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.

翻译：在设计阶段,科学家们很难准确地确定最佳操作限度,以避免突然发生低氧化氮排放,并且可以在燃烧系统中提供更好的燃料效率。然而,接近精度燃烧可以使发动机更容易受到倾斜井喷的影响。 精度井喷是一种不可取的现象,可能导致突然火焰消散,导致电力突然丧失。在设计阶段,科学家们很难准确地确定最佳操作限度,以避免突然发生低氧化氮排放,因此,开发精确和可计算可移植的框架,用于在低氧化氮排放引擎中进行在线LBO检测。根据我们的知识,我们第一次建议采用深层学习的方法来探测燃烧系统中的精度井喷井喷。在这项工作中,我们利用实验室规模的梳子收集不同协议的数据。我们从每个协议开始就远离LBO,逐渐转向LBO系统,在每个条件下捕捉到一个准时间序列数据集。因此,我们必须利用我们数据集中的一个协议作为参考协议和由域专家附加说明的条件,为我们经过训练的深层学习模型找到一个过渡状态指标,以便在其他测试协议中探测低氧化物。我们发现一个实验室规模的梳收集数据。我们所建议采用的方法比其他测试方法更精确,因此更接近于实际的计算方法。