Publication Type:

Journal Article

Source:

Applied Mechanics & Materials, Volume 813/814, p.943-948 (2015)

URL:

http://eds.b.ebscohost.com/abstract?site=eds&scope=site&jrnl=16627482&AN=111329303&h=PqM47iGlPzshiOmHaTCnPcWg3lFXXOrrI6ZvYcSv7%2fJaFMkwWMaqPiOiTzGmBdOlTw1xqYMIahqOp1qZTv76pw%3d%3d&crl=c&resultLocal=ErrCrlNoResults&resultNs=Ehost&crlhashurl=login.aspx%3fdi

Abstract:

Gearbox plays a vital role in various fields in the industries. Failure of any component in the gearbox will lead to machine downtime. Vibration monitoring is the technique used for condition based maintenance of gearbox. This paper discusses the use of machine learning techniques for automating the fault diagnosis of automobile gearbox. Our experimental study monitors the vibration signals of actual automobile gearbox with simulated fault conditions in the gear and bearing. Statistical features are extracted and classified for identifying the faults using decision tree and Naïve Bayes technique. Comparison of the techniques for determining the classification accuracy is discussed.

Cite this Research Publication

P. Sundar, KN, V., Saimurugan, M., G. Kumare, P., and Sreenath, P. G., “Automobile Gearbox Fault Diagnosis using Naive Bayes and Decision Tree Algorithm.”, Applied Mechanics & Materials, vol. 813/814, pp. 943-948, 2015.