A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Neural Computation
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared.