Title: A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal
Authors: Rahul Kumar Sharma; V. Sugumaran; Hemantha Kumar; M. Amarnath
Addresses: Department of Mechanical Engineering, School of Mechanical and Building Sciences, VIT University, Vandalur – Kelambakkam Road, Chennai – 600127, Tamil Nadu, India ' Department of Mechanical Engineering, School of Mechanical and Building Sciences, VIT University, Vandalur – Kelambakkam Road, Chennai – 600127, Tamil Nadu, India ' Department of Mechanical Engineering, National Institute of Technology Karnataka, Srinivasanagar, Surathkal, Mangalore-575025 Karnataka, India ' Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur Dumna Airport Road, P.O. Khamaria, Jabalpur – 482 005, Madhya Pradesh, India
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.
Keywords: naive Bayes; Bayes net; machine learning approach; fault diagnosis; roller bearings; sound signals; decision tree; statistical features; decision making; condition monitoring; decision support systems; DSS; classification; acoustic signals.
International Journal of Decision Support Systems, 2015 Vol.1 No.1, pp.115 - 129
Received: 16 Aug 2013
Accepted: 04 May 2014
Published online: 02 Feb 2015 *