Title: Fault diagnosis of bearings through vibration signal using Bayes classifiers

Authors: Hemantha Kumar; T.A. Ranjit Kumar; M. Amarnath; V. Sugumaran

Addresses: Department of Mechanical Engineering, National Institute of Technology Karanataka, Surathkal, P.O. Srinivasanagar, Mangalore – 575 025, Karanataka, India ' Department of Mechanical Engineering, Birla Institute of Technology and Science – Pilani, K.K. Birla Goa Campus, NH-17 Bypass, Zuarinagar, Goa-403726, India ' Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O. Khamaria, Jabalpur – 482 005, Madyapradesh, India ' VIT University, Chennai Campus, Vandalur-kelambakam Road, Chennai-600048, India

Abstract: 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.

Keywords: bearing faults; bearings; fault diagnosis; decision tree; Naive Bayes; Bayes net; feature selection; machine learning; vibration signals; Bayes classifiers; simulation; classification accuracy.

DOI: 10.1504/IJCAET.2014.058002

International Journal of Computer Aided Engineering and Technology, 2014 Vol.6 No.1, pp.14 - 28

Published online: 17 Jun 2014 *

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