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Monitoring combustion systems using HMM probabilistic reasoning in dynamic flame images

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  • Chen, Junghui
  • Hsu, Tong-Yang
  • Chen, Chih-Chien
  • Cheng, Yi-Cheng

Abstract

In this paper, a novel method of on-line flame detection in video is proposed. Processing the data generated by an ordinary camera monitoring scene, it aims to early detect the current state of the combustion system and prevent the system from further degradation and occurrence of failure. Due to the dynamic change of the combustion system, the turbulent flame flicker produces images with different spatial and high temporal resolutions. The proposed method consists of hidden Markov model (HMM) and multiway principal component analysis (MPCA). MPCA is used to extract the cross-correlation among spatial relationships in the low dimensional space while HMM constructs the temporal behavior of the sequential observation. Although the prior process knowledge may not be available in the operation processes, the probability distribution of the normal status can be trained by the images collected from the normal operation processes. Subsequently, monitoring of a new observed image is achieved by a recursive Viterbi algorithm which can find the transition state sequence from series of observed image data. The proposed method, like the philosophy of traditional statistical process control, can generate simple probability monitoring charts to track the progress of the current transition state sequence and monitor the occurrence of the observable upsets. The advantages of the proposed method, data from the monitoring practice in the real combustion systems, are presented to help readers delve into the matter.

Suggested Citation

  • Chen, Junghui & Hsu, Tong-Yang & Chen, Chih-Chien & Cheng, Yi-Cheng, 2010. "Monitoring combustion systems using HMM probabilistic reasoning in dynamic flame images," Applied Energy, Elsevier, vol. 87(7), pages 2169-2179, July.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:7:p:2169-2179
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    References listed on IDEAS

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    1. Hwang, Cheol-Hong & Lee, Seungro & Kim, Jong-Hyun & Lee, Chang-Eon, 2009. "An experimental study on flame stability and pollutant emission in a cyclone jet hybrid combustor," Applied Energy, Elsevier, vol. 86(7-8), pages 1154-1161, July.
    2. Fichera, A. & Losenno, C. & Pagano, A., 2001. "Clustering of chaotic dynamics of a lean gas-turbine combustor," Applied Energy, Elsevier, vol. 69(2), pages 101-117, June.
    3. Yan, Zhuoyong & Liang, Qinfeng & Guo, Qinghua & Yu, Guangsuo & Yu, Zunhong, 2009. "Experimental investigations on temperature distributions of flame sections in a bench-scale opposed multi-burner gasifier," Applied Energy, Elsevier, vol. 86(7-8), pages 1359-1364, July.
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    Citations

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    Cited by:

    1. Chen, Hua & Yan, Tingting & Zhang, Xiaogang, 2020. "Burning condition recognition of rotary kiln based on spatiotemporal features of flame video," Energy, Elsevier, vol. 211(C).
    2. Zhou, Dongdong & Cheng, Shusen, 2019. "Measurement study of the PCI process on the temperature distribution in raceway zone of blast furnace by using digital imaging techniques," Energy, Elsevier, vol. 174(C), pages 814-822.
    3. Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
    4. González-Cencerrado, A. & Peña, B. & Gil, A., 2012. "Coal flame characterization by means of digital image processing in a semi-industrial scale PF swirl burner," Applied Energy, Elsevier, vol. 94(C), pages 375-384.
    5. Tian Qiu & Minjian Liu & Guiping Zhou & Li Wang & Kai Gao, 2019. "An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model," Energies, MDPI, vol. 12(13), pages 1-17, July.
    6. Chen, Junghui & Chang, Yu-Hsiang & Cheng, Yi-Cheng & Hsu, Chen-Kai, 2012. "Design of image-based control loops for industrial combustion processes," Applied Energy, Elsevier, vol. 94(C), pages 13-21.
    7. Koziel, Sylvie & Hilber, Patrik & Westerlund, Per & Shayesteh, Ebrahim, 2021. "Investments in data quality: Evaluating impacts of faulty data on asset management in power systems," Applied Energy, Elsevier, vol. 281(C).

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