Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
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 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.