Fault diagnosis of deep groove ball bearing through discrete wavelet features using support vector machine

Abstract

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.