Burning condition recognition of rotary kiln based on spatiotemporal features of flame video

Hua Chen, Tingting Yan and Xiaogang Zhang

Energy, 2020, vol. 211, issue C

Abstract: In the coal-fire industry, recognition of burning condition is vital for combustion control and optimization as it can provide early warning of abnormal conditions in the combustion system. We propose an effective model based on spatiotemporal features extracted from flame video for burning condition recognition especially for unsteady state condition. This model extracts features from both the spatial and temporal dimensions based on multiple adjacent frames to capture the appearance and motion information of the flame. Two new three-dimensional (3D) descriptors are developed to characterize the dynamic characteristics of the flame video stream. A computationally simple and fast dynamic texture descriptor, 3DBLBP, is designed to extract flame dynamic texture and motion from three adjacent frames of a video block, and a dynamic structure descriptor, HOPC-TOP is developed by extracting 3D phase congruency information of video clip from three orthogonal planes to capture structure and motion characterizes of flame. The two descriptors are combined to extract spatiotemporal features from flame clip for burning condition recognition. Experiment results show the proposed framework can achieve a high recognition accuracy in the real-world data, thus verifying the effectiveness of our proposed framework for the recognition of burning conditions in a rotary kiln.

Keywords: Condition recognition; Unsteady state; Spatiotemporal feature; 3DBLBP; HOPC-TOP (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:211:y:2020:i:c:s0360544220317643

DOI: 10.1016/j.energy.2020.118656

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