The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Independent component analysis: algorithms and applications
Neural Networks
Kernel independent component analysis
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Expert Systems with Applications: An International Journal
Development of smart sensors system for machine fault diagnosis
Expert Systems with Applications: An International Journal
Bearing Diagnosis Using Time-Domain Features and Decision Tree
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Car assembly line fault diagnosis based on robust wavelet SVC and PSO
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Fault diagnosis model based on Gaussian support vector classifier machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine
Expert Systems with Applications: An International Journal
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Forensics and Security
Information Sciences: 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
Application of multiclass support vector machines for fault diagnosis of field air defense gun
Expert Systems with Applications: An International Journal
Dynamic theorem proving algorithm for consistency-based diagnosis
Expert Systems with Applications: An International Journal
A novel fault diagnosis system using pattern classification on kernel FDA subspace
Expert Systems with Applications: An International Journal
Machine health prognostics using survival probability and support vector machine
Expert Systems with Applications: An International Journal
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Intelligent prognostics for battery health monitoring based on sample entropy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent fault diagnosis of rotating machinery using infrared thermal image
Expert Systems with Applications: An International Journal
Support vector machine classifier for diagnosis in electrical machines: Application to broken bar
Expert Systems with Applications: An International Journal
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Multi-sensor data fusion using support vector machine for motor fault detection
Information Sciences: an International Journal
Hi-index | 12.08 |
Recently, principal components analysis (PCA) and independent components analysis (ICA) was introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively. In this paper, the feasibility of using nonlinear feature extraction is studied and it is applied in support vector machines (SVMs) to classify the faults of induction motor. In nonlinear feature extraction, we employed the PCA and ICA procedure and adopted the kernel trick to nonlinearly map the data into a feature space. A strategy of multi-class SVM-based classification is applied to perform the faults diagnosis. The performance of classification process due to various feature extraction method and the choice of kernel function is presented and compared to show the excellent of classification process.