power quality disturbances. There is an urgent need of technique which automatically classifies and detects power quality disturbances. In this paper authors developed an online radial-basis-function NN-based detection technique. In proposed scheme simple statistical parameters described which are used as input noise signals to classify vital conditions of power system like sag, swell of Induction motor, arc load, short circuit of welding machine, phase to earth fault and healthy condition. Detailed design procedure for RBF based classifier is presented for which experimental data of one HP, single phase, 50 Hz squirrel cage Induction motor, Welding machine to generate actual arcing load, Advantech data acquisition system is used. A Wavelet Transform Technique is applied to extract features from monitored data. By principle component analysis and sensitivity analysis dimension reduction is also achieved which classify the six types of PQ disturbances.  

  Power Quality, Wavelet transform, RBF, PCA


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2.       Hamid EY, Kawasaki ZI. “Wavelet-based data compression of power system disturbances using the minimum description length criterion,” IEEE Transaction on  Power Delivery 2002;17(2):460–6

3.       S. Santoso, E. J. Powers, W. Mack Grady, and P. Hofmann, “Power quality assessment via wavelet transform analysis,” IEEE Transaction on Power Delivery, vol. 11, no. 2, pp. 924–930, April 1996

4.       W.A. Wilkinson, M.D. Cox “Discrete wavelet analysis of power system transients,” IEEE Transaction on  Power System 11 (1996) 2038–2044

5.       Flavio B. Costa, “Boundary Wavelet Coefficients for Real-Time Detection of Transients Induced by Faults and Power-Quality Disturbances,” IEEE Transactions on Power Delivery, Vol. 29, No. 6, December 2014

6.       Milan Biswal, P. K. Dash “Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree,” IEEE Transaction On Industrial  Informatics, Vol. 9, No. 4, November 2013, 1819

7.       Huseyin Eris_ti, Ozal Yıldırım, Belkıs Eris_ti, Yakup Demir, “Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine,” Electrical Power and Energy Systems 61, 2014 553–562
8.       Raj Kumar, Bhim Singh, D. T. Shahani, Ambrish Chandra, Kamal Al-Haddad, “Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree,” IEEE Transactions on Industry Applications, Vol. 51, no. 2, March/April 2015
9.       Rodríguez, J.A.Aguado, F.Martín, J.J.López, F.Mu˜noz, J.E.Ruiz, “Rule-based classification of power quality disturbances using S-transform”, Electric Power Systems Research 86, 2012 113– 121.

10.    Biswal, M. Biswal, S. Mishra, R. Jalaja, “Automatic Classification of Power Quality Events Using Balanced Neural Tree,” IEEE Transaction On Industrial Electronics, Vol. 61, No. 1, January 2014, 521B

11.    Zhigang Liu, Qiaoge Zhang, “An Approach to Recognize the Transient Disturbances With Spectral Kurtosis,”  IEEE Transaction On Instrumentation and Measurement, Vol. 63, No. 1, January 2014

12.    Prakash K. Ray, Soumya R. Mohanty, Nand Kishor,  João P. S. Catalão, “Optimal Feature and Decision Tree-Based Classification of Power Quality Disturbances in Distributed Generation Systems,” IEEE Transactions On Sustainable Energy, Vol. 5, No. 1, January 2014.
13.    Raj Kumar, Bhim Singh, D. T. Shahani, Ambrish Chandra, Kamal Al-Haddad, “Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree,” IEEE Transactions on Industry Applications, Vol. 51, no. 2, March/April 2015.
14.    Cheng-I Chen,Yeong-Chin Chen, “A Neural Network Based  Data Driven Non –Linear Model On Time And Frequency Domain Voltage –Current Characterization For Power Quality Study,” IEEE Transactions on Power Delivery, TPWRD, 2015

15.    Liu Hua, Wang Yuguo, Zhao Wei,“ Power Quality Disturbances Detection and Classification Using Complex Wavelet Transformation and Artificial Neural Network Control,” Chinese Digital Object Identifier, pp, 208 – 212, 2007

16.    ElangoM.K, Kumar A.N. Duraiswamy K, “Identification of power quality disturbances using Artificial Neural Networks,” IEEE Conference on Power and EnergySystems, pp. 1 – 6 , 2011

17.    Yuliang Liu ,  Li Zhao ,  Shigang Cui ,  Qingguo Meng , Hongda Chen, “Quantum-behaved particle swarm optimization-ANN based identification method for typical power quality disturbance,” China,pp. 1103-1108

18.    Nantian Huang, DianguoXu , XiaoshengLiu, LinLin, “Power quality disturbances classification based on S-transform and probabilistic neural network,” Elsevier Neurocomputing  (2012) 12–23

19.    Wael R., Anis Ibrahim and Medhat M. Morcos, “Artificial Intelligence and Advanced Mathematical Tools for Power Quality  Applications: A Survey,” IEEE transactions on power delivery, VOL. 17(2), pp. 668-673, APRIL 2002

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21.    Aneesh C, Sachin Kumar, Hisham P M, K P Soman, “Performance comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the classification of power quality disturbances using Support Vector Machine,” International Conference on Information and Communication Technologies (ICICT 2014)

22.    Guo-sheng Hu, Feng-feng Zhu, You-zhi Zhang , “Power Quality Faint Disturbance Identification Using Wavelet Packet Energy Entropy and Weighted Support Vector Machines,” Third International Conference on Natural Computation,  Vol. 5, pp. 649 – 653, 2007






Vivek Kumar, Rajeshwar Prasad Singh, Vikash Kumar

Paper Title:

