We present the initial results of cellular neural network (CNN)-based autowave metric to high-speed pattern recognition of gray-scale images. The approach is applied to a problem involving separation of metallic wear debris particles from air bubbles. This problem arises in an optical-based system for determination of mechanical wear. This paper focuses on distinguishing debris particles suspended in the oil flow from air bubbles and using the CNN technology to create an online fault monitoring system. The goal is to develop a classification system with an extremely low false alarm rate for misclassified bubbles. The CNN algorithm detects and classifies single bubbles and bubble groups using binary 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 provides a solution in real time.
['István Szatmári', 'Abraham Schultz', 'Csaba Rekeczky', 'T. Kozek', 'Tamás Roska', 'Leon O. Chua']
Morphology and autowave metric on CNN applied to bubble-debris classification