Pattern Recognition Letters
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Determination of sample size using power analysis and optimum bin size of histogram features
International Journal of Data Analysis Techniques and Strategies
Fuzzy lattice classifier and its application to bearing fault diagnosis
Applied Soft Computing
Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool
Expert Systems with Applications: An International Journal
A classifier fusion system for bearing fault diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing's nature of vibration reveals its condition and the features that show the nature are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Finding out good features that discriminate the different fault conditions of the bearing is often a problem. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper addresses the feature selection process using decision tree and uses kernel based neighborhood score multi-class support vector machine (MSVM) for classification. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race faults. The statistical features are extracted therefrom and classified successfully using MSVM. The results of MSVM are compared with and binary support vector machine (SVM).