A Study of Technology Used and Comparison Between Traditional and Hf120 (Advanced Aero Engine)

Abstract:      Paper includes detailed study of the HF120 turbofan engine, which is the first product from GE Honda Aero Engines. This paper is to showcase state of art technology involved in gas turbine engines and to bring out various aspects and future trends in field of propulsion technology. Paper also includes development in gas turbine engine that have helped to achieve great fuel efficiency and less noise, increased power, thrust and also advancements made in the field of materials have contributed in a major way in gas turbine in accordance to the future trends that have come up in recent years. The paper reviews the evolutionary process that has taken place over the years with reference to the different design concepts used for aero engines. At General Electric, the official corporate slogan is “Imagination at work.” At Honda, it’s “The power of dreams.” Two of the world's most respected names in propulsion have come together to design and manufacture engines for the next generation of very light jets. The joint venture, known as GE Honda Aero Engines, combines the strengths of two industry leaders recognized for delivering high performance and reliable engines. The HF120 is an advanced 2000lb thrust class turbofan propulsion system. It incorporates a development philosophy and operational features consistent with Honda's tradition of Innovation and GE's rigorous design and testing standards.

  HF120, Honda's, GE's, design and testing, “Imagination at work.”, GE Honda, 2000lb


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15.     National Partnership”, Dept. of Energy Workshop Next Generation Gas Turbine

16.     Power Systems, Austin, TX Feb. 9-10, 1999.

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T. Romen Singh, O. Imocha Singh

Paper Title:

Image Resizing with Enhancement Technique on DCT Domain

Abstract:       This paper presents an image resizing technique with enhancement process so as to get an enhanced resized image. This technique is applied on single DCT domain and it is based on the energy compaction and de-correlation property of DCT. Resizing is based on energy compaction while enhancement is based on de-correlation property of CDT. In this technique, resizing is implemented on arbitrary ratio of dimension of the resized image. Enhancement technique is applied by rescaling the AC and DC values of the DCT matrix of the input image.  Since it is applied on single DCT domain there is no blocking artefact like block domain. The performance of this technique is compared with other related techniques using PSNR, RMSE and CCC and found outperform.

 Image Resizing, Arbitrary Ratio,  DCT, Enhancement, Power-Law.


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5.        S.A. Martucci, “Image resizing in the discrete cosine trans- form domain,” Proc. of 1995 IEEE Inter. Conf. Image Pro- cessing, pp. 244–247, Washington D.C., 1995.

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13.     T.R Singh, S. Roy, Kh. M Singh, “Global DCT Domain Power-Law Transformation in Image Enhancement Technique,” Proceedings of International Symposium on Computational and Business Intelligence (ISCBI 2013),India,978-0-7695-5066-4/13 c 2013 IEEE, DOI 10.1109/ISCBI.2013.61, pp. 247-253, August 24-26, (2013).






Sampath Kumar B, Varsha N, Zaara FK

Paper Title:

Recognition of Medicinal Leaves using PCA & SVM

Abstract:   In pharmacological science and bhaishaja kalpana, the recognition of plant leaves(leaf) is very important to make medicine. A Recognizing plant leaf has so far been an important and difficult task. In this paper leaf recognition system using the support vector machine as a classifier is proposed. The leaf recognition system consists of image acquisition, preprocessing, feature extraction and classification. The preprocessing involves  a typical image processing conversion  such as transforming color images to gray scale image then binary , smoothing and to contour image etc. In the feature extraction involves  geometrical features are extracted from leaf images such as diameter, length, area, and perimeter etc. Total 10 digital morphological features (DMF) including geometrical features. These 10 features are  orthogonalized into five principal variables using principal component analysis (PCA). These features are given as input vector to the support vector machine (SVM) for classifying leaves.

  Digital Morphological Feature(DMF); leaves(Leeaf) Recognition; Principle component analysis (PCA); Support Vector Machine (SVM)


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5.       Ji- Xiang  Du, De-Shuang Huang, Xiao Feng Wang, and Xiao Gu, “Computer-aided plant species identification (capsi) based on leaf shape matching technique,” Transactions of the Institute of Measurement and Control,vol.28,2006,pp.275-284.

6.       J.-X.  Du,  X.-F.  Wang,  and G.-J. Zhang, “Leaf shape based plant species recognition,” Applied Mathematics and Computation, 2007,vol.185

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8.       N. Cristianini, and J. Shawe-Taylor, “An Introduction to Support Vector Machines”, Cambridge University Press, 2000.






Parag R. Nadkarni, Padmakar J. Salunke, Trupti Narkhede

Paper Title:

Analytical Investigation of Cable Stayed Bridge Using Various Parameters

Abstract:   In this paper, analysis of 240 m long fan type cable stayed bridge having single plane of cables is carried out with the help of software facilities. Effects of various parameters such as stiffness of deck and pylon and number of cables on the behaviour of cable stayed bridge were observed. To save time in modelling of bridges manually, a programming tool has been developed in excel software with the help of visual basic macro for the purpose of parametric study of Cable stayed bridge. With the help of this tool, number of models of cable-stayed bridge can be automatically generated in software SAP-2000. From the analysis of number of models, comparison of bending moments at critical locations in the structure is done.

   Cable stayed bridge, parametric study, SAP2000, Interactive Database, VB program, Form control, Class 70R, Class A


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4.        Hany W. George, 1999, ”Influence of deck material on response of cable-stayed bridges to live loads”, Journal of Bridge Engineering, Vol.4, No.2, pp.136-142.

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