Characterization of wear-debris group in on-line visual ferrographic images

Wear-debris characterization using ferrography, which is of importance in machine condition monitoring and fault diagnosis, remains a challenge. The newly developed on-line visual ferrograph can provide digital wear-debris images and perform on-line analysis during wear monitoring. In this article, image projection transformation was utilized for extracting the overall characteristics of the wear-debris chains according to the fundamental feature of wear-debris arraying along the horizontal direction. Moreover, Full Binary Tree Based Image Division was also proposed to analyse the regional features on different scales in on-line visual ferrographic images. Several descriptive parameters including thinning ratio, chain length and chain width were proposed. In the experiments, four types of images with different wear-debris groups were compared and a group of time-sequence on-line visual ferrographic images of an inline four-cylinder gasoline engine was studied. It is found that more comprehensive wear information can be acquired through the proposed multi-parameter description method. Meanwhile, the image projection transformation method extracts macrocharacteristics of images rapidly and efficiently and the thinning ratio measures wear-debris width in an intuitive way.