The nature of statistical learning theory
The nature of statistical learning theory
Fault diagnosis in power plant using neural networks
Information Sciences: an International Journal - Intelligent manufacturing and fault diagnosis (II). Soft computing approaches to fault diagnosis
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Bayes classification based on minimum bounding spheres
Neurocomputing
A novel fuzzy compensation multi-class support vector machine
Applied Intelligence
Sphere-structured support vector machines for multi-class pattern recognition
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Density weighted support vector data description
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper presents a novel fuzzy classifier for fault diagnosis of rolling machinery based on support vector data description (SVDD) and kernel possibilistic c-means clustering. The proposed method considers the effect of negative samples, which should be rejected by positive class, to the SVDD classifier. Firstly, we compute weights of training samples to the given positive class using the kernel PCM algorithm. Then according to weights, we select some meaning samples to construct a new training set, and train these samples with the proposed weighted SVDD algorithm. The proposed method is applied to the fault diagnosis of rolling element bearings, and experimental results show that the proposed method can reliably separate different fault conditions, and reduce the effect of outliers to classification results.