Fire is a great danger to the society and economy as well as human life safety, and a small spark can lead to a serious fire. In order to improve the reliability of natural gas station fires warning system, this paper employ YOLO v5 target detection algorithm to investigate the detection of station flames. Firstly, 5488 official datasets and flame images searched for the Internet are collected as training sets and labeled. Secondly, the labeled images are used to train the YOLOv5 network model under Linux operating platform to get the appropriate weighting coefficients to minimize the value of the model loss function. Finally, the trained model is used to identify and detect the flames in the actual natural gas station site environment. The experimental results found that the YOLOv5 algorithm model can achieve real-time supervision of flames, with recognition average precision up to 89.2, fast flame detection and high recognition sensitivity. This work helps to facilitate the realization of real-time monitoring of flames and other hazardous factors of natural gas stations and improve station safety.
An accident analysis method based on Fault Tree and Bayesian network is proposed to analysis the accident cause for the explosion of Qinglong natural gas pipeline. The method maps the fault tree to the Bayesian network and combines the causal modelling ability of Fault Tree analysis with the probabilistic dynamic updating ability of Bayesian network. The results demonstrate that the most influential factor of the explosion event is the "natural gas leakage", which is much higher than other factors such as "fire source" in the site of accident. Four key failure causation chains were identified by probabilistic updating. The identified key risk factors and failure causation chains can provide guidance for on-site hazard source identification and risk prediction.
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