Fault diagnosis of the roller bearings as pattern classification problem has three main steps: feature extraction, feature selection and classification. Wavelets have been widely used for feature extraction from vibration signals. Identifying a suitable wavelet for a given application is a challenging task in the whole process. This paper investigates the use of decision tree for selecting apt wavelet for fault diagnosis of roller bearings with discrete wavelet transform features. The study is done on vibration signals of roller bearings from different fault conditions. The faults considered in this study are bearings with inner race fault, bearings with outer race fault and bearings with both of them. The decision tree has been used for feature selection as well as for classification. Many commonly used wavelets families have been considered in this study and their classification accuracies were compared.
V. Sugumaran and Dr. K. I. Ramachandran, “Wavelet Selection Using Decision Tree for Fault Diagnosis of Roller Bearings”, International Journal of Applied Engineering Research, vol. 4, pp. 201-225, 2009.