Global geometric similarity scheme for feature selection in fault diagnosis

Authors:
Chao Liu;Dongxiang Jiang;Wenguang Yang
Affiliations:
-;-;-
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

Citing 24
Cited 0

Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence

Pattern Recognition
Letters: ISOLLE: LLE with geodesic distance

Neurocomputing
Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

Expert Systems with Applications: An International Journal
Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network

Expert Systems with Applications: An International Journal
The use of features selection and nearest neighbors rule for faults diagnostic in induction motors

Engineering Applications of Artificial Intelligence
Dimensionality reduction techniques for blog visualization

Expert Systems with Applications: An International Journal
Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

Expert Systems with Applications: An International Journal
Bearing performance degradation assessment using locality preserving projections

Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Artificial neural network approach for locating internal faults in salient-pole synchronous generator

Expert Systems with Applications: An International Journal
Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine

Expert Systems with Applications: An International Journal
A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method

Expert Systems with Applications: An International Journal
Fuzzy criteria for feature selection

Fuzzy Sets and Systems
Feature selection based on sensitivity analysis of fuzzy ISODATA

Neurocomputing
Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks

Neurocomputing
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing
A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine

Expert Systems with Applications: An International Journal
An overview of statistical learning theory

IEEE Transactions on Neural Networks
Feature subset selection Filter-Wrapper based on low quality data

Expert Systems with Applications: An International Journal
Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS

Expert Systems with Applications: An International Journal
A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection

International Journal of Remote Sensing
Genetic algorithm-based heuristic for feature selection in credit risk assessment

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

This work presents a global geometric similarity scheme (GGSS) for feature selection in fault diagnosis, which is composed of global geometric model and similarity metric. The global geometric model is formed to construct connections between disjoint clusters in fault diagnosis. The similarity metric of the global geometric model is applied to filter feature subsets. To evaluate the performance of GGSS, fault data from wind turbine test rig is collected, and condition classification is carried out with classifiers established by Support Vector Machine (SVM) and General Regression Neural Network (GRNN). The classification results are compared with feature ranking methods and feature wrapper approaches. GGSS achieves higher classification accuracy than the feature ranking methods, and better time efficiency than the feature wrapper approaches. The hybrid scheme, GGSS with wrapper, obtains optimal classification accuracy and time efficiency. The proposed scheme can be applied in feature selection to get better accuracy and efficiency in condition classification of fault diagnosis.