煤矿井下无轨胶轮车弯道事故风险综合预警研究
Research on comprehensive early warning of accident risk of trackless rubber-tyred vehicle on curves in underground coal mine
蔡安江 1惠伟刚1
作者信息
- 1. 西安建筑科技大学机电工程学院,陕西西安 710055
- 折叠
摘要
针对煤矿井下无轨胶轮车弯道事故多发的问题,提出了综合考虑多因素的风险预警指标体系.基于UWB定位轨迹数据及客观因素数据,以五级计分法为预警因素量化指标,建立了煤矿井下无轨胶轮车弯道事故风险分级预警机制;构建了 SSA-BP神经网络预警模型,进行了仿真学习与训练,并与BP、SVM模型进行了对比.结果表明,建立的分级预警指标机制对煤矿井下无轨胶轮车弯道安全性提升有所帮助,构建的SSA-BP神经网络模型对煤矿井下车辆弯道事故风险预警具有较高的准确性与稳定性.
Abstract
In view of the problem of frequent accidents of trackless rubber-tyred vehicles on curves in underground coal mine,a risk warning index system of underground trackless rubber-tyred vehicles curve was put forward considering multiple factors.Based on UWB positioning track data and objective factor data,and taking the five-grade scoring method as the quantitative indicator of early warning factors,the grading early warning mechanism for the accident risk of underground trackless rubber-tyred vehicle on curves in underground coal mine was established.The SSA-BP neural network early warning model was constructed,simulation learning and training were carried out,and compared with BP and SVM models.The results show that the established grading early warning index mechanism is helpful to improve the safety of underground trackless rubber-tyred vehicle on curves,and the constructed SSA-BP neural network has high accuracy and stability for the early warning of accident risk of vehicle on curves in underground coal mine.
关键词
无轨胶轮车/事故风险预警/五级计分法/SSA-BP神经网络Key words
trackless rubber-tyred vehicle/accident risk early warning/five-grade scoring method/SSA-BP neural network引用本文复制引用
基金项目
工信部物联网集成创新与融合应用项目(2018-4700)
榆林市科技计划项目(CXY-2022-172)
出版年
2024
NETL
NSTL
万方数据