Fault diagnosis of bearings through vibration signal using Bayes classifiers

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.