A case study on classification of features by fast single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box

This paper deals with the application of fast single-shot multiclass proximal support vector machine for fault diagnosis of a gear box consisting of twenty four classes. The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing multiclass proximal support vector machine. The efficiency and time consumption in classifying the twenty four classes all-at-once is reported.