Abstract
This paper presents a system to monitor the wear process in machines using computer vision and image processing techniques applied to wear particle analysis. Particles are classified using their visual attributes to predict wear failure modes in engines and other machinery. The aim of the current work is to develop an automated system to classify wear particles and thereby predict wear failure modes in engines and other machinery, such that it obviates the need for specialists and reliance on human visual inspection techniques. The paper describes an interactive control system CAVE (Computer Aided Vision Engineering) in terms of the stages involved in processing data to acquire morphological features of wear particles from microscopic images and their automatic classification.