A Study on the Classification Ability of Decision Tree and Support Vector Machine in Gearbox Fault Detection

Abstract

Gearbox is the only medium which balances the power and torque relations for the appropriate operating conditions, at very high speeds it controls the power output of the drive unit. Its application is wide in the field of automotive and industries. Condition monitoring of gearbox access the operating condition of the gearbox components such as gears and, bearings to take necessary condition based maintenance to avoid the machine downtime and operation losses. This paper identifies the suitable accelerometer position to acquire vibration signals for identification of gear faults using machine learning techniques. The study includes 2 fault class, 2 gear speeds (1st and 4th gear), 3 loading conditions and, 3 operating speeds each for 2 sensor locations. Features were collected for each class in both sensor location points from accelerometer. Statistical features were extracted and the classification efficiencies were calculated from both SVM and J48 Decision tree algorithm.