Assessment of flame stability through a convolutional denoising autoencoder and statistical analysis
Flame stability assessment is essential for optimizing combustion operation and improving combustion quality. However, an accurate and reliable assessment of stability is difficult, heavily relying on prior expert knowledge and massive labeled data. This study proposes a novel method for flame stabi...
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| Published in: | Combustion and flame Vol. 258; p. 113069 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.12.2023
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| Subjects: | |
| ISSN: | 0010-2180 |
| Online Access: | Get full text |
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| Abstract | Flame stability assessment is essential for optimizing combustion operation and improving combustion quality. However, an accurate and reliable assessment of stability is difficult, heavily relying on prior expert knowledge and massive labeled data. This study proposes a novel method for flame stability assessment through flame images and deep learning techniques. In this method, the deep image features are extracted by an unsupervised convolutional denoising autoencoder (CDAE), and then quantitatively analyzed by a stability index. In particular, the CDAE introduces a new loss function composed of denoising coding constraints and reconstruction similarity to improve its training efficiency. The stability index is established based on clustering analysis and statistical analysis of the deep image features, with a numerical interval of [0, 1]. The effectiveness of the proposed method is verified by the flame images obtained from ethylene-air diffusion combustion conditions. Results show that the proposed method extracts representative flame features accurately and quantifies the flame stability with strong robustness and generalization ability. |
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| AbstractList | Flame stability assessment is essential for optimizing combustion operation and improving combustion quality. However, an accurate and reliable assessment of stability is difficult, heavily relying on prior expert knowledge and massive labeled data. This study proposes a novel method for flame stability assessment through flame images and deep learning techniques. In this method, the deep image features are extracted by an unsupervised convolutional denoising autoencoder (CDAE), and then quantitatively analyzed by a stability index. In particular, the CDAE introduces a new loss function composed of denoising coding constraints and reconstruction similarity to improve its training efficiency. The stability index is established based on clustering analysis and statistical analysis of the deep image features, with a numerical interval of [0, 1]. The effectiveness of the proposed method is verified by the flame images obtained from ethylene-air diffusion combustion conditions. Results show that the proposed method extracts representative flame features accurately and quantifies the flame stability with strong robustness and generalization ability. |
| ArticleNumber | 113069 |
| Author | Tang, Xiaoyu Hossain, Md. Moinul Xu, Chuanlong Han, Zhezhe |
| Author_xml | – sequence: 1 givenname: Zhezhe surname: Han fullname: Han, Zhezhe organization: School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, PR China – sequence: 2 givenname: Xiaoyu surname: Tang fullname: Tang, Xiaoyu organization: School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, PR China – sequence: 3 givenname: Md. Moinul orcidid: 0000-0003-4184-2397 surname: Hossain fullname: Hossain, Md. Moinul organization: School of Engineering, University of Kent, Canterbury, Kent, CT2 7NT, UK – sequence: 4 givenname: Chuanlong surname: Xu fullname: Xu, Chuanlong email: chuanlongxu@seu.edu.cn organization: School of Energy and Environment, Southeast University, Nanjing, 210096, PR China |
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| CitedBy_id | crossref_primary_10_1016_j_engappai_2025_110574 crossref_primary_10_1016_j_combustflame_2023_113215 crossref_primary_10_1016_j_fuel_2025_135518 crossref_primary_10_1016_j_ast_2025_110243 crossref_primary_10_1016_j_proci_2024_105730 crossref_primary_10_23919_CHAIN_2025_000009 |
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| Keywords | Flame image Statistical analysis Flame stability Convolutional denoising autoencoder Stability index |
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| SubjectTerms | Convolutional denoising autoencoder Flame image Flame stability Stability index Statistical analysis |
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