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A Fire Source Localization Algorithm Based on Temperature and Smoke Sensor Data Fusion

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Abstract

Traditional video surveillance, temperature-based or smoke-based fire source location methods are difficult to timely and accurately locate the fire source in warehouses with the characteristics of burning intensely, smoke spreading quickly, and being sheltered by shelves and goods. To overcome the drawbacks, a deep-learning-based fire source localization algorithm with temperature and smoke sensor data fusion according to the different stages of the combustion process is proposed in this paper. The temperature and smoke concentration information are collected from sensors distributed in different spatial locations of a warehouse. A convolutional neural network is used to exact the fusion data feature. The deep learning algorithm is adopted to construct the fire source localization model where the fusion data feature of temperature and smoke concentrations are the inputs and the fire source coordinates are the outputs. By using Fire Dynamics Simulator, a warehouse that meets the practical application is constructed and kinds of fire scenes are simulated. The experimental results show that the RMSE of the model localization reaches 0.63, 0.08, and 0.17 in three stages respectively, which verifies the effectiveness of the proposed fire source localization algorithm.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (61873121, 52004131), the Natural Science Foundation of Jiangsu Province (BK20181376).

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Authors and Affiliations

  1. College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China

    Lijuan Li, Junjie Ye, Chenyang Wang & Chengwen Ge

  2. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, China

    Yuan Yu & Qingwu Zhang

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  1. Lijuan Li
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  4. Chengwen Ge
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Contributions

LL: Conceptualization, Methodology, Analysis, Writing—review & editing. JY: Methodology, Validation, Visualization, Analysis, Writing—original draft. CW: Investigation, Methodology, Validation, Writing—review & editing. CG: Conceptualization, Methodology, Visualization, Writing—original draft. YY: Resources, Supervision. QZ: Project administration.

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Correspondence to Yuan Yu.

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Appendix

Appendix

The difference between the temperature data collected by the nearest detector of the fire source point. From Figure 21, the smaller the grid size, the higher the temperature of the sensor collection. The overall trend of temperature rise is consistent, but when the size of the grid is reduced by 0.1 m, the CFD fire model calculation time was more than doubled. Considering the calculation accuracy and time consumption, this article selects a grid size of 0.25 m.

Figure 21
figure 21

Comparison of the temperature of the same detector at a different grid size

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Li, L., Ye, J., Wang, C. et al. A Fire Source Localization Algorithm Based on Temperature and Smoke Sensor Data Fusion. Fire Technol 59, 663–690 (2023). https://doi.org/10.1007/s10694-022-01356-6

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