Intelligent Fault Diagnosis System using BEMD based Thermal Image Enhancement And Support Vector Machines

This study proposes an investigation of a novel thermal image enhancement based on bi-dimensional empirical mode decomposition (BEMD) and applies this method for rotating machinery fault diagnosis system. In this work, thermal images of machine conditions are firstly decomposed into intrinsic mode functions (IMFs) by utilizing BEMD. At each decomposition level, the IMF is expanded and fused with the residue by using gray-scale transformation and principal component analysis fusion technique, respectively. Finally, the enhanced image is rebuilt from the improved IMFs in reconstruction process. In order to diagnose the machine faults, histogram features are extracted from enhanced image. This results in high dimensionality of feature set which causes difficulties for data storage and decreases the accuracy in fault diagnosis. The high dimensionality is surmounted by employing the generalized discriminant analysis (GDA), which is one of the feature extraction methods. The features obtained from GDA are subsequently utilized for fault diagnosis system in which support vector achine is used as classifier. The results show that the proposed enhancement method is capable of improving the accuracy of classification and efficiently assisting in rotating machinery fault diagnosis