  Lean combustion is environment friendly with low NOx emissions and also
provides better fuel efficiency in a combustion system. However, approaching
towards lean combustion can make engines more susceptible to lean blowout. Lean
blowout (LBO) is an undesirable phenomenon that can cause sudden flame
extinction leading to sudden loss of power. During the design stage, it is
quite challenging for the scientists to accurately determine the optimal
operating limits to avoid sudden LBO occurrence. Therefore, it is crucial to
develop accurate and computationally tractable frameworks for online LBO
detection in low NOx emission engines. To the best of our knowledge, for the
first time, we propose a deep learning approach to detect lean blowout in
combustion systems. In this work, we utilize a laboratory-scale combustor to
collect data for different protocols. We start far from LBO for each protocol
and gradually move towards the LBO regime, capturing a quasi-static time series
dataset at each condition. Using one of the protocols in our dataset as the
reference protocol and with conditions annotated by domain experts, we find a
transition state metric for our trained deep learning model to detect LBO in
the other test protocols. We find that our proposed approach is more accurate
and computationally faster than other baseline models to detect the transitions
to LBO. Therefore, we recommend this method for real-time performance
monitoring in lean combustion engines.
