# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
###### Abstract  
Cable overload is one of the most critical contributors to early cable fires. This study proposes a hybrid Bayesian network (BN)-based fire risk analysis model, to investigate the evolution of overload-induced early cable fire risks. In particular, the fire risk transmission paths caused by cable overload are reported, considering the critical factors that likely lead to fires. A BN with a specific structure was considered using the fire risk transmission paths. Later, given the risk index system, a hybrid fire risk assessment model caused by cable overload was developed based on the entropy weight method. Subsequently, the corresponding risk levels were evaluated based on the evolution of the fire risk, using numerical simulations. Finally, a case study was conducted to validate the proposed methods, and the results indicated that the proposed methods can effectively evaluate the state of the cable and explain the causes of fire risk, which can be used for early fire warnings.  
Bayesian network; cable overload; fire; risk transmission; risk assessment Article  
BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires  
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quently, this study focuses on analyzing the risk of overload-induced early fires from the perspective of cable fire risk transmission paths based on a Bayesian network (BN).  
To estimate the risk level of overload-induced early cable fires, this study proposes a novel BN-based hybrid fire risk assessment model. First, the evolution of the cable overload fire risk is studied; then, a BN with a specific structure is proposed for presenting the evolution process. Subsequently, the prior probabilities of basic factors are explored, and the conditional probabilities of network nodes are determined based on cable-temperature-rise experiments. In addition, given the risk index system and combined with the entropy weighting method, the risk assessment model of overload-induced early cable fires is established, and the risk level is estimated based on the numerical simulations of risk evolution. The results show that the proposed model can evaluate the overload fire risk states of cables and infer the causes of the risk states. To address the aforementioned concerns and research gaps, this study focuses on the overload-induced early cable fire risk analysis, based on the proposed BN and considering the effects of mechanical damage, cable aging, and other factors.  
The remainder of this paper is organized as follows. Section 2 reviews and discusses previous studies and the main concerns of this study, where the existing research gaps are identified. Section 3 constructs the cable overload-induced early cable fire BN, based on the risk transmission of early cable fires. To formulate the cable fire risk using the BN, a cable overload coupling experiment was conducted for investigating the causes of the cable temperature in the case of the cable overload. To assess the early cable fire risk, a risk index system combined with the entropy weighting method is developed in Section 4. Furthermore, to validate the effectiveness of the proposed method, a case study and its computational results are presented in Section 5. Finally, Section 6 concludes the paper by providing valuable insights and by highlighting future directions.