Morphology and autowave metric on CNN applied to bubble-debris classification

In this study, me present the initial results of cellular neural network (C NN)-based autowave metric to high-speed pattern recognition of gray-scale i mages. the application is to a problem involving separation of metallic wea r deris particles from air bubbles. This problem arises in an optical-based system for determination of mechanical wear. This paper focuses on disting uishing debris particles suspended in the oil flow from air bubbles and aim s to employ CNN technology to create an online fault monitoring system, For the class of engines of interest bubbles occur much more often than debris particles and the goal is to develop a classification system with an extre mely low false alarm rate for missclassififed bubbles, The designed analogi c CNN algorithm detects and classifies single ubbles and bubble groups usin g inary morphology and autowave metric, The debris particles are separated based on autowave distances computed between bubble models and the unknown objects, Initial experiments indicate that the proposed algorithm is robust and noise tolerant and when implemented on a CNN universal chip it provide s a solution in real time.