Title: Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis
Authors: Saravanan Natarajan
Addresses: Department of Engineering, Mechanical and Industrial Engineering Section, Higher College of Technology, Ministry of Manpower, Muscat, Sultanate of Oman
Abstract: This paper discusses about the extraction of histogram features from the vibration signal of the different conditions of the gear box under investigation, and the application of machine learning method, support vector machine in machine condition monitoring and diagnostics. This paper aims at using classification methods for fault diagnosis of the gear box under investigation. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This work investigates the use histogram features and support vector machine for classification. The results show that the developed method can reliably diagnose different conditions of the gear box.
Keywords: gearbox faults; fault diagnosis; histogram features; support vector machines; SVM; gearboxes; vibration signal processing; machine learning; condition monitoring; feature extraction; classification.
International Journal of Systems, Control and Communications, 2017 Vol.8 No.1, pp.57 - 71
Received: 03 Jul 2015
Accepted: 14 Jul 2016
Published online: 12 Jan 2017 *