Grain size determination and classification using adaptive image segmentation with shape-context information for indirect mill faults detection

In this paper, the authors described methods of material granularity evaluation and a novel method of grain size determination with inline mill device diagnostics. The mill diagnostic can be carried out with vibration measurements, machine vision or infrared imaging. Milling process is an extremely energy- and cost-consuming process, thus diagnostics process should be performed with high efficiency. Method proposed in this paper is based on the online examination of the final product during the milling process using real-time digital images. Determination of the total number of the grain, size of each grain, also classification of the grains is main goal of proposed method. In the proposed method, the visible camera with lightning mounted at two assumed angles has been used, what increases the grain detection process. Proposed method uses an adaptive segmentation to match correctly the grains, the information about particles shape and context is used to optimize the grain classification process in the next step. Finally, during image processing, the simple rule-based method is used to obtain information about overall quality of the product and possible faults in the milling process based on the evaluated parameters.