Neural computing: theory and practice
Neural computing: theory and practice
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
C4.5: programs for machine learning
C4.5: programs for machine learning
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Improving Text Classification using Local Latent Semantic Indexing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Special issue on e-maintenance
Computers in Industry - Special issue: E-maintenance
Computers in Industry - Special issue: E-maintenance
Development of an e-maintenance system integrating advanced techniques
Computers in Industry - Special issue: E-maintenance
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Inference of power plant quake-proof information based on interactive data mining approach
Advanced Engineering Informatics
Performance of KNN and SVM classifiers on full word Arabic articles
Advanced Engineering Informatics
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Computers & Mathematics with Applications
Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments
International Journal of Computer Integrated Manufacturing
Real-time turbine maintenance system
Expert Systems with Applications: An International Journal
Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Advanced Engineering Informatics
Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring
Expert Systems with Applications: An International Journal
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
Robotics and Computer-Integrated Manufacturing
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Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance.