Faculty Publications
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Publications by NITK Faculty
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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 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 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).Item Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal(Tech Science Press sale@techscience.com, 2017) Sharma, R.K.; Sugumaran, V.; Kumar, H.; Amarnath, M.Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. © Copyright 2017 Tech Science Press.Item Condition monitoring of single point cutting tools based on machine learning approach(International Institute of Acoustics and Vibrations P O Box 13 Auburn AL 36831, 2018) Gangadhar, N.; Kumar, H.; Narendranath, S.; Sugumaran, V.This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools. © 2018 International Institute of Acoustics and Vibrations. All Rights Reserved.Item 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.