A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
International Journal of Data Analysis Techniques and Strategies
Application of mother wavelet functions for automatic gear and bearing fault diagnosis
Expert Systems with Applications: An International Journal
Fault diagnosis model based on Gaussian support vector classifier machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fault diagnosis of ball bearings using continuous wavelet transform
Applied Soft Computing
Wavelet basis functions in biomedical signal processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Estimating the risk of fire outbreaks in the natural environment
Data Mining and Knowledge Discovery
Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool
Expert Systems with Applications: An International Journal
Healthcare Data Mining: Predicting Hospital Length of Stay PHLOS
International Journal of Knowledge Discovery in Bioinformatics
Healthcare Data Mining: Predicting Hospital Length of Stay PHLOS
International Journal of Knowledge Discovery in Bioinformatics
Journal of Intelligent Manufacturing
Hi-index | 12.06 |
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. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). 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 SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.