Gearbox is the core component in any automotive/industrial application and it consists of gears and gear trains to vary the speed and torque of the machine. In order to reduce the machine breakdown cost and to increase the service life it is vital to know its operating conditions frequently to find the point of defect. The vibration signals are used to extract statistical features for 3 different classes namely Gearbox with Good gear, Gear Tooth breakage and Gear Face wear. The features were collected according to the experimental conditions with 3 fault classes, 3 speeds and 1 load condition with total of 9 testing conditions. The prominent statistical features were selected using decision tree algorithm. The set of IF-Then rule was generated and coded in LabVIEW for automated machine fault diagnosis.
P. T Kumar, Jasti, A., Dr. Saimurugan M., and Ramachandran, K. I., “Vibration Based Fault Diagnosis of Automobile Gearbox Using Soft Computing Techniques”, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, New York, NY, USA, 2014.