输送带
领结
模糊逻辑
贝叶斯网络
动态贝叶斯网络
火力发电站
计算机科学
工程类
环境科学
机械工程
人工智能
电气工程
天线(收音机)
作者
Xin Liu,Zhou Chongbo,Wen Jiang,Huiguang He,Hao Liu
出处
期刊:Journal of physics
[IOP Publishing]
日期:2024-11-01
卷期号:2903 (1): 012017-012017
标识
DOI:10.1088/1742-6596/2903/1/012017
摘要
Abstract In thermal power plants, coal conveyor belts pose significant risks that jeopardize the stability of the energy supply, underscoring the need for effective risk management. To address the complexity, uncertainty, and polymorphism issues in belt conveyor systems, we introduce a BT-UFDBN risk analysis method specifically for coal conveyor belts. This method develops a typical Bow-tie model, identifies potential risk factors for unplanned stoppages, and utilizes fuzzy evaluation methods and an improved SAM method to determine prior probabilities. The Bow-tie (BT) model is then mapped into a Dynamic Bayesian Network (DBN). To manage uncertainties within the DBN, the Leaky Noisy-OR gate model, stationarity, and first-order Markov assumptions are employed to ensure the model’s validity and practical relevance. This paper uses a belt conveyor system from a thermal power plant as a case study to validate the model’s effectiveness in predicting accident consequences, diagnosing fault causes, and proposing targeted preventive measures for identified weak points. The study provides theoretical guidance for risk management of coal conveyor belts in power plants.
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