A comparative study of naïve Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal. (5th February 2015)

• Record Type: Journal Article
• Authors: Sharma, Rahul Kumar; Sugumaran, V.; Kumar, Hemantha; Amarnath, M.
• Is Part Of: International journal of decision support systems. Volume 1:Number 1(2015)
• Page Start: 115
• Page End: 129
• Publication Date: 2015-02-05
• Subjects: naïve Bayes -- NB -- Bayes net -- BN -- machine learning approach -- fault diagnosis -- bearing -- sound signal -- decision tree algorithm -- statistical features -- decision-making -- condition monitoring -- decision support systems; Decision making -- Data processing -- Periodicals; 658.40302856312
• Languages: English
• ISSNs: 2050-6988

Abstract

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.