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 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 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 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.