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矿用电动无轨胶轮车运行风险监测云模型

Cloud model for risk monitoring of mining electric traction rubber-wheel vehicles

  • 摘要: 随着电动化时代的到来,电动无轨胶轮车有取代传统胶轮车的趋势,然而限制其广泛应用的重要原因之一为风险管控问题。为监测电动无轨胶轮车在矿山环境下的风险状态,结合相关标准、文献研究结果和现有监测手段,明晰了风险演化路径,选取可监测指标并确定低、中、高三级风险评价论域,建立了风险评级指标体系。基于路径触发概率和事件后果严重程度对指标进行动态赋权,构建了加权综合云模型。对不同场景模拟分析的结果表明:该模型能够直观且合理地反映整车系统风险等级,比较不同场景的风险大小,矿山环境相较日常恶劣环境危险性更高。指标敏感性分析表明:瓦斯浓度、电池温度、环境温度对系统风险影响较大。电池温度与瓦斯浓度共同作用时,对系统风险状态的影响更加显著。未来,随着运行监测数据的规范和增加,将逐步纳入更多风险因素,完善指标体系,提升模型适用性。

     

    Abstract: With the emergence of the electric era, electric traction rubber-wheel vehicles are increasingly replacing traditional rubber wheel vehicles. However, one significant obstacle hindering their widespread adoption is the issue of risk control. In order to effectively monitor the risk status of electric traction rubber-wheel vehicles in mining environments, a comprehensive approach was developed by integrating relevant standards, literature research findings, and existing monitoring methods. This approach clarifies the path of risk evolution, selects appropriate monitoring indicators, determines low, medium and high-risk evaluation domains, and establishes a rating index system for assessing risks. By considering both the probability of triggering events along with their potential severity consequences, a weighted comprehensive cloud model has been constructed. Simulation results from various scenarios demonstrate that this model can intuitively and reasonably reflect the level of risk within vehicle systems while also allowing for comparisons between different scenarios. It is important to note that mine environments pose greater dangers compared to daily harsh environments. Sensitivity analysis on key indices reveals that gas concentration levels, battery temperature fluctuations and ambient temperature have a significant impact on overall system risk. Furthermore, when battery temperature interacts with gas concentration levels simultaneously it leads to an even more pronounced influence on system risk states. In the future, as operation monitoring data becomes standardized and increases in volume over time, additional risk factors will gradually be incorporated into the index system resulting in improvements to its applicability while optimizing performance.

     

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