Major disruptions in tokamak pose a serious threat to the vessel and its
surrounding pieces of equipment. The ability of the systems to detect any
behavior that can lead to disruption can help in alerting the system beforehand
and prevent its harmful effects. Many machine learning techniques have already
been in use at large tokamaks like JET and ASDEX, but are not suitable for
ADITYA, which is comparatively small. Through this work, we discuss a new
real-time approach to predict the time of disruption in ADITYA tokamak and
validate the results on an experimental dataset. The system uses selected
diagnostics from the tokamak and after some pre-processing steps, sends them to
a time-sequence Long Short-Term Memory (LSTM) network. The model can make the
predictions 12 ms in advance at less computation cost that is quick enough to
be deployed in real-time applications.Comment: 7 pages, 4 figure