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Combustion Condition Recognition of Coal-Fired Kiln Based on Chaotic Characteristics Analysis of Flame Video
Jiang, Yu1; Chen, Hua2; Zhang, Xiaogang1; Zhou, Yicong3; Wang, Lianhong1
2022-06
Source PublicationIEEE Transactions on Industrial Informatics
ISSN1551-3203
Volume18Issue:6Pages:3843-3852
Abstract

Keeping combustion stable and detecting unstable states in time is crucial for coal-fired furnaces such as rotary kilns, boilers, and oxygen furnaces. Because of the interference and complex conditions in the industrial field, recognition of combustion conditions by vision analysis is difficult. In this article, we propose a robust nonlinear dynamic system analysis-based approach for combustion condition recognition by extracting chaotic characteristics from a flame video. We first discover chaotic characteristics in the intensity sequence extracted from a flame video of coal-fired kilns, and then we further find that the underlying chaos rules differ between combustion conditions. Based on this finding, we design a set of trajectory evolution features and morphology distribution features of chaotic attractors for combustion condition recognition. After reconstructing the chaotic attractors from the intensity sequence of a flame video by phase space reconstruction, the quantified features are extracted from the recurrence plot and morphology distribution and put into a decision tree to recognize the combustion condition. The experimental results on real-world data show that the proposed method can recognize the combustion condition in coal-fired kilns effectively and promptly. Compared with other methods, the recognition accuracy is improved more than 5%.

KeywordChaotic Characteristics Combustion Condition Flame Video Morphology Distribution Features (Mdfs) Trajectory Evolution Features (Tefs)
DOI10.1109/TII.2021.3118135
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems;computer Science, Interdisciplinary Applications;engineering, Industrial
WOS IDWOS:000761218600028
Scopus ID2-s2.0-85119596691
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorZhang, Xiaogang
Affiliation1.College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China
2.College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
3.Department of Computer and Information Science, University of Macau, Macau, 999078, Macao
Recommended Citation
GB/T 7714
Jiang, Yu,Chen, Hua,Zhang, Xiaogang,et al. Combustion Condition Recognition of Coal-Fired Kiln Based on Chaotic Characteristics Analysis of Flame Video[J]. IEEE Transactions on Industrial Informatics,2022,18(6):3843-3852.
APA Jiang, Yu,Chen, Hua,Zhang, Xiaogang,Zhou, Yicong,&Wang, Lianhong.(2022).Combustion Condition Recognition of Coal-Fired Kiln Based on Chaotic Characteristics Analysis of Flame Video.IEEE Transactions on Industrial Informatics,18(6),3843-3852.
MLA Jiang, Yu,et al."Combustion Condition Recognition of Coal-Fired Kiln Based on Chaotic Characteristics Analysis of Flame Video".IEEE Transactions on Industrial Informatics 18.6(2022):3843-3852.
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