Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Weighted Nearest Mean Classifier for Sparse Subspaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonnegative features of spectro-temporal sounds for classification
Pattern Recognition Letters
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Genetically programmed-based artificial features extraction applied to fault detection
Engineering Applications of Artificial Intelligence
Use of particle swarm optimization for machinery fault detection
Engineering Applications of Artificial Intelligence
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An intelligent fault diagnosis system for newly assembled transmission
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A novel feature extraction and selection scheme was proposed for hybrid fault diagnosis of gearbox based on S transform, non-negative matrix factorization (NMF), mutual information and multi-objective evolutionary algorithms. Time-frequency distributions of vibration signals, acquired from gearbox with different fault states, were obtained by S transform. Then non-negative matrix factorization (NMF) was employed to extract features from the time-frequency representations. Furthermore, a two stage feature selection approach combining filter and wrapper techniques based on mutual information and non-dominated sorting genetic algorithms II (NSGA-II) was presented to get a more compact feature subset for accurate classification of hybrid faults of gearbox. Eight fault states, including gear defects, bearing defects and combination of gear and bearing defects, were simulated on a single-stage gearbox to evaluated the proposed feature extraction and selection scheme. Four different classifiers were employed to incorporate with the presented techniques for classification. Performances of four classifiers with different feature subsets were compared. Results of the experiments have revealed that the proposed feature extraction and selection scheme demonstrate to be an effective and efficient tool for hybrid fault diagnosis of gearbox.