First Application of Cellular Neural Network Methods to the real time identification of Hot Spots in JET

In order to qualify materials and develop integrated scenarios for ITER, Joint European Torus (JET) is going to operate with a new wall consisting of beryllium in the main chamber and tungsten in the divertor. These new materials will require particular care when the machine operates, considering that they are much more vulnerable than the present combination of graphite and stainless steel. Early detection of hot spots, which are regions of the first wall where the temperature approaches dangerous levels, is considered one essential element in the safety strategy. In this paper, cellular nonlinear networks (CNNs) are applied to the task of detecting hot spots in the infrared images of JET wide-angle camera. The ability of the CNNs to process the pixels of the images in parallel makes this technology a very good candidate for this task. Various algorithms are presented, which can locate the hot regions in any part of the image with a temporal resolution on the order of 60 ms, which is considered adequate for safety purposes at JET. In addition to the frame-by-frame static identification of the hot spots, their evolution in time is also followed to determine if they approach dangerous parts of the vacuum vessel. The potential of the CNNs would therefore allow for the implementation of alternative protection strategies, such as following the increase and displacement of the hot spots inside the entire vacuum vessel and identifying particles dropping into the plasma.