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- W4386814433 abstract "The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method." @default.
- W4386814433 created "2023-09-18" @default.
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- W4386814433 date "2023-09-17" @default.
- W4386814433 modified "2023-09-26" @default.
- W4386814433 title "A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method" @default.
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- W4386814433 doi "https://doi.org/10.3390/app131810384" @default.
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