Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item High-speed and parallel approach for decoding of binary BCH codes with application to Flash memory devices(2012) Kumar, H.; Sripati, U.; Rajesh Shetty, K.In this article, we propose a high-speed decoding algorithm for binary BCH codes that can correct up to 7bits in error. Evaluation of the error-locator polynomial is the most complicated and time-consuming step in the decoding of a BCH code. We have derived equations for specifying the coefficients of the error-locator polynomial, which can form the basis for the development of a parallel architecture for the decoder. This approach has the advantage that all the coefficients of the error locator polynomial are computed in parallel (in one step). The roots of error-locator polynomial can be obtained by Chien's search and inverting these roots gives the error locations. This algorithm can be employed in any application where high-speed decoding of data encoded by a binary BCH code is required. One important application is in Flash memories where data integrity is preserved using a long, high-rate binary BCH code. We have synthesized generator polynomials for binary BCH codes (error-correcting capability, s) that can be employed in Flash memory devices to improve the integrity of information storage. The proposed decoding algorithm can be used as an efficient, high-speed decoder in this important application. © 2012 Taylor & Francis.Item Enhancing the error-correcting capability of imai-kamiyanagi codes for data storage systems by adopting iterative decoding using a parity check tree(2012) Kumar, H.; Sripati, U.; Rajesh Shetty, K.; Shankarananda, B.A novel low-complexity, soft decision technique which allows the decoding of distance-5 double error-correcting Imai-Kamiyanagi codes by using a parity check tree associated with the Tanner graph is proposed. These codes have been applied to memory subsystems and digital storage devices in order to achieve efficient and reliable data processing and storage. For the AWGN channel, gains in excess of 1.5 dB at reasonable bit error rates with respect to conventional hard decision decoding are demonstrated for the (46, 32), (81, 64), and (148, 128) shortened Imai-Kamiyanagi codes. Copyright © 2012 by the IETE.Item Fault diagnosis of helical gear box using decision tree through vibration signals(RAMS Consultants, 2013) Sugumaran, V.; Jain, D.; Amarnath, M.; Kumar, H.This paper uses vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using J48 decision tree algorithm. The paper also discusses the effect of various parameters on classification accuracy. © RAMS Consultants.Item Fault diagnosis of bearings through vibration signal using Bayes classifiers(Inderscience Publishers, 2014) Kumar, H.; Ranjit Kumar, T.A.; Amarnath, M.; Sugumaran, V.Bearings are an inevitable part in industrial machineries, which is subjected to wear and tear. Breakdown of such crucial components incur heavy losses. This study concerns with fault diagnosis through machine learning approach of bearing using vibration signals of bearings in good and simulated faulty conditions. The vibration data was acquired from bearings using accelerometer under different operating conditions. Vibration signals of a bearing contain the dynamic information about its operating condition. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The decision tree has been formulated using J48 algorithm. The selected features were then used for classification using Bayes classifiers namely, Naïve Bayes and Bayes net. The paper also discusses the effect of various parameters on classification accuracy. © 2014 Inderscience Enterprises Ltd.Item Fault diagnosis of deep groove ball bearing through discrete wavelet features using support vector machine(COMADEM International rajbknrao@btinternet.com, 2014) Vernekar, K.; Kumar, H.; Gangadharan, K.V.Bearings are the most important and frequently used machine components in most of the rotating machinery. In industry, breakdown of such crucial components causes heavy losses. So prevention of failure of such components is very essential. This paper presents an online fault detection of a bearing used in an internal combustion engine through machine learning approach using vibration signals of bearing in healthy and simulated faulty conditions. Vibration signals are acquired from bearing in healthy as well as different simulated fault conditions of bearing. The Discrete Wavelet Transform (DWT) features were extracted from vibration signals using MATLAB program. Decision tree technique (J48 algorithm) has been used for important feature selection out of extracted DWT features. Support vector machine is being used as a classifier and obtained results found with classification accuracy of 98.67%.The advantage of machine learning technique for fault diagnosis over conventional vibration analysis approach has demonstrated in this paper.Item Fault Detection of Gear Using Spectrum and Cepstrum Analysis(Springer Nature, 2015) Vernekar, K.; Kumar, H.; Gangadharan, K.V.This paper presents an experimental investigation on damage detection of internal combustion (IC) engine gear box using conventional vibration spectrum and cepstrum analysis. Experiment was carried out on two stroke internal combustion engine gearbox without considering the combustion. Vibration signals were collected for healthy as well as defective gear condition. The signals