Faculty Publications

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Author: search.filters.author.Vernekar, K.Has files: No

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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.
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    Fault diagnosis of single point cutting tool through discrete wavelet features of vibration signals using decision tree technique and multilayer perceptron
    (Krishtel eMaging Solutions Pvt. Ltd, 2017) Gangadhar, N.; Vernekar, K.; Kumar, H.; Narendranath, S.
    Tool condition monitoring in machining plays a crucial role in modern manufacturing systems, finding state of the tool wear in early with the help of condition monitoring system will reduce downtime and excessive power drawing while machining. Vibration analysis of mechanical systems can be used to identify the tool condition to distinguish good or worn tool. In this study, vibration signals were acquired during turning operation, fault diagnosis using machine learning techniques has been carried out with new and different type of worn-out tool inserts. Discrete Wavelet Features (DWT) were extracted from acquired vibration signal for various cutting tool conditions using MATLAB. Most significant features were selected out of extracted discrete wavelet features using decision tree technique (J48 algorithm). Multilayer perceptron has been used as a classifier, selected features were given as input for the classifier. The classification accuracy with multilayer perceptron was found to be 96%. © KRISHTEL eMAGING SOLUTIONS PVT. LTD.
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    Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm
    (Springer India sanjiv.goswami@springer.co.in, 2017) Vernekar, K.; Kumar, H.; Gangadharan, K.V.
    This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis. © 2017, Indian Academy of Sciences.
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    Engine gearbox fault diagnosis using machine learning approach
    (Emerald Group Publishing Ltd. Howard House Wagon Lane, Bingley BD16 1WA, 2018) Vernekar, K.; Kumar, H.; Gangadharan, K.V.
    Purpose: Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach: This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings: The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value: This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques. © 2018, Emerald Publishing Limited.