Early detection of lean blowout in a combustor using symbolic analysis of colour images

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Recently, lean combustion has been accepted as one of the preferable modes to run both small and large scale energy sectors as it reduces the level of NOx (oxides of nitrogen) emissions. However, ultra-lean combustion is more susceptible to lean blowout (LBO) which significantly reduces engine life and operating reliability. Therefore, early detection of LBO is important so that operator can get sufficient lead time to initiate precautionary actions. The current investigation proposes a novel technique based on the conversion of flame colour matrix into symbolic strings. The generation of symbolic strings is done based on the partitioning of the hue space obtained from transformation of RGB (Red-Green-Blue) to HSV (Hue-Saturation-Value) using uniform partitioning method. The partitions are determined at the reference state and kept fixed for other equivalence ratios (anomalous or non-reference states). Thus, the hue value at each pixel of the image is assigned a symbol depending on its belonging to a particular partition. Thereafter, an array of symbolic state probability vector at reference combustion state is determined based on the probability of pixel hue value being in a particular symbolic state or symbol (number of symbolic states is considered as 8). Also, for other anomalous or non-reference combustion states, symbolic state probability vectors are estimated using the partition formed at the reference state. The difference in symbolic state probability vectors between reference and anomalous combustion states gives the anomaly measure, which is used here to detect the flame blowout. The early increasing trend of anomaly towards the blowout for both premixed and partially premixed flames indicate that the measure can be helpful to detect the approach towards LBO. The estimation of anomaly is shown for using the flame images of different quantities. The random selection of five images from a flame video shows to be very effective in capturing the trend of anomaly which also ensures low computational effort. Thus, the tool based on symbolic colour image analysis (SCIA) may also be helpful in the mitigation of LBO.