Application Research Of Ant Colony Algorithm In Ferrography Image Processing

Wear is one of the main reasons caused mechanical equipment failure. Taking wear conditionmonitoring and fault diagnosis of mechanical equipment is significant, because it can achieve thepreventive maintenance of equipment, reduce the cose of downtime, ensure the safety of personneland equipment and avoid the occurrence of sudden failure and disaster. Ferrography is a wear particleanalysis and machine condition monitoring technology which appeared in the1970s. Currently, it hasbecome one of the most economic and effective methods of wear detection. The processing andanalysis of ferrograph image is the key of modern ferrograph technology. Achieving the accuratesegmentation of wear debris has an important impact on the subsequent particle analysis andrecognition, even the wear condition detection and fault diagnosis of a mechanical system.In this paper, the researches of wear debris segmentation and edge detection have been studied,as follows:(1) Aims at the accurate segmentation of wear debris chains and the abnormal large wear debriswhich are difficult to segment in ferrograph image, this paper presented a ferrograph imagesegmentation method combining watershed and ant colony algorithm. Firstly, the gradient image iscomputed by the presented colorful gradient operator, and then it is segmented by the labelingwatershed algorithm, this step can achieve the pre-segmentation of wear debris. Secondly, thedynamic searching radius and regional color difference have been introduced to the ant colonyclustering algorithm, the similar regions are merged by the improved ant colony clustering criteria,and in this step the segmentation of abnormal large wear debris is obtained. However, because thereare so many small homogeneous or adherent wear particles that some of them may be mistakenlymerged together. In order to achieve the accurate segmentation of the different type of wear particles,the discrimination and correction of clustering results should be taken by using the shape parameterslike aspect ratio after the ant colony clustering. Finally, the proposed algorithm is compared with Otsuthreshold method, K-means clustering and Fuzzy C-means clustering, the experiments show that thismethod is an effective way in the segmentation of wear debris chain and abnormal large wear debris.(2) In this paper, the ant colony algorithm is adopted in wear particle edge detection. Firstly, thetarget pixels which contain the edge information of wear particles can be extracted by thresholdmethod, and then the ants are distributed in these pixels. Secondly, the ants move in the guide of pixelgradient values and color difference to achieve the edge detection of wear debris. Finally, this paper discusses the influence of different parameters to the detection results.In this paper, Visual C++6.0is used as a software platform and OpenCV library used to fulfilledthe algorithms. The experimental results demonstrate that the methods of image segmentation andedge detection presented in this paper are feasible and effective.