A Novel Feature Selection method for Fault Detection and Diagnosis of Control Valves
Publication Type:Journal Article
Source:International Journal of Computer Science Issues, Citeseer (2011)
Keywords:Artificial bee colony, Control Valves, Fault Detection and Diagnosis, Feature selection, naïve Bayes.
In this paper, a novel method for feature selection and its application to fault detection and Isolation (FDI) of control valves is presented. The proposed system uses an artificial bee colony (ABC) optimized minimum redundancy maximum relevance (mRMR) based feature selection method to identify the important features from the measured control valve parameters. The selected features are then given to a naïve Bayes classifier to detect nineteen different types of faults. The performance of the proposed feature selection system is compared to that of six other feature selection techniques and the proposed system is found to be superior.
Cite this Research Publication
Related Research Publications
- Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump
- Decision support system using artificial immune recognition system for fault classification of centrifugal pump
- Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features
- Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals
- An Improved role based trust management system based on Artificial bee Algorithms in wireless Networks,