were analysed in time domain, frequency domain and cepstrum plots for fault detection. An experimental result demonstrates the dynamic behaviour in frequency domain, which is dominated by gear mesh frequency (GMF) and its harmonics.Based on the experimental results obtained, spectrum and cepstrum analysis can be effectively used for fault prediction of machine components. © Printed in India.Item A neural network based method for estimation of heat generation from a teflon cylinder(Global Digital Central, 2016) Kumar, S.; Kumar, H.; Gnanasekaran, N.The paper reports the estimation of volumetric heat generation (qv) from a Teflon cylinder. An aluminum heater, which acts as a heat source, is placed at the center of the Teflon cylinder. The problem under consideration is modeled as a three dimensional steady state conjugate heat transfer from the Teflon cylinder. The model is created and simulations are performed using ANSYS FLUENT to obtain temperature data for the known heat generation qv. The numerical model developed using ANSYS acts as a forward model. The inverse model used in this work is Artificial Neural Network (ANN). Estimation of heat generation is carried out by minimizing the error between the simulated temperature and the experimental/surrogated temperature. The efficacy of the ANN method is explored for the estimation of unknown heat generation as both forward model and inverse model. The concept of Asymptotic Computational Fluid Dynamics (ACFD) is introduced as a fast forward model which is obtained by performing CFD simulations. The unknown heat generation is estimated for the surrogated data using ANN. In order to mimic experiments, noise is added to the surrogated data and estimation of heat generation is also carried out for the perturbed/noise added temperature data. © 2016, Global Digital Central. All rights reserved.Item Fault diagnosis of bearings through sound signal using statistical features and bayes classifier(Krishtel eMaging Solutions Pvt. Ltd, 2016) Kumar, H.; Sugumaran, V.; Amarnath, M.Bearing is one of important rotary elements used in almost all machinery. This study concerns with fault diagnosis through machine learning approach using acoustic signals (sound) of bearings in good and simulated faulty conditions. The acoustic data was acquired from near field area of bearings using microphone under different operating conditions. Acoustic signals of a bearing contain the dynamic information about its operating condition. Abundant literature reported suitability of vibration signals for fault diagnosis applications, however, not much using sound signals for diagnosis applications. Also, transducers used for measurement of sound are less costly than transducers used for vibration measurement. Hence, usage of sound signals for fault diagnosis applications of bearings found beneficial. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using Bayes classifier. The paper also discusses the effect of various parameters on classification accuracy. © KRISHTEL eMAGING SOLUTIONS PVT. LTD.Item An investigation on characteristics and free vibration analysis of laminated chopped glass fiber reinforced polyester resin composite(Asian Research Publishing Network arpn@arpnjournals.com, 2016) Allien, V.; Kumar, H.; Desai, V.In this paper material characterization and free vibration analysis of polyester resin based two, four and six layers chopped strand mat (CSM 450g/m2 specific weight) glass fiber reinforced with (CGRP) composite materials has been determined. In material characterization the tensile, flexural, impact, inter-laminar shear strength, fracture toughness has been evaluated. The results have revealed that, the four layer CGRP composite material has high impact, inter laminar shear strength and fracture toughness compared to two and six layers composite material. Free vibration analysis was carried out to determine the natural frequency of the CGRP composite materials theoretically and numerically (FEA). The result obtained from free vibration analysis indicated that natural frequency of six layers CGRP composite material is more than two and four layers CGRP composite material. © 2006-2016 Asian Research Publishing Network (ARPN).Item Fault diagnosis studies of face milling cutter using machine learning approach(Multi-Science Publishing Co. Ltd claims@sagepub.com, 2016) Madhusudana, C.K.; Budati, S.; Gangadhar, N.; Kumar, H.; Narendranath, S.Successful automation of a machining process system requires an effective and efficient tool condition monitoring system to ensure high productivity, products of desired dimensions, and long machine tool life. As such the component's processing quality and increased system reliability will be guaranteed. This paper presents a classification of healthy and faulty conditions of the face milling tool by using the Naive Bayes technique. A set of descriptive statistical parameters is extracted from the vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features are fed to the Naive Bayes algorithm. The output of the algorithm is used to study and classify the milling tool condition and it is found that the Naive Bayes model is able to give 96.9% classification accuracy. Also the performances of the different classifiers are compared. Based on the results obtained, the Naive Bayes technique can be recommended for online monitoring and fault diagnosis of the face milling tool. © 2016 The Author(s).