Application of evolutionary algorithm to limitation of a set of statistical features of thermovision images

Abstract

Thermovision is more and more often used in machinery and apparatus diagnostics. With the aid of a thermographic camera non-contact simultaneous temperature measurements can be carried out at many points of an object and they can be recorded in a form of a thermographic image. The thermographic image can be a source of diagnostic information. Extraction of this information requires the necessity of application of different methods of the analysis of thermographic images. From thermographic image a huge amount of features can be extracted which causes problems with efficient assessment of technical state due to informational noise. There are methods which allow to search and find relevant features that are useful for diagnostic processes. In the paper application of evolutionary algorithm for selection of optimal diagnostic features has been shown. In case of assessment of selected features neural classifier has been used. A set of 259 features for each image has been considered. After searching process two features have been selected and the obtained classification results have been of very good quality. Efficiency of classifier has been in some cases 100% and not less than 97%. The results have shown that the evolutionary algorithm can be applied to selection of relevant diagnostic features.