Evaluation of thermography image data for machine fault diagnosis

Abstract

A novel approach for fault diagnosis of rotating machine based on thermal image investigation using image histogram features is proposed in this paper. Herein, the machine learning and statistical approach are adopted along with thermal image signal to machine condition diagnosis. Using thermal images, the information of machine condition can be investigated more simply than other conventional methods of machine condition monitoring. In this work, the behaviour of thermal image is investigated with different conditions of machine. A test-rig that represents the machine in industry was set up to produce thermal image data in the experiment. Some significant features have been extracted and selected by means of principal component analysis, and independent component analysis, and other irrelevant features have been discarded. The aim of this study is to retrieve thermal images by means of selecting a proper feature to recognise the fault pattern of the machine. The result shows that the classification process of thermal image features by support vector machine and other classifiers can serve machine fault diagnosis.