Faculty Publications

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Author: search.filters.author.Vernekar, K.Subject: search.filters.subject.Internal combustion engines

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    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.
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    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.