A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal

Bearing is an important and necessary part of any big or small machinery and for proper working of machinery the bearing condition should be good. Hence, there is a requirement for continuous bearing monitoring. For the condition monitoring of bearings sound signal can be used. This paper uses sound signal for condition monitoring of roller bearing by naïve Bayes and Bayes net algorithms. The statistical features from the sound signals were extracted. Then features giving better results were selected using J48 decision tree algorithm. These selected features were classified using naïve Bayes and Bayes net algorithm. The classification results for both naïve Bayes and Bayes net algorithm for fault diagnosis of roller bearing using sound signals were compared and results were tabulated.