A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Digital Image Processing
Journal of Parallel and Distributed Computing
Expert Systems with Applications: An International Journal
Extended Relief Algorithms in Instance-Based Feature Filtering
ALPIT '07 Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)
Application of an intelligent classification method to mechanical fault diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)
Expert Systems with Applications: An International Journal
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This study presents a new intelligent diagnosis system for classification of different machine conditions using data obtained from infrared thermography. In the first stage of this proposed system, two-dimensional discrete wavelet transform is used to decompose the thermal image. However, the data attained from this stage are ordinarily high dimensionality which leads to the reduction of performance. To surmount this problem, feature selection tool based on Mahalanobis distance and relief algorithm is employed in the second stage to select the salient features which can characterize the machine conditions for enhancing the classification accuracy. The data received from the second stage are subsequently utilized to intelligent diagnosis system in which support vector machines and linear discriminant analysis methods are used as classifiers. The results of the proposed system are able to assist in diagnosing of different machine conditions.