Robust image segmentation for feature extraction from internal combustion engine in-cylinder images

Abstract

In-cylinder imaging diagnostics for internal combustion engines provide rich information on the structure and evolution of reaction zone features, which affect both engine out emissions and efficiency. However, the most common analysis of in-cylinder combustion luminosity imaging considers ensemble averaged images, which are not suitable for characterizing processes that vary significantly between cycles, such as ignition and soot formation and oxidation. Here, a robust image segmentation algorithm is presented for feature extraction from single-cycle in-cylinder combustion images and is used with a ‘combination of interpretations’ (COI) approach to analyze OH*-chemiluminescence imaging of premixed and non-premixed natural gas combustion modes in an optically-accessible reciprocating engine. Dynamic thresholding and region size filtering are combined with watershed segmentation to create a parameterized adaptive watershed (PAW) segmentation algorithm. The fusion of these segmentation methods is novel to combustion imaging and is demonstrated to provide quantified improvement relative to the current state of the art segmentation methods; PAW segmentation provides increased sensitivity for early ignition processes, and more robustly identifies the reaction zones at later stages of combustion. The PAW algorithm requires no adjustment between the two considered combustion modes or for any stage of the combustion process. The reliability of the PAW output enables feature extraction of individual reaction zone location and area from the combustion images using a polar-sector coordinate system for COI analysis. This approach characterizes the cyclic variability of individual fuel jets, identifies coupling of auto-ignition behavior between adjacent reaction zones, and demonstrates systematic errors arising from measurement of auto-ignition in ensemble averaged images. Application of PAW segmentation and the analysis approach presented here can provide more complete characterization of other spatially-resolved internal combustion diagnostics, particularly where there is high process variability, overlapping image regions, or wide signal intensity ranges.

An improved subpixel-level registration method for image-based fault diagnosis of train bodies using SURF features

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Deep learning based techniques for flame identification in optical engines

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