Intelligent fault diagnosis of rotating machinery using infrared thermal image

